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Multi-atlas based segmentation of brain images: Atlas selection and its effect
on accuracy
P. Aljabara,⁎, R.A. Heckemannb, A. Hammersc, J.V. Hajnalb, D. Rueckerta
aVisual Information Processing Group, Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UK
bImaging Sciences Department, MRC Clinical Sciences Centre, Imperial College London, UK
cDivision of Neuroscience and Mental Health, MRC Clinical Sciences Centre, Imperial College London, UK
a b s t r a c ta r t i c l ei n f o
Article history:
Received 13 November 2008
Revised 22 January 2009
Accepted 7 February 2009
Available online 23 February 2009
Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images.
Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by
propagating manual delineations from multiple atlases in a database to a query subject and combining them.
The atlas databases which can be used for these purposes are growing steadily. We present a framework to
address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom
subset of atlases for each query subject provides more accurate subcortical segmentations than those given
by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-
based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study.
Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the
top of the ranked list. The resulting segmentations were compared with manual reference segmentations
using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice
overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a
similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain
image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is
a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of
meta-information for selection.
© 2009 Elsevier Inc. All rights reserved.
Magnetic resonance (MR) imaging of the brain has established
itself as an essential diagnostic method in neurology research and
clinical practice. Quantitative studies often rely on the capability to
label orsegment regions of the brain thathavedistinctivestructural or
functional properties. This enables comparisons within and between
subjects for determining how such regions are affected by physiolo-
gical and pathological processes as well as therapeutic measures. Such
studies benefit from increasing numbers of MR images becoming
publicly available for use in research. This availability has made the
creation and maintenance of MR image databases incorporating
structural segmentations (manual or otherwise) more feasible. Good
examples are the Internet Brain Segmentation Repository1and the
LONI Probabilistic Brain Atlas (Shattuck et al., 2008). An obvious
application of this work is the use of expert annotations in the form of
prior information to assist in providing automatic segmentations of
query or unseen images.
An atlas, in the context of this work, is defined as the pairing of a
image registration. The atlas MR image can be registered to the query
be transformed and treated as a segmentation estimate for the query
subject. Within this process, commonly called atlas-based segmenta-
tion, the atlas that is propagated can represent a single segmented
The propagation of the atlas might also form a step within a larger
framework. For example, probabilistic or 'soft' atlases may be propa-
gated and treated as priors in a Bayesian framework within a further
segmentation step (Murgasova et al., 2006).
Sources of error in atlas-based segmentations include registration
differences) or existence of labelling errors in the atlas segmentation,
something that cannot be overcome by accurate registration.
If a database of atlases is available, multiple segmentations from a
group of atlases can be propagated to the query. After propagation,
they can be treated as separate classifiers and fused to form a single
consensus segmentation estimate. The main benefit of the multi-atlas
NeuroImage 46 (2009) 726–738
⁎ Corresponding author.
E-mail address: firstname.lastname@example.com (P. Aljabar).
1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
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segmentation approach is that the effect of errors associated with any
single atlas propagation can be reduced in the process of combination.
Multi-atlas segmentation has been shown to be effective in compar-
isonwith other atlas-based approaches (Rohlfinget al., 2004a) and for
the task of segmenting structures in the human brain (Heckemann
et al., 2006a; Klein and Hirsch, 2005). Relevant work has also been
carried out on the methods used for combining classifiers within a
multi-atlas segmentation framework (Warfield et al., 2004; Rohlfing
et al., 2004b).
Multi-atlas segmentation faces various issues, however, if the
numberof atlasesinthedatabasebecomeslarge.Ona practicallevel,if
every atlas is registered with the query image, the computational cost
of segmentation increases linearly with the size of the database. More
importantly, it is possible that the population represented by the
atlases is heterogeneous, for example in terms of age, morphology or
pathology. In this case, for a given query, certain subjects in the
database may be more appropriate as candidate segmentations than
others. Propagating and combining only these subjects' atlases is
likely to produce a better segmentation estimate than one that draws
on the full atlas database.
These considerations provide a motivation for the selection of
atlases that are appropriate for a given query image, and this work
presents an investigation of a practical strategy for such a selection
approach within the context of multi-atlas segmentation. Rohlfing
et al. (2004a) and Wu et al. (2007) investigated the optimal
selection of a single template during atlas-based segmentation. Our
work contrasts with this in that we select multiple atlases for
subsequent propagation and fusion. We present the results of a series
of experiments to assess the performance of atlas selection using a
database consisting of 275 MR images and accompanying manual
subcortical segmentations. Automated segmentation is carried out
based on ranking and selecting atlases from a database according to
criteria that are expected to predict their suitability for segmenting a
given target. To test this, the accuracy of the resulting segmentations
is measured using leave-one-out cross-validation and compared with
the accuracy of segmentations derived from combining random sets
of atlases. We also investigate different criteria for ranking the atlases
and the effect of selecting and combining increasing numbers of
atlases from a ranked set.
Over the mainly subcortical structures studied, a mean Dice
overlap of 0.854 was obtained using selection. This compares with a
reference value of 0.811 obtained by fusing random sets of atlases. For
individual structures, selection provides typical Dice accuracy gains of
0.02 to 0.05 over random sets with the biggest improvement of 0.12
being shown by segmentations of the caudate nucleus.
In this paper, methods for multi-atlas segmentation and selection
are initially described. This is followed by descriptions of the
experiments to assess the effectiveness of atlas selection and their
results which are discussed in the final section. Part of the research
presented in this study appeared previously in a conference paper
(Aljabar et al., 2007).
We describe multi-atlas segmentation along with the motivation
and possible strategies for atlas selection. These strategies can be
based on image information within the atlases or on subject-specific
Background: multi-atlas segmentation
Atlases within a database can be registered to a query image, and
their segmentations can be transformed and subsequently fused or
combined to provide a consensus segmentation estimate for the
query. Sometimes described as classifier fusion or label fusion, this
method is illustrated schematically in Fig.1. This multi-atlas approach
tosegmentation reduces the effectof errors associated with individual
propagated atlases. For example, a registration error for a particular
propagated atlas is less likely to affect the final segmentation when
combined with other atlases. The proportion of errors incurred during
propagation that are independent are those that are averaged out
when multiple atlases are combined (Heckemann et al., 2006a). As
well as a gain in accuracy, Heckemann et al. (2006a) also demonstrate
that precision improves as more atlases are combined.
The fusion of the propagated segmentations (or classifiers) takes
place at the voxel level and can be achieved in different ways. In what
is probably the simplest approach, the atlas segmentations are
transformed using nearest-neighbour interpolation so that they each
provide a discrete or 'hard' labelling for each voxel. The final label
assigned to a voxel can then be decided by 'majority vote'.
More sophisticated methods for the combination or fusion of the
segmentations are also available. For example it is possible to use a
linear interpolator when transforming individual labels in order to
obtain a probabilistic or 'soft' estimate for the label from each
segmentation. This can be used to generate an array of values (pij) for
a given voxel where pijrepresents the confidence level or probability
of the voxel being assigned label i by the jth segmentation. A number
of different rules can be used to generate a consensus estimate based
on such data, and a good overview of these can be found in Kittler
et al. (1998).
A notable example of producing consensus segmentations in the
context of medical image processing is the STAPLE framework
presented by Warfield et al. (2004). The STAPLE approach uses
Expectation Maximisation to iterate between the estimation of the
'true' consensus segmentation and the estimation of reliability
parameters for each of the raters (which in this work are represented
by propagated segmentations). The reliability parameters are based
on the sensitivity and specificity of each rater and are used to weight
their contributions when generating the consensus estimate. The
current consensus estimate can, in turn, be used to measure the
reliability of the raters and this forms the basis of the EM iterations.
Theuseofmajorityvoting foreachvoxelhas,however,beenshown
to be effective in a number of contexts. Rohlfing et al. (2004a) used a
database of images of bee brains to show that fusing segmentations
using majority voting is robust and accurate compared with, for
example, the propagation of an average shape atlas, or of an individual
atlas, selected according to its similarity to the query image. The vote
rule has also been shown to perform well relative to other fusion
approaches in a more general pattern recognition context (Kittler
et al., 1998).
