Computer-Aided Classification of Cells in Complex Brain Tissue from 5-Channel 3-D Confocal Datasets

Abstract

Abstract Spectral Unmixing and Image Segmentation Cells Classification Experimental Results and Validation R1R2FundamentalScienceFundamentalValidatingTestBEDsTestBEDsL1L1L2L3R3S1 S2 S3 S4 S5 Bio-Med Enviro-Civil The cellular organization of brain tissue is truly complex. This work presents a computational method to identify the principal cell types in three-dimensional (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. Features Extraction SpectralUnmixingNisslChannelIba-1ChannelTraceProcessesGFAPChannelTraceProcessesConvergenceAnalysisEBAChannelSurfaceSegmentationCyQuantChannelNuclearSegmentationInspect &EditSegmentationOutput