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This contribution presents a method for automatic detection of excitatory, asymmetric synapses and segmentation of synaptic junctional complexes in stacks of serial electron microscopy images with nearly isotropic resolution. The method uses a Random Forest classifier in the space of generic image features, computed directly in the 3D neighborhoods of each pixel, and an additional step of interactive...
In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function,...
Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific...
Karyotype analysis is a widespread procedure in cytogenetics to assess the possible presence of genetics defects. The procedure is lengthy and repetitive, so that an automatic analysis would greatly help the cytogeneticist routine work. Still, automatic segmentation and full disentangling of chromosomes are open issues. We propose an automatic procedure to obtain the separated chromosomes, which are...
A tool for diagnosis assistance by automatic segmentation of microscopic cellular images is introduced. This method is based on an automatic segmentation technique combining (with the Dempster-Shafer rule) the results obtained by support vector machines (SVM) applied within different color spaces. This combination is performed by integrating uncertainties and redundancies for each color space. Those...
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