In the context of human brain image segmentation, we have
previously presented a series of experiments to investigate the
precision and accuracy of structural multi-atlas segmentation using
Fig.1. Schematic illustration of multi-atlas segmentation. A set of atlas anatomical images
Aiare registered to the query anatomy Q. The resulting transformations are used to
transform the corresponding atlas segmentations Li to the query. The transformed
segmentations L′iare then combined to create an estimate of the query segmentation LQ.
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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majority voting (Heckemann et al., 2006a,b). These show that multi-
atlas segmentation performs at levels of accuracy approaching those
of expert human raters with Dice overlap values (against a manual
gold standard) increasing from an average of 0.754 for non-rigidly
propagated singleatlasesto0.836 forthefusionof29 propagated label
sets. The atlases in that work consisted of brain MR images for 30
subjects with corresponding manual segmentations created using the
protocol published by Hammers et al. (2003).
A leave-one-out cross-validation approach can be used to assess
the accuracy of a multi-atlas segmentation of a query image based on
treating the manual segmentation available for that image as a gold
standard. The precision of multi-atlas segmentation can be assessed
by measuring the agreement of segmentations produced by different
subsets of atlases. The agreement between an automated segmenta-
tion estimate and a manual segmentation or between a pair of
automated segmentations can be assessed using overlap measures. A
number of measures are available, with the Dice coefficient (Dice,
1945) being a popular option in the literature. The Dice coefficient is
given by
d =
2jA \ Bj
jAj + jBj
where A and B represent the regions of the label being compared and
the volumes are measured by voxel counts.
Using the Dice coefficient as a measure, we have shown
(Heckemann et al., 2006a) that, under reasonable assumptions, the
overlap accuracy of a multi-atlas segmentation estimate, as a function
of the number of segmentations fused n, is well modelled by
d n
ð Þ = a −
where the constants to be determined, a and b, satisfy 0≤a≤1 and
Eq. (1) models monotonically increasing overlap as more segmen-
tations are fused. This increase is limited by the asymptotic constant a
while the constant b determines the rate at which the overlap
increases. As more segmentations are fused, overlap accuracy
increases because random errors, associated with individual atlas to
query registrations and atlas variability, are increasinglycancelled out.
There remains, however, a systematic bias that cannot be removed by
simply using more segmentations. It is represented by the difference
between the asymptotic value a and one. The systematic bias can arise
for a number of reasons. For example, the anatomy of the query
subject may represent a variation that is not, or not sufficiently,
represented by the atlas database. There may also be a limit imposed
by correspondence accuracy; the registrations may, for example,
produce affine transformations that do not provide the local small
scale alignment that may be necessary for accurate segmentation. In
Heckemann et al. (2006a,b), it was shown that when the transforma-
tions used to propagate atlas segmentations are non-rigid and have
a fine degree of local control (i.e. have a high-resolution), the
asymptotic level of the Dice overlap achieved is higher and the rate
of convergence is faster.
Atlas selection
Motivation and background
The size of the atlas database can affect various aspects of the
process of multi-atlas segmentation as well as the quality of the final
segmentation. As discussed in Background: multi-atlas segmentation,
the average segmentation accuracy achieved by fusing random sets of
atlases increases asymptotically as the number fused becomes large.
This asymptotic increase in accuracy means that there are diminishing
returns in using larger and larger numbers of atlases. For a large atlas
database, however, the increased computational cost of registering
large numbers of atlases to the query image is an immediate practical
problem. A second difficulty relates to the way inwhich an anatomical
structure might vary across the population. If a structure is
represented by, say, two morphologically distinct variants in the
population (and in the atlas database), then fusing a large number of
atlases may give a shape that does not represent either variant very
well. In such circumstances, for a given query subject, only a proper
subset of the atlases in the database is appropriate to use — those
sharing the variant represented in the query. Finally, in our
experience, the fusion of a large number of atlases is more likely to
create a smooth estimate of the structure being segmented and yet a
shape which is less smooth may be a better estimate.
For such reasons, the selection of a limited number of atlases,
appropriate forthequerysubject and prior to multi-atlassegmentation,
would appear preferable to the fusion of an arbitrarily large number of
atlases. Furthermore, it is natural to ask whether the asymptotic level
of accuracy given by fusing large numbers of random atlases is the best
that can be achieved, i.e. whether it is possible to equal or exceed this
accuracy by fusing a smaller number of selected atlases.
In previous work on atlas-based segmentation, the term 'selection'
has typically been used to describe the identification of the single best
atlas for propagation to a query (see, for example, Rohlfing et al.
(2004a) and Wang et al. (2005)). Han et al. (2008) compare the
selection of a single atlas against the propagation and fusion of their
entire atlas database. Our earlier work (Aljabar et al., 2007) and the
work in this paper contrasts with these approaches as we apply
selection towhole sets of atlases prior to propagation tothe target and
decision fusion. Klein et al. (2008) use a similar approach where
multi-atlas segmentation is carried out using only atlases that reach a
user-defined threshold of similarity with the target. For a given
threshold, the number of atlases used for different targets may vary.
Our work contrasts this by fixing (for each experiment) the number of
atlases selected for different targets. This enables an assessment of the
effect of selection on segmentation quality across a range of subjects.
The selection of atlases for segmenting a particular query image
also has parallels with clustering problems. For a large atlas database,
it is possible to search for clusters or modes of the population it
represents. Given an unseen query image and an estimate of the
cluster it belongs to, a better segmentation is expected using images
from the same cluster rather than from different ones. See Blezek and
Miller (2007) and Sabuncu et al. (2008) for examples of clustering
approaches applied to brain MR images.
Proposed methods for selection
Given a large atlas database, a fixed subset size and a query image,
it is theoretically possible to identify the optimal subset of atlases for
generating a multi-atlas segmentation of the query. Leaving aside the
question of how segmentations given by different subsets can be
compared, exhaustively searching all possible subsets is clearly
impractical for a large database.
We therefore present an alternative heuristic approach that uses a
measure of the 'similarity' of each atlas to the query subject. Once
assigned, this measure can be used to rank the atlases. Multi-atlas
segmentation can then becarried outusinga numberof the top-ranked
atlases. A simple approach is to interpret similarity as image similarity,
i.e. to derive it from the intensities in the query and atlas images.
Alternatively, similarity may bederived from meta-information relating
to the subjects. The ranking of each atlas is then based on how closely
the atlas subject matches the query in terms of a clinical variable, such
as age, pathology, clinical history, genetics, gender, handedness, etc.
Selection using image similarity
A selection framework that relies on evaluating the similarity of a
pair of images (an atlas and a query image) requires an estimate of the
correspondence between them. It is possible to align all the atlases to
each new query prior to making a selection. In order to avoid the
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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computational burden of a large number of registrations direct to the
query, we apply a selection framework that makes use of a standard
space defined by a reference image.
This approach identifies an arbitrary reference image in advance
and all atlases in the database are aligned to it. When a new query
image is given, it is also aligned in the same way to the reference. The
image similarityof thequery image andeach of the atlasescan thenbe
evaluated, since they are all aligned. These similarity values can then
be used to assign ranks. The top-ranked atlases are selected and
registered directly to the query in order to generate a multi-atlas
segmentation estimate in the native space of the query image. This
approach uses two types of registrations: those that align images to
the reference prior to selection and those used to propagate atlases
when generating the multi-atlas segmentations. This selection frame-
work is illustrated schematically in Fig. 2.
Image similarity can be expressed using a variety of metrics,
including sums of squared differences (SSD), cross-correlation (CC),
mutual information (MI) (Collignon et al.,1995; Viola and Wells,1995)
and normalised mutual information (NMI) (Studholme et al.,1999). In
the context of image registration, information theoretic measures such
as MI and NMI are intended for aligning multi-modality images. In the
context of selection, this makes such measures more appropriate for
images with widely differing levels of contrast and appearance, for
example if they were acquired on different MR scanners.
