Abstract
In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population.

Abstract
Cell tracking is a fundamental problem for studying live cell dynamics. A novel approach for low frame rate cell tracking in fluorescent microscopy image is herein proposed. The method is based on sliding band filter detection and Delaunay triangulation sub-graph matching. With this approach we can track a large amount of small cells without their motion model and not relying on cell motion continuity between consecutive images. The effectiveness and robustness of the method were validated by visual inspection and on a ground truth dataset.

Abstract
In vivo observation and tracking of cell division in the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of cells. The possibility of automating the process of cell detection/marking is an important step to provide research tools to the biologists in order to ease the search for a special event as cell division. This paper discusses an automatic cell segmentation method, which selects the best cell candidates from a starting watershed based image segmentation. The selection of individual cells is obtained using a Support Vector Machine (SVM) classifier, based on the shape and edge strength of the cells' contour. The resulting segmentation is largely pruned of badly segmented cells, which can reduce the false positive detection of cell division. This is a good result on its own and a starting point for improvement of cell segmentation methodology.

Abstract
Plant development is orchestrated by transcription factors whose expression has become observable in living plants through the use of fluorescence microscopy. However, the exact quantification of expression levels is still not solved and most analysis is only performed through visual inspection. With the objective of automating the quantification of cell nuclei fluorescence we present a new approach to detect cell nuclei in 3D fluorescence confocal microscopy, based on the use of the sliding band convergence filter (SBF). The SBF filter detects cell nuclei and estimate their shape with high accuracy in each 2D image plane. For 3D detection, individual 2D shapes are joined into 3D estimates and then corrected based on the analysis of the fluorescence profile. The final nuclei detection's precision/recall are of 0.779/0.803 respectively, and the average Dice's coefficient of 0.773. © 2010 IEEE.

Abstract
A 3-D data acquisition and scene segmentation system is described. For 3-D data acquisition a structured light technique based on the ratio of two intensity images is used. This technique avoids the correspondence problem, that appears in other structured light techniques, and allows the acquisition of dense range images. The calibration steps of the system are described. Measurement results with different scenes are presented and system accuracy is evaluated. Taking into account that this system allows the acquisition of a dense range image and an intensity image, registered with it, a new approach to the segmentation of 3-D scenes using both range and intensity information is proposed. The two images are segmented separately and iteratively. At each iteration, the segmentation results are combined. The segmentation proceeds until all segments are planar or no more splits are possible. A two-step merging procedure follows: first, planar surfaces that may have been split are "reconstructed", by merging adjacent planar regions that satisfy some constraints; then, regions belonging to curved surfaces are identified and merged, using a curvature analysis along line segments delimited by edge points, combined from both images. Finally, the resulting surfaces are described, using adequate functions. © 1996 IEEE.

Abstract
In this paper a method for automatic detection of microaneurysms in digital angiograms of the eye fundus is described. These lesions of the human retina, a characteristic of the earliest phases of diabetic retinopathy, present themselves in the angiographic images as small, round, hyperfluorescent objects. The proposed method includes initial pre-processing and enhancement steps, followed by object segmentation. In the final phase, microaneurysms are validated using two new criteria based on local intensity, contrast and shape relations. The combination of these local features with global image parameters makes possible a high degree of independence from image intensity characteristics. © 1999 IEEE.