Zebrafish Retinal Cell Image Analysis

Counting and classification of zebrafish retinal cells are important gist in the inspection of cell mutation, have important biological significance. Previous counting and classification of zebrafish retinal cells are mainly based on manual operation. Because of heavy workload, automatic methods should be developed to finish above task. In this thesis, in the view of image analysis of zebrafish retinal cell, following issues are researched: image segmentation, cell counting,feature extraction and selection,and cell classification.For image segmentation,gradient- flow-tracking based method is adopted, which is composed of three key steps: gradient vector diffusion,gradient flow tracking and adaptive thresholding. After gradient vector diffusion and gradient flow tracking, the cell image is separated into some small regions, each region containing one cell and its background. Local adaptive thresholding is performed for each small region, the single cell can be extracted from the background. Above method favorably overcomes the shortcoming of over-segmentation and under-segmentation by other algorithms for the segmentation of touching cells On the basis of above cell image segmentation, boundary tracking based algorithm is used for cell counting, which refers to image labeling algorithm and 8-connected boundary tracking.Finally, cell features and classification rules are studied, color density features and morphological features of zebrafish retinal cells are extracted. Then the problems of feature selection and the design of classifier are studied. The least distance classifier is used to classify the zebrafish retinal cells.