A related choice concerns the region over which to evaluate the
similarity metric. The region of interest (ROI) where the similarity
metric is evaluated can be the complete overlap of the atlas and query
images, or it can be made more specific. Forexample, if a hippocampal
segmentation is required, the ROI might represent a suitably located
region that is large enough to be likely to encompass the target
hippocampus and yet small enough to avoid evaluating the similarity
metric over regions distant from the structure of interest that have
little effect on its segmentation.
Another choice relating to image similarity selection concerns the
transformations used during the registrations. These can be rigid,
affine or non-rigid and, in the non-rigid case, the degree of local
control (or flexibility) can be varied. As mentioned above, the spatially
normalising transformations that are carried out prior to selection
(Fig. 2 left) need to be distinguished from the transformations used to
propagate atlas segmentations after selection and directly to the
query image (Fig. 2 right). The principle we have adopted for spatially
normalising transformations is that they should correct for gross
differences in orientation and configuration but should not correct for
small scale differences, as this will tend to make the atlases very
similar to each other and harder to rank with respect to a given query.
By contrast, the propagation of atlases during multi-atlas segmen-
tation uses transformations with a high degree of local control which,
as discussed in Background: multi-atlas segmentation, have been
shown to generate more accurate segmentations. The non-rigid
registration method used to propagate atlas segmentations can be
chosen from among a number of different approaches (see Zitová and
Flusser (2003) for an overview). This work uses the free-form
deformation (FFD) model of Rueckert et al. (1999) where displace-
ments at a lattice of control points are blended using B-spline basis
functions (De Boor, 1978).
Another distinction needs to be made between the use of a
similarity metric for selection and its use for registration. During
registration, the similarity metric is used as an optimisation objective
function, whereas for selection, the similarity metric is evaluated once
post hoc for the query and atlas images after alignment. We have used
the same metric (NMI) for selection and for registrations (both pre-
and post-selection) although there is no strict requirement that the
same metric is used in all stages.
The type of spatial normalisation carried out has an effect on the
selection process. If the atlases and the query are only rigidly aligned
to the standard space, selection will favour atlases that are already
very similar in size and configuration to the query while some atlases
may be rejected that couldhave been usefulfor segmentation after, for
example, a global change of scale.
In contrast, high-resolution non-rigid normalisation implies that
the variation among atlas subjects is mainly represented in the
normalising transformations rather than the aligned images and an
image-based ranking of the atlases becomes harder to apply.
While it is possible to extract features from transformations as a
basis for selection (see for example Commowick and Malandain
(2007)), our focus in this work is on image similarity as a selection
criterion and an intermediate level of spatial normalisation is used.
These could be, for example, affine 12-parameter transformations or
coarse non-rigid transformations that only correct for large scale
configurational differences.
In terms of computation, most of the cost of multi-atlas
segmentation with image similarity selection is incurred by registra-
tions. If N atlases are in the database and S of the top-ranked atlases
are propagated and fused, then the number of registrations for a
given query is 1+N+S. The N registrations spatially normalising the
atlases prior to selection can, however, be carried out 'off-line' so that
the bulk of the on-line computational cost is represented by the S
fine-scale non-rigid registrations that propagate the segmentations to
the query.
Selection using meta-information
Any meta-information collected from subjects at the time of
scanning can also be used as a basis for atlas selection. If information
such as gender, age, handedness, clinical status etc. is available for the
atlas database as well as for the query subject, then atlases can be
selected according to how well the corresponding subjects match the
query subject on some aspect of this meta-information. For example,
the atlas subjects who are closest in age to the query can be selected
for multi-atlas segmentation. Selection using meta-information can
be carried out independently of the image data, i.e. no pre-processing
or alignment of the images is required. After selection by meta-
information, the generation of the final segmentation is carried out
in the manner described above and illustrated on the right hand side
of Fig. 2.
Data and experiments
In order to assess the impact of the selection methods described
above upon segmentation quality, a number of experiments were
Fig. 2. Multi-atlas segmentation with image similarity selection. Left: All the atlas
anatomies Aiand the query image Q are registered to the reference image R. Similarities
between the spatially normalised query and each of the atlases are used to generate
ranks. Right: Top-ranked atlases are selected and registered directly to the query image.
The selected atlas segmentations Li are propagated to the query giving the
segmentations L′iwhich are fused to generate the native space segmentation estimate
LQfor the query image.
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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carried out which use selection prior to multi-atlas segmentation
using vote rule decision fusion. The next section describes the data
used and implementation choices for selection and multi-atlas
segmentation. This is followed by experiments aiming to measure
the effect of selection on segmentation accuracy. The objectives of
these experiments were to estimate the accuracy possible using
similarity selection, to assess the suitability of image similarity as a
selection criterion, to describe the effect of the number of atlases
selected after ranking and, finally, to compare image similarity
selection with selection based on age.
Atlas database and choice of standard space
The data used in the experiments, consisting of T1-weighted MR
brain images of 275 male and female subjects were made available by
the Centre for Morphometric Analysis (CMA, Massachusetts General
Hospital, Charlestown, MA). Ages were available for 224 subjects and
ranged between 4 and 83 years with a mean of 27.9±21.1 years. The
images were acquired from multiple centres and various structures
were manually delineated within each image according to a pre-
defined protocol (Filipek et al., 1994). An example of an anatomical
image and the corresponding manual segmentation are shown in
Fig. 3.
The setof manually delineated structures thatwere available forall
the images was mainly subcortical and consisted of the lateral
ventricle, caudate nucleus, putamen, nucleus accumbens, pallidum,
thalamus, amygdala, hippocampus and brainstem. In this work, using
leave-one-out cross-validation, a subject can be treated as a query and
its manually delineated structures can be treated as a gold standard
for measuring the accuracy of segmentations obtained by multi-atlas
segmentation (using the remaining atlases in the database).
The MNI simulated brain image provided by the Montreal
Neurological Institute (MNI), McGill University, Quebec (Holmes
et al.,1998) is used to definethe standard spaceduringimage similarity
Given the available structures, as listed above, a region of interest
(ROI) representing a mask of the subcortical region was used when
evaluating image similarity for selection. This was generated by
identifying a subcortical mask separately for each atlas image and
spatially normalising all the masks using affine registrations between
each of the CMAT1 images and the MNI reference. The union of all the
aligned masks was then found and morphologically dilated twice
(3×3×3 cubic structuring element) to generate a collective sub-
cortical mask for the database that is uniformly applied during all
experiments. The resulting mask is shown in Fig. 4.
The images for all subjects were transformed to standard space
following affine registrations of the T1 scans with the reference. The
similarity metric used for selection was NMI. Twenty atlases were
selected from the ranked database for each leave-one-out experiment
with the exception of the experiment described in Varying the
number of atlases selected which investigates the effect of selecting
varying numbers of atlases.
The non-rigid registrations from each set of selected atlases to the
corresponding leave-one-out query were initialised with affine
transformations and were modelled using FFDs. The FFD registrations
Fig. 3. An example dataset from the CMA data. Top to bottom: A T1-weighted MR image; the segmentation boundaries overlaid on the anatomy; the segmentation.
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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were implemented using a hierarchical coarse-to-fine approach
(Schnabel et al., 2001) with successive control point spacings of
20 mm,10 mm and 5 mm. At each stage in the registration the control
points with the current spacing were optimised and then used as a
starting point for the next.
Where possible, all the subjects in the database were treated in
turn as a test subject and the remaining subjects were used as a
training set from which to draw atlases for segmentation. This was
impractical, however, for experiments that use segmentations
obtained from many different random sets of atlases and which
require a large number of additional registrations. For such experi-
ments, an exemplar group of three subjects was used as a test set. The
three exemplar subjects were chosen to represent the adolescent,
young adult and older subjects. The individuals chosen are referred to
as Subjects 1, 2 and 3 and have ages at scan of 12, 29 and 79 years
respectively. For the experiment that makes use of age data, 224
subjects for whom age data were available were each treated as test
Image similarity selection: assessing the accuracy obtained
Experiments were carriedout using leave-one-out cross-validation
to assess the accuracy of segmentations based on image similarity
selection and multi-atlas segmentation. Summative results were
obtained using all subjects in the database and more detailed results
for separate structures within individual subjects were obtained using
the exemplars as test subjects.
