Abstract

The cellular organization of brain tissue is truly complex. This work presents a computational method to identify the principal cell types in threedimensional (3-D) confocal image stacks with multiple fluorescent channels. The cells are classified into four major classes (Neurons, Microglia, Astrocytes and Endothelials) by using a two-step classifier that applies fuzzy c-means clustering followed by Support Vector Machines (SVM). The resulting classification results were validated against a human expert, and the accuracy of the classifier was %95.5 in the correctly segmented nuclei.

Notes

Poster presented at the 2007 Thrust R2D Image Understanding and Sensor Fusion Methods Conference

Keywords

Computer-Aided Classification, brain tissue, SVM

Subject Categories

Three-dimensional imaging, Brain mapping

Disciplines

Biomedical Engineering and Bioengineering

Publisher

Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)

Publication Date

2007

Rights Holder

Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)



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