images generated will be harder to analyse than images acquired by an operator as they would have chosen other locations to image and focus correctly. On the other hand, the operator might introduce an unconscious bias in his selection by choosing only the cells whose phenotype is most like the one expected before the experiment. Histology Histology is a microscopy application where tissue slices are stained and observed under the microscope (typically light microscope, but electron microscopy is also used). When using a light microscope, unlike the case of fluorescent imaging, images are typically acquired using standard color camera-systems. This reflects partially the history of the field, where humans were often interpreting the images, but also the fact that the sample can be illuminated with white light and all light collected rather than having to excite fluorophores. When more than one dye is used, a necessary preprocessing step is to unmix the channels and recover an estimate of the pure dye-specific intensities. It has been shown that the subcellular location of stained proteins can be identified from histology images. If the goal is a medical diagnostic, then histology applications will often fall into the realm of digital pathology or automated tissue image analysis, which are sister fields of bioimage informatics. The same computational techniques are often applicable, but the goals are medically- rather than research-oriented. Important Problems Subcellular Location Analysis Subcellular location analysis was one of the initial problems in this field. In its supervised mode, the problem is to learn a classifier that can recognize images from the major cell organelles based on images. Methods used are based on machine learning, building a discriminative classifier based on numeric features computed from the image. Features are either generic features from computer vision, such as Haralick texture features or features specially designed to capture biological factors (e.g., co-localization with a nuclear marker being a typical example). For the basic problem of identifying organelles, very high accuracy values can be obtained, including better than ? results. These methods are useful in basic cell biology research, but have also been applied to the discovery of proteins whose location changes in cancer cells. However, classification into organelles is a limited form of the problem as many proteins will localize to multiple locations simultaneously (mixed patterns) and many patterns can be distinguished even though they are not different membrane-bound components. There are several unsolved problems