The summative data were obtained using segmentations for all 275
subjects in the database produced using similarity selection as de-
scribed in Selection using image similarity. For each subject, 20 atlases
were selected from the remainder of the database and registered to
the subject. The accuracy of the resulting segmentation was measured
using Dice overlap with the subject's manual segmentations.
The resulting average accuracy values for all 275 subjects using
leave-one-out cross-validation are shown in Fig. 5. As a comparison,
the Dice values for the fusion of random sets for the exemplar subjects
(see below) were pooled and the resulting averages are also shown.
This figure shows, for example, that the average combined left–right
Dice accuracy for segmentations of the hippocampus was approxi-
mately 0.83 when using similarity selection and 0.81 based on fusing
random sets. Separate left–right mean Dice values and standard
deviations for each structure are also shown in Table 1. The mean Dice
overlap over all structures was 0.854 using image similarity selection
and 0.811 using random sets.
Table 1 also shows the averages based on the pooled random set
data for comparison. Dice accuracy gains of 0.02 to 0.05 are typical
when using similarity-based atlas selection instead of random sets.
The most dramatic improvements are shown for the caudate nucleus
with an improvement of approximately 0.12. The location of the
caudate nucleus may mean that any improvement in its segmenta-
tion accuracy is related to improvement in the segmentation of the
adjacent ventricles. Due to their much larger size, the increase in
Dice for the ventricles (approximately 3–5 points assuming a scale
of 0–100) is expected to be smaller than the increase for the
caudate nucleus.
Experiments were also carried out for the three exemplars where
accuracy was measured for each structure within each subject. The
segmentations for each exemplar were generated using image
similarity selection and the resulting Dice accuracy of each structure
against the manual segmentationwas subsequentlycompared against
the accuracy of 1000 further segmentations, each obtained by fusing
random sets of atlases. Comparing with the distribution of Dice
obtained from random atlas sets helps to assess the significance of the
accuracy obtained via atlas selection.
In order to enable the fusion of random sets of atlases for each of
the exemplar subjects, all the remaining images in the database were
non-rigidly aligned to each exemplar up to a 5 mm control point
spacing. For each of the three exemplar subjects, random sets (20
atlases each) were drawn from among the remaining 274 non-rigidly
alignedatlases.The segmentationswithineachrandomset werefused
to generate an automated segmentation estimate and the Dice
overlaps with the query's manual segmentation were calculated.
This process was repeated 1000 times with different sets of random
atlases in order to estimate the distribution of Dice overlaps for each
structure within each of the exemplar subjects. Box plots of the
estimated Dice overlap distributions are shown for the segmented
structures in Fig. 6, which illustrates the results for Subject 2, the 29-
year-old subject. The corresponding box plots for the younger and
older subjects are shown in Figs. 7 and 8. As an example, focusing on
the left pallidum in Fig. 6, the random overlap distribution has a
median of approximately 0.84, the limits of the inter-quartile range
Fig. 4. The subcortical mask used for image similarity selection overlaid onto the image of the MNI simulated MR image.
Fig. 5. Average Dice overlap values for all 275 subjects obtained by fusing atlases
selected by image similarity (grey bars). For comparison, the average Dice values
obtained by fusing random sets of atlases are also shown (white bars).
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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are 0.83 and 0.85 and the whiskers, representing the extremes of the
distribution, are at 0.79 and 0.87. The Dice overlap of the single
segmentation of the left pallidum derived using image similarity
selection is plotted as a circle and is above the whisker at the upper
end of the random distribution (≈0.89).
It can be seen in the figures that, in nearly every case, the
segmentation accuracy obtained using selection exceeds the 75th
percentile of the random Dice distribution. The order of structures by
selection Dice accuracyandthe spread of random Dice estimates differ
markedly across the three exemplar brains. This might reflect local
differences, or simply rater heterogeneity. For Subjects 1 and 3, the
accuracy of caudate segmentation using selection exceeds the random
Dice distributions by a large amount: the differences between the
random distributions' upper quartile and selection accuracy are in the
range of 9–12 points. For a number of other structures, the accuracy
given by selection also exceeds the outlier values of the random
distribution. A negligible percentage (b0.01%) of the fused random
sets achieved overlap values outside the outlier limits defined by the
whiskers of each box plot. These were not plotted for better
readability. The outlier limits were set at 1.5× the inter-quartile
range (IQR) above the upper quartile and 1.5× IQR below the lower
The quality of similarity selection accuracy values can also be
assessed by representing them as z-scores based on the random
overlap distributions. The similarity selection Dice value for a
particular subject and structure can be converted into a z-score by
subtracting the mean of the random overlap distribution and dividing
by the standard deviation. The z-score measures the signed difference
between an overlap achieved using similarity selection and the
random distribution mean expressed as a number of standard
deviations. The use of z-scores assumes a Gaussian distribution for
the Dice values which is theoretically not possible as Dice coefficients
are strictly bounded between zero and one. We have, however, shown
in previous work (Heckemann et al., 2006a) that the Gaussian
assumption is reasonable when Dice values for a structure are
sufficiently distant from these bounds.
The z-scores for Subjects 1–3 are shown in Table 2 (left–right
values combined) and in Fig. 9 (left–right values separated). In
general, these data show that similarity selection prior to multi-atlas
segmentation performs much better than the fusion of random sets of
atlases. The least improved structure across all three subjects was the
nucleus accumbens, a small structure for which similarity selection
gave accuracy values, on average, 1.6 standard deviations above the
mean. The 29-year-old exemplar subject (number 2) showed a lower
overall improvement for selection over random fusion (average z-
score 2.12) than the older and younger subjects. This may relate to the
age of exemplar Subject 2 being closer to the overall mean age (27.9±
21.1 years) of the database, i.e. that randomly chosen sets of atlases
may be more likely to be similar to Subject 2. Therefore the accuracy
after random atlas fusion may match more closely the accuracy given
by selected atlases. The average z-score achieved overall was 2.91,
which represents a cumulative percentage of 99.8% of a normal
distribution, making the Dice overlaps achieved by selection sig-
nificantly higher than those obtained by random fusion.
Image similarity as a selection criterion
We made an assessment of the extent to which image similarity
represents a suitable selection criterion with respect to the ultimate
Fig. 6. Illustration for a 29-year-old subject (Subject 2 in the text) of similarity selection
segmentation accuracy. This was assessed by Dice overlap with the subject's manual
segmentation and is plotted as a circle. The boxplots indicate the Dice overlap
distribution from 1000 fusions of random sets of 20 atlases each. The bounds of each
box represent the 25th and 75th percentiles of the random distribution of Dice values
obtained. The random overlap distributions provide a benchmark against which the
overlaps from selection can be compared.
2see e.g. http://mathworld.wolfram.com/Outlier.html.
Table 1
Per structure results for the overlap accuracy achieved after image similarity selection.
Structure L/RSelectionRandom
Lateral ventricleL
0.914 (0.04)
0.911 (0.04)
0.908 (0.02)
0.909 (0.02)
0.883 (0.03)
0.879 (0.03)
0.898 (0.02)
0.898 (0.02)
0.819 (0.05)
0.818 (0.05)
0.832 (0.04)
0.837 (0.04)
0.778 (0.06)
0.776 (0.06)
0.765 (0.07)
0.751 (0.07)
0.941 (0.01)
0.866 (0.09)
0.882 (0.05)
0.854 (0.02)
0.862 (0.02)
0.747 (0.11)
0.766 (0.09)
0.887 (0.03)
0.882 (0.03)
0.803 (0.03)
0.800 (0.02)
0.808 (0.03)
0.825 (0.03)
0.749 (0.05)
0.728 (0.09)
0.726 (0.08)
0.698 (0.07)
0.903 (0.02)
The middle column shows the average Dice value over 275 leave-one-out
segmentations. For comparison, the final column shows the average overlap achieved
by fusing 1000 random sets of 20 atlases each for the exemplar subjects. Left–right
overlaps are shown on successive rows.
