The use of large-scale multifaceted data is common in a wide variety of scientific applications. In many cases, this multifaceted data takes the form of a field-based (Eulerian) and point trajectory-based (Lagrangian) representation as each has a unique set of advantages in characterizing a system of study. Furthermore, studying the increasing scale and complexity of these multifaceted datasets is limited by perceptual ability and available computational resources, necessitating sophisticated data reduction and feature extraction techniques. In this work, we present a new 4D feature segmentation extraction scheme that can operate on both the field and point trajectory data types simultaneously. The resulting features are time-varying data subsets that have both a field and point-based component, and were extracted based on underlying patterns from both data types. This enables researchers to better explore both the spatial and temporal interplay between the two data representations and study underlying phenomena from new perspectives. We parallelize our approach using GPU acceleration and apply it to real world multifaceted datasets to illustrate the types of features that can be extracted and explored.
@cite_22: 
@cite_13: This paper presents a multi-scale morphology approach to the volume segmentation and visualization problem. The basis of the approach is applying morphological operations with spherical structuring elements at various sizes to create a representation of the volume data that encodes structural information at multiple scales. Through an interactive user interface, the user can effectively segment and visualize a specific feature of interest using a fast, region growing method with this multi-scale data representation. A graph representing the segmented feature is created to facilitate interactive visual inspection and refinement of the feature. We have introduced a new volume data visualization technique based on interactive segmentation rather than the traditional transfer-function based classification approach. This new technique offers the user greater power in isolating and examining volumetric features of interest.
@cite_6: The representation of an image as a ow network has gained an increased interest in research for the 2D and 3D segmentation eld. One of these segmentation approaches consists in applying a minimum cut algorithm to separate the image in background and foreground. The most remarkable algorithm to segment a 2D image using this approach is GrabCut. This article presents a novel segmentation of 3D image using GrabCut implemented on the GPU. We proposed a scheme where a volume dataset is used as input, instead of a 2D image. The original GrabCut algorithm is adapted to be executed on the GPU eciently. Our algorithm is fully parallel and is optimized to run on Nvidia CUDA. Tests performed showed excellent results with dierent volumes, reducing the computation time and maintaining a correct separation background foreground.
@cite_4: Although direct volume rendering is established as a powerful tool for the visualization of volumetric data, efficient and reliable feature detection is still an open topic. Usually, a tradeoff between fast but imprecise classification schemes and accurate but time-consuming segmentation techniques has to be made. Furthermore, the issue of uncertainty introduced with the feature detection process is completely neglected by the majority of existing approaches.In this paper we propose a guided probabilistic volume segmentation approach that focuses on the minimization of uncertainty. In an iterative process, our system continuously assesses uncertainty of a random walker-based segmentation in order to detect regions with high ambiguity, to which the user's attention is directed to support the correction of potential misclassifications. This reduces the risk of critical segmentation errors and ensures that information about the segmentation's reliability is conveyed to the user in a dependable way. In order to improve the efficiency of the segmentation process, our technique does not only take into account the volume data to be segmented, but also enables the user to incorporate classification information. An interactive workflow has been achieved by implementing the presented system on the GPU using the OpenCL API. Our results obtained for several medical data sets of different modalities, including brain MRI and abdominal CT, demonstrate the reliability and efficiency of our approach.
We can also explore the specific techniques that already exist for segmentation and feature extraction in field-based datasets. One popular set of techniques involve region growing. @cite_13 used an interactive region growing method coupled with various morphological operations in order to extract multi-scale features from volume data. Additionally, Pra @cite_4 utilized a uncertainty based region growing technique to minimize extraction errors that may falsely mask structures of interest or include unwanted background fluctuations. Furthermore, Ram 'i @cite_6 took the GrabCut algorithm @cite_22 , an image segmentation technique that treats pixels as a flow network, and extended it to 3D volumes.