Fig. 7. Illustrationof similarityselectionsegmentation accuracyfora 12-year-old subject
(Subject 1 in the text). See Fig. 6 for further explanation.
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aim of obtaining accurate segmentations. When registering two
images, X and Y, the objective is to find a transformation Tθwith
parameters θ that maximises the similarity, sim(X, Tθ(Y)); the only
images that are used during registration are X and Y. This contrasts
with the use of similarity for selection where similarities between a
query image and multiple other subject images are considered. If Q
represents a query image and the atlas database images are
represented by Yi, 1≤i≤n, then the set of n values
sim Q;Y1
are used to provide ranks for the database.
Given two atlases, A and B, it is desirable that similarity selection
determines the better of A and B as potential segmentation atlas for
Q. This means that if sim(Q, A)Nsim(Q, B) then atlas-based
segmentation using atlas A should generate a more accurate
segmentation of Q. Such a desirable property may, however, be
confounded, for example by contrast differences or varying quality of
the atlases or scan protocol.
An experiment was therefore carried out to assess the suitability of
image similarity as a selection criterion. This focuses on the relation-
ship between the ranks of atlases as given by image similarity and
their performance (as individual atlases) in segmenting the query
image. Working in the affine normalised space, each of the atlas
subjects was left out in turn and the similarity ranks of the remaining
atlases were calculated. Additionally, the accuracy of the remaining
images in segmenting a structurewas estimated from the Dice overlap
Þ; N ;sim Q;Yn
of their label for that structure with that of the left out subject.
Repeating this process for all subjects allows the average accuracy to
be calculated across the atlases at each rank. This procedure was
carried out twice as the rankings are not symmetric: the rank of atlas
A as a segmenter for query B does not, in general, equal the rank
obtained by reversing their roles. This meant that 275×274
comparisons were carried out.
The results are illustrated by plots of the average Dice overlap
against the rank. These are shown for the hippocampus and the lateral
ventricle in Fig.10. Each point plotted shows the average Dice overlap
for all atlases at a given rank in segmenting (as a single atlas) the
query for which the rank is calculated. These plots indicate that high
ranks are associated with a higher level of accuracy. Although no
segmentations are fused in this experiment, the correlation between
atlas rank, determined by similarity, and accuracy supports the use of
similarity as a basis for selecting atlases prior to fusion (correlation
coefficients: lateral ventricle, −0.95; hippocampus, −0.88).
Varying the number of atlases selected
When using similarity selection, the atlases are ranked according
to their similarity with the query image. The number of atlases to be
used for multi-atlas segmentation can be chosen by the user. This
section describes a test of the effect of varying the number of atlases
selected upon the final segmentation. Using each of the three
exemplar subjects, the ranks of the remaining atlases were deter-
mined as described in Selection using image similarity. Increasing
numbers are then selected from the ordered list of atlases. For each
number selected, the corresponding segmentations were fused to
provide separate segmentation estimates. The set of segmentations
combined after selecting k atlases are the same used after selecting
k−1 atlases with the inclusion of the kth atlas in the ordered list.
The accuracy of each segmentation estimate was assessed using its
Dice overlap with the query image's manual segmentation. This
process was enabled by having all the atlases non-rigidly aligned to
each of the exemplar subjects.
Graphs to illustrate how the resulting segmentation accuracy
varies with the number of atlases used are shown in Fig. 11. These
graphs show the average combined left–right Dice accuracies of
various structures' segmentations for the three exemplar query
subjects. The average accuracy over all structures is also shown in
Fig. 11. In order to provide a comparison, for each number, k, of
Fig. 8. Illustration of similarity selection segmentation accuracy for a 79-year-old
subject (Subject 3 in the text). See Fig. 6 for further explanation.
Table 2
z-scores for the Dice accuracy of combined left–right segmentations produced after
image similarity selection.
Lateral ventricle
The z-scores are based on the means and standard deviations of the random overlap
distributions (see Image similarity selection: assessing the accuracy obtained).
Fig. 9. z-scores for the Dice overlap accuracy of segmentations based on image
similarity selection (see also Table 2). Separate bars are shown for paired left–right
structures. Abbreviations: Lateral ventricle (LV), thalamus (thal), caudate nucleus
(caud), putamen (put), pallidum (pall), hippocampus (hipp), amygdala (amyg),
nucleus accumbens (accum), brainstem (stem).
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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atlases selected from the ranked list, a number of random sets of k
atlases each were also fused and their Dice overlaps were averaged.
50 random sets were fused for each value of k with each exemplar
subject and the resulting averages are also shown in each chart as a
dashed line.
The general pattern for the ranked atlas results shows a sharp
initial increase in overlap accuracy up to a maximum level followed by
a gradual decline. This contrasts with the overlaps achieved by fusing
random sets of atlases; after an initial sharp rise, these show a
continued slower and monotonic increase, always remaining below
the accuracy obtained by selection. In Fig. 11, the shape of the curves
obtained from the random atlas sets is predicted by Eq. (1). This
equation does not apply to the curves for segmentations obtained
from the ranked atlases.
As the number fused approaches the number of atlases in the
database, the accuracy of ranked and random atlas fusion converges to
the same level, the accuracy that would be obtained by combining the
whole database to give a segmentation estimate. In all cases, this level
of accuracy is exceeded by using a relatively small number of selected
The number of ranked atlases required for the highest accuracy
variesforthedifferentstructures.The overlapsfor thecaudatenucleus
reach a maximum for about 8 atlases, while the maximum for the
hippocampus is reached after selection of the top 25 atlases on
average. The average overlaps across all structures in the bottom right
of the figure show a fairly flat section of the highest overlap values for
between 15 and 25 atlases.
Comparing similarity- and age-based selection
An assessment was made of the value of meta-information by
comparing the segmentation accuracy given by similarity-based
selection and by age-based selection. This comparison was possible
for 224 subjects in the database, the ages of the remaining subjects
were not available. Similarity ranking and selection was carried out as
described in Selection using image similarity. Age selection was
Fig. 10. Relationship between average Dice accuracy obtained by individual atlases for a given rank as determined by image similarity with the query (N=275). Results for the
hippocampus are shown on the left, those for the lateral ventricle are shown on the right.
Fig. 11. Segmentation accuracy for various structures in the exemplar subjects after
fusing increasing numbers of ranked atlases (Varying the number of atlases selected).
The vertical axes show average Dice value obtained and the horizontal axes show the
number fused. For comparison, the dashed lines show the average overlap obtained by
fusing random sets of atlases of increasing size (N=50 for each point). The contiguous
and dashed lines in each plot will converge as more atlases are used. Data for five
structures (combined left–right) are shown. The average over all structures is shown in
the plot at the bottom right.
Fig. 12. Comparison of age-based selection and similarity-based selection for the 224
subjects with age data available. The horizontal axis shows the age. Subjects in different
age groups are plotted with different symbols. The vertical axis shows the gain that age
selection gives over similarity selection.
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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carried out by ranking the atlas subjects based on their age proximity
to the query and identifying the twenty closest subjects in age.
The overlaps of the segmentations obtained using each selection
method with the subjects' manual segmentations were then evalu-
ated to allow the selection methods to be compared. The average
overlap over the subcortical structures was used to give a summary
measure for each selection criterion on each subject. Subtracting the
means then gives a single figure per subject representing the gain
obtained by age-based selection.
The resulting values are shown in Fig.12, where points lying above
the horizontal axis represent subjects for whom age selection
represents an improvement over similarity selection. The subjects
are divided into the three broad age groups and their results plotted
with different markers.
Amongtheyoungest subjects(represented bydots) therearesome
for whom age selection appears to produce a slightly worse result,
althoughthedifferencesare small.Themeangainthroughageselection
for the adolescent subjects was −0.0055±0.0057 and the maximum
change was −0.0199. The results for the older group (represented by
circles) were more varied than the younger groups, but contained
the subjects for whom age selection made the most improvement.
The mean Dice gain for the older subjects was 0.007±0.011 with a
maximum change of +0.03. The results for the young adult group
(represented by crosses) are intermediate between those for the
adolescents and those for the older groups.
To give a qualitative impression of the atlases selected, the top 10
atlases byimagesimilarity rankwereidentified for the three exemplar
subjects described in Implementation. Transverse sections through
the frontal and occipital horns of both lateral ventricles from each of
the MRIs associated with the top-ranked atlases for each subject are
shown in Fig. 13 (age 12), 14 (age 29) and 15 (age 79).
The ages of the top ten atlases for each of these three subjects are
shown in Table 3. The table shows that, although the top-ranked
atlases were selected by image similarity, they match the age of the
query subject well. This further demonstrates the level of agreement
between age- and similarity-based selection and relates to their
comparable segmentation accuracy as shown in Fig. 12.
We have presented an investigation of strategies for atlas selection
prior to multi-atlas segmentation. The accuracy obtained by atlas
selection has been assessed against the levels of accuracy achieved by
fusing random sets of atlases. The results show that the accuracy
achieved by similarity selection is significantly higher than that
achieved by the fusion of random sets of atlases. The results obtained
from a large number of leave-one-out cross-validation experiments
(Table1) compare very well withthe state-of-the-art (see forexample
Fischl et al. (2002), Klein and Hirsch (2005) or Chupin et al. (2007))
and are comparable with some previous manual segmentation
Fig. 13. Exemplar Subject 1 (age 12, left) and the top 10 atlases selected using image similarity (see also Table 3).
Fig. 14. Exemplar Subject 2 (age 29, left) and the top 10 atlases selected using image similarity (see also Table 3).
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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methods, for example Spinks et al. (2002). Inparticular, the results we
obtained compare well with those presented in Heckemann et al.
(2006a), which also use multi-atlas segmentation. It is worth noting,
however, that our accuracy levels were achieved using a coarser FFD
registration than those giving the best results in Heckemann et al.
(2006a) (control point spacings of 5 mm vs.2.5 mm).
Age-based selection provides very similar levels of segmentation
accuracy to similarity selection, indicating the potential that meta-
information selection has when used with multi-atlas segmentation.
The framework for similarityselectionpresented in Selectionusing
image similarity is reasonably simple and provides good results.
Variations are clearly possible, for example over whether or not a
standard space is used or in the choice of space in which similarity is
measured. Wehaveused theMNIsimulatedbrainimageas areference
for the selection step. We are aware that the choice of a single image
may introduce a bias but feel that the results obtained justify the
method as a whole and leave to future work an investigation of the
effect of 'bias-free' or 'group-average' atlases at the selection stage. We
have also varied the choice of similarity metric, again obtaining very
similar results.
The number of exemplar subjects for which comparisons are made
against random overlap distributions is limited by practical con-
siderations, in particular CPU time, since many registrations are
required. In spite of this limitation, it remains possible to obtain a
reasonableimpression of thegaininperformancethatcan be obtained
by similarity-based selection (see for example Table 1). In comparison
with approaches that build explicit models, for example active
appearance models or EM approaches, the computational time
required for segmenting new images can be relatively high. The bulk
of the computation required for our approach is taken up by atlas to
query registrations. We ran these in parallel using the Condor
distributed scheduling system3and the time required to segment an
image varied, with typical times being 3 to 4 h. In contrast, methods
that use explicit models typically require a significant time to build
and train the model, although the application to new data is usually
much faster.
We have assessed the suitability of image similarity as an atlas
selection criterion. The results (Image similarity as a selection
criterion) suggest that the similarity-based rank of an atlas
correlates well with its accuracy as a potential segmenter of
structures in the subcortical region for the query and provides
justification for the use of image similarity in selection. Additionally,
a visual inspection of the top-ranked atlases for three query subjects
(Figs. 13–15) indicate that the anatomies of the image similarity
selected atlases show a close match with those of the query subjects.
It appears plausible that an individual atlas will perform better if the
target subject is morphologically similar and our work provides
evidence to support this intuition. The ROI used in this work
encompassed a number of subcortical structures and the hippo-
campus. We are encouraged to carry out further work to assess the
impact of ROIs that either target structures more specifically or are
intended for use in segmenting cortical regions.
The accuracy figures achieved by the fusion of different numbers
of ranked atlases presented in Varying the number of atlases selected
indicate that simply using larger and larger numbers of atlases (after
ranking and beyond approximately 20 atlases) leads to lower
accuracy in the resulting segmentation. When applying image
similarity selection, as the number of atlases fused increases from a
low number, the accuracy achieved tends to rise quickly to a
maximum and then gradually to decline. The decline in the overlap
values as increasing numbers of atlases are used may be explained by
the convergence or bias of the resulting fused segmentation towards
the mean shape; this may not represent the query subject as well as a
segmentation provided by fewer atlases selected specifically for the
The number of atlases required for the maximal accuracy varied
according to structure, but the fusion of approximately 20 atlases
produces near maximal accuracy when the results are averaged across
structures. This suggests that the choice of 20 atlases after selection, as
is carried out in the remaining experiments, is reasonable. If the
Fig. 15. Exemplar Subject 3 (age 79, left) and the top 10 atlases selected using image similarity (see also Table 3).
Table 3
The ages of the top 10 atlas subjects selected using image similarity for three different
query subjects.
Rank of atlasAge of query subject
Mean (SD)
9.4 (2.55)
34.72 (15.74)
67.6 (14.20)
The three subjects were chosen to cover the age range of the data.
The atlas subjects for each exemplar in this table correspond to the transverse slices
illustrated in order in Figs. 13–15.
P. Aljabar et al. / NeuroImage 46 (2009) 726–738
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segmentation of a particular structure is required, the number of
atlasesused fromtheranked atlases couldthen be adapted specifically
to suit the structure.
Another general implementation choice was that of the ROI used
for making similarity comparisons. This represented a mask that
covered all the subcortical regions for all subjects. A structure-specific
implementation could easily incorporate a mask tailored to the target
Comparing similarity- and age-based selection gives a comparison
of the accuracyachieved after selection basedonageand onsimilarity.
The results suggest that the difference in their performance is
generally negligible, with both approaches significantly outperform-
ing random fusion (as shown by the results in Image similarity
selection: assessing the accuracy obtained). For some subjects,
selection on age produces a slightly worse result, although it should
be stressed that the differences are very small. The subjects for whom
age selection gave the largest improvement were among the oldest.
This may be due to accelerating morphological change during old age
and its impact on the appearance of the image data. This suggests that
multi-atlas segmentation for older subjects may benefit more from
the use of age-based selection instead of similarity-based selection.
Interestingly, when comparing against the Dice overlaps generated by
the fusion of random sets of atlases (Image similarity selection:
assessing the accuracy obtained), the subject for whom the similarity
selection gave the greatest improvement over random atlas fusion
(Table 2) was the oldest subject (age 79).
There are a number of potential directions for future work on
selection and multi-atlas segmentation. For example, adaptive
weighting of the different atlases could be used during the fusion
stage where, perhaps, a local measure of the similarity between
atlas and query might be used to weight votes during fusion rather
than using a simple majority vote. Future work can also be carried
out to assess the use of STAPLE when fusing propagated atlases
after selection. The simplest possible fusion method (vote rule) was
used in this work in an effort to reduce computational cost.
Alternatively, selection based on geometric features can be
explored and compared with selection based on image similarity.
Such an approach may draw upon work in deformation- or tensor-
based morphometry and may make use of geometric features
extracted from the transformations between the atlases and a
reference. Such features can, in turn, be used as a basis for selection.
Initial work in this area is presented by Commowick and Malandain
(2007) where the displacement magnitudes of non-rigid query to
atlas transformations are used to select the most similar atlas to the
query image.
In a recent application, the methods for selection and structural
segmentation presented in this work were successfully incorporated
into a framework for assisting clinical diagnosis in cross-sectional
studies. The framework also makes use of graph-theoretic spectral
clustering techniques and was applied to image data acquired from
subjects with early symptoms of dementia (Aljabar et al., 2008a,b). In
further recent work, the accuracy of the selection and segmentation
scheme presented in this work compared very favourably against
other segmentation methods, based either on Active Appearance
Models or on EM-based classification, which were applied to the same
data (Babalola et al., 2008).
P. Aljabar acknowledges the support of the EPSRC Integrated Brain
Image Modelling project (GR/S82503/01) and the MRC funding of the
NeuroGrid project (www.neurogrid.ac.uk). R.A. Heckemann was
supported by a grant from the Dunhill Medical Trust. The authors
would like to thank David Kennedy and the Centre for Morphometric
Analysis at MGH for making the image and segmentation data
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text: I am trying to import this project into eclipse using the egit plugin. I have been using this tutorial to learn how. But the problem is figuring out what to enter into the dialog box within eclipse. Neither of the links above provide adequate information about that. I am including a picture of the eclipse dialog box below. Can anyone show me what to put into each input field in the dialog box?

subset: commoncrawl


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Posts tagged "oscar pistorius"
Oscar Pistorius Sentenced To 5 Years In Prison
Oscar Pistorius has been sentenced to (a paltry) five years in jail for the death of his girlfriend Reeva Steenkamp. Pistorius was convicted of culpable homicide. [Us Weekly] Jill Duggar and her husband are expecting a son, the first of what I'm sure will be a boatload of kids. [People]…
Amelia McDonell-Parry
October 21, 2014
Oscar Pistorius Found Guilty Of Culpable Homicide
A South African judge found Olympic athlete Oscar Pistorius guilty of culpable homicide (equivalent to manslaughter) in the murder of his girlfriend Reeva Steenkamp. Pistorius shot her to death in his home on Valentine's Day 2013; he claimed that he thought intruders were in his home. Yesterday, the same judge acquitted Pistorius of…
Jessica Wakeman
September 12, 2014
Oscar Pistorius Acquitted Of Premeditated Murder
Today, South African paralympic athlete Oscar Pistorius was acquitted of premediated murder for killing his girlfriend, Reeva Steenkamp, on Valentine's Day last year. The judge said it could not be proved that Pistorius intended to kill Steenkam…
Jessica Wakeman
September 11, 2014
Reeva Steenkamp's Texts To Oscar Pistorius Weeks Before He Killed Her Are Totally Disturbing: "I'm Scared Of You Sometimes"
Today the prosecution rested its case in the trial of South African Paralympian athlete Oscar Pistorius for the murder of his girlfriend, Reeva Steenkamp, 29, on Valentine's Day 2013. Steenkamp, a model, was found shot several times through the locked bathroom door in Pistorius' home; he claimed that he accidentally shot…
Jessica Wakeman
March 25, 2014
Irish Bookies Bet On Whether Oscar Pistorius Will Be Convicted Of Murdering His Girlfriend
Irish bookies Paddy Power have a bet going on the Oscar Pistorius trial, promising "money back if he walks" — that is, if the Olympian isn't convicted of allegedly murdering his girlfriend Reeva Steenkamp on Valentine's Day one year ago. Advocacy groups against domestic violence have called the bidding "sick" and a Change.org…
Jessica Wakeman
March 3, 2014
Shirtless Oscar Pistorius Covers
A shirtless Oscar Pistorius is on the cover of TIME magazine regarding the murder of his girlfriend, Reeva Steenkamp. [Mirror UK] More uncomfortable PDA between Mary-Kate Olsen and her much-older boyfriend, Olivier Sarkozy. [Socialite Life] Oberlin College cancelled classes today after someone wearing a KKK robe wa…
Jessica Wakeman
March 4, 2013
Oscar Pistorius Granted Bail
After days of testimony from both the prosecution and defense, a South African magistrate has granted Paralympic sprinter Oscar Pistorius -- accused of murdering his girlfriend Reeva Steenkamp -- bail. In delivering his decision, the magistrate pointed out that there are many holes in Pistorius's defense -- Pistorius admits he shot Steenkamp, but says he…
Amelia McDonell-Parry
February 22, 2013
Pistorius Case: Witness Heard Woman Scream, More Gunshots
Police labeled Oscar Pistorius a flight risk as his bail hearing resumed in South Africa today, reports the AP. Prosecutors, who accuse the athlete of premeditated murder, told the court that a witness heard "nonstop talking, like fighting" from 2 a.m. to 3 a.m. on the night girlfriend Reeva Steenkamp was shot, The New York Times reports. The…
February 20, 2013
Oscar Pistorius Denies Reeva Steenkamp's Death Was Premeditated Murder
This morning, Olympic athlete Oscar Pistorius faced his first bail hearing regarding the death of his girlfriend Reeva Steenkamp and disputed the premeditated murder charge against him. The higher charge caries the most intense bail requirements of South African law, according to The Atlantic. Here's an update on where the case i…
Sarah Gray
February 19, 2013
Oscar Pistorius Accused Of Premeditated Murder
Olympic athlete Oscar Pistorius sobbed in court today as prosecutors accused him of planning to murder his girlfriend Reeva Steenkamp, 30, whom he allegedly shot four times yesterday morning in his home. Former girlfriends and mothers of ex-girlfriends have been weighing in with the press, painting a disconcerting picture of Pistorius. [Sky News,…
Jessica Wakeman
February 15, 2013
Oscar Pistorius & Reeva Steenkamp: 9 Things To Know About The Couple
Oscar Pistorious, 26, a Paralympian and Olympian and inspiration to athletes around the world, was arrested today — Valentine's Day — and charged with murdering his girlfriend, model Reeva Steenkamp. Police responded to a 3 a.m. call about gunshots in Pistorius' upscale neighborhood of Silver Woods in Pretoria, South Africa, where Steenkamp, 30,…
Jessica Wakeman
February 14, 2013
Olympic Athlete Oscar Pistorius Charged With Murdering Girlfriend Reeva Steenkamp
2012 Olympic athlete Oscar Pistorius was charged with murdering his girlfriend, model Reeva Steenkamp, today after she was found dead after being shot four times at his home in Pretoria, South Africa. [People, US Weekly] Hot ginge Ron Weasley, aka Rupert Grint, has been cast in a new TV show for…
Jessica Wakeman
February 14, 2013
Simply Irresistible
All »
The 20 Hottest Sex Scenes From "Game Of Thrones"
When you play the game of thrones, you either win or you die. But you get to f*ck a lot first. So . . . it's kinda worth it. The show is known for its gratuitous amounts of nudity and raunchy sex, so what better way to celebrate the impending Game of Thrones Spring premiere…
Tres Sugar
March 29, 2015
The 10 Definitive Cult Films You Absolutely Must See
What makes a movie a "cult classic"? Film history is littered with hundreds of films that never made it big, but developed small, passionate followings that allowed them to build up a reputation over time. Sometimes a good film is poorly marketed by a studio more concerned with their bottom line; other times genuinely bad…
Ask Men
March 28, 2015
Dave Grohl Helps Out A Dying Fan
A big Foo Fighters fan with terminal cancer will get to scratch off one item from his wish list: meeting Dave Grohl and the band. The band was flying home from Australia when a flight attendant told Grohl about the wish of her friend, Australian Ken Powell, 56, who has late-stage melanoma, report…
March 28, 2015
Amanda Knox's Murder Conviction Has Been Overturned
Foxy Knoxy is off the hook.
Claire Hannum
March 27, 2015
You'll Love
If These Style Blogger Poses Could Talk...
8 More Realistic Types Of Female Orgasms
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Onvia ReviewsOnvia 50
"A day in the life "
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I have been working at Onvia full-time (less than a year)Pros
Onvia's work environment is captivating and inspiring. Every team has its own personality that is well balanced and designed to fit that teams needs. There is a wealth of knowledge and experience to gain from the people here.Cons
Some rolls are still being developed and can cause an unclear definition of responsibilities. Not many company involvement with the community or company events out side work hours.Advice to ManagementAdvice
Upper management is definitely present and involved in daily operations.RecommendsPositive OutlookApproves of CEO
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Pretty straightforward interview, nothing special. All behavioral questions. Just wanted to get to know your background, why Onvia, etc. Did a 15 minute phone interview initially, and then they called back a couple weeks later to schedule an in-office. Met with HR, and content manager. Got a tour of the office. and got to interact with employees in potential department.Accepted OfferPositive ExperienceEasy Interview
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Onvia aims to help companies win government contracts and government agencies find suppliers by providing a database of business leads. The company identifies purchasing behavior and bid opportunities from some 89,000 federal, state, and local government agencies, as well as private-sector enterprises, and delivers notices of those opportunities to its 6,200 subscribers. Its Onvia database includes data on millions of current and historical contracting opportunities in markets such as construction, consulting, and information technology. Subscriptions account for about... More


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Meerut: The clash involving 10 Sikh Light Infantry unit in Uttar Pradesh`s Meerut Thursday, in which two officers and a jawan were injured, is the fourth major instance of indiscipline in the Indian Army in the last three years.
In August 2012, there was face-off between officers and men of the 16th Cavalry Regiment in Jammu and Kashmir`s Samba after a soldier shot himself dead there. Defence Minister AK Antony later said that the incident of suicide led to the unrest.
In May 2012, officers and soldiers of the 226 Field Artillery Regiment, which was going through a month-long field training camp, came to blows at the firing rage in Mahi near Nyoma in Ladakh.
An army Court of Inquiry (CoI) had recommended disciplinary action against 168 personnel, including the unit`s commanding officer.
In April 2010, an altercation took place between an officer and a jawan of 45th Cavalry in Punjab`s Gurdaspur.


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a Remix of ... http://www.freesound.org/people/snardin42/sounds/73200/ Cartoon clang : hit with shovel/spade noise.
3D remixed version of Magic Wand ... http://www.freesound.org/samplesViewSingle.php?id=75832
slow creepy warped version. Sounds better when downloaded (no compression artifacts).
"bwaang" sweep gong-ish synthesized sound at different speeds pitches.
High frequency 3D flyby woosh (in both directions). Sounds much better when downloaded.
Request for electronic chirp clink sound ... http://www.freesound.org/forum/viewtopic.php?p=24618#24618
Another variant of ERH's loop of synth string. sounds more a Zither.
Wider version of ERH's synth string loop, like the original it doesn't loop.
longer wider version or ERH's string loop (which doesn't loop, but this does, just)
Remix (MP3) with original hissy "gas-leak" equalisation. Sounds much better when downloaded: without the compression artifacts which can be heard ...
The letter "U" in Morse code at different speeds, a request. The frequency of the note is 1000Hz sine (like ...
Pycckuu's synthetic angelic choir, minus the gas-leak type hiss. More like organ than human, (others in this pack are more ...
Attempts to reduce bow noise from SpleenCast's "hearts and flowers" violin. Like similarly named #3 but more treble. Original sample ...
Attempts to reduce bow noise from Spleencast's "hearts and flowers" violin. Original sample at either end.
Weird, industrial rhythm loop made from Jeff25's thermos-flask noise.


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Swallowing Risk Of Getting AIDS
Here's a taste of what TheBody.com has to offer on this topic:
Get the Facts
Oral Sex: What's the Real Risk for HIV?...gonorrhea through oralsex. Studies on the Risks of Oral Sex Even though oral sex is a lower-risk activity, people have reportedlybecome infected with HIV in this way. A number of studies have tried todetermine the exact level of risk of oral sex, but it can be difficult to getaccurate information from study participants. Since oral sex is not the onlysexual activity for most people, it is also difficult to single out oral sex asthe...
Ask the Experts
BJ and swallowedHello Dr Bob!I'm 22 YO and I'm writing from Venezuela... I've been reading this forum for more than one year.My question is... Through this year... I performed oral sex to 3 friends... they cummed in my mouth and I swallowed on the three separated...
Response from Robert J. Frascino, M.D.
The Robert James Frascino AIDS Foundation...taking effective combination antiretroviral therapy, which has driven the HIV plasma viral load to undetectable levels, the risk of HIV transmissions is significantly decreased, but not completely eliminated. See below. Only you can decide what... Read more »
Swallowed cum. am I at risk??I had a night with 2 guys. started with condoms but ended doing full penetration with both of them both them in me and me in them(no comdom, but no come) and I swallowed their come, Am I at risk????? I'm scared as I overheard him say to his partner...
Response from Robert J. Frascino, M.D.
The Robert James Frascino AIDS Foundation...Hi,I think you already know the answer to your own question, now don't you?Unprotected sex places you at risk for STDs, including HIV. I strongly advise you not place yourself at any additional risk. You'll also need STD screening plus Read more »
Swallowing-Increased Risk of Transmission?Hello Dr Bob,Thank you so much for this wonderful site. I have gained some valuable knowledge from the variety of questions. One thing I noticed is that we need to change the sex education programs in our public schools. After reading some of the...
Response from Robert J. Frascino, M.D.
The Robert James Frascino AIDS Foundation...receptive partner for oral sex is the one who has to decide whether to spit or swallow. Regarding the estimated statistical risk for unprotected receptive oral sex, the 1 in 10,000 statistic refers to all cummers. We don't have statistical... Read more »
Risk from swallowing a pill dropped on dirty floor?Dr. Frascino: What is the risk of any type of viral infection (HIV/Hepatitis) or other bacterial infection from swallowing a pill after it was dropped on a public bathroom floor that may have deposits of blood, urine, water, etc.? Thank you in...
Response from Robert J. Frascino, M.D.
The Robert James Frascino AIDS Foundation...Hi,The risk would be so remote as to be essentially nonexistent.Dr. Bob Read more »
Reducing the Risk of Getting HIV From Sexual Activities...transmitted diseases . Lubricant Use of lubricants can make sexual penetration more comfortable and may also help reduce the risk of condom breakage during sex. With latex condoms, however, be sure to use a water-based lubricant. Do not use... Read more »
HIV Transmission...how effective this method really is in reducing transmission. It is best to use fresh needles each time. This eliminates any risk of infection. Many cities offer free needle exchange programs. Tattoos or body piercings should always be done by... Read more »
HIV and Safer Sex...helps condoms feel better.) Do not use oil-based lubes like Vaseline, Crisco, or baby oil with condoms because they increase the risk of the condom breaking. If you're not going to use condoms, try to reduce the friction by using a lot of lube... Read more »
You Can Have a Healthy Pregnancy if You Are HIV Positive...mothers was also HIV positive. However, we now know a lot about how to prevent vertical transmission, and with proper care the risk of your child becoming infected with HIV can be less than 2 percent. Experts have written treatment guidelines... Read more »
Connect With Others
Posted by sadface, 2 Replies...Oral sex..if I was to "swallow" does that put me at risk for Hiv?? Thanks all.. Sf Read more »
Swallowing fear
Posted by Anonymous, 1 Reply...possible exposure) I am still having these symptoms and am scared that it is seroconversion. I know they say that oral sex is low risk but because i swallowed and becuase my throat was a little red I am worried that might mean that the odds are... Read more »
swallowing infected bload
Posted by lost, 3 Replies...i ve been experiencing difficulty in swallowing for 2 months now, i don't have a sore throat at all, i am not sick, i feel very healthy, i only have this problem i feel like Read more »
Question For G-Man
Posted by Anonymous, 1 Reply...I was the one that asked you if me swallowing pre cum during oral sex was risky...you said it was very low risk, then another person said there was no risk at all. Which one of you is correct? I'm just Read more »
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