Image Processing Techniques and Segmentation Evaluation -doctoral Thesis

This thesis presents contributions in the field of microscopic image analysis, in particular the automatic segmentation of fluorescent images of cell nuclei and colon crypts. The evaluation methodology of the segmentation results is detailed and a new evaluation criterion is presented. The proposed discrepancy method is based on the comparison: machine segmentation vs. ground-truth segmentation. This error measure eliminates the inconveniences that appear in the case of concave objects and allows easy control of the method sensibility regarding the objects shape similarity according to the field in which it is used. An analysis of the most used image processing methods in microscopic image segmentation is presented by considered both the pathological fields: cytology and histology. Segmentation methods are also proposed for both fields: segmentation of the nuclei (used in cytometry) and crypts segmentation (used in hystometry). Since the critical problem in microscopic images from tissues with colon carcinoma is the touching nuclei, three techniques are proposed to find the boundaries of touching/clustered nuclei. Since all methods need accurate background delineation, two approaches are proposed for this matter. The segmentation problem of specific chained configurations is solved using the points with high concavity and a set of templates and rules to validate and to pair these points. The clustered/touching cell nuclei within complex structures are separated using the shape of the section profile or a cross-correlation with a specific template of the separation areas. Regarding the histological structures, two automatic segmentation techniques robustly identify the epithelial layer/crypts. Both proposed methods use hierarchical approaches like morphological hierarchy or anisotropic diffusion pyramid. A useful study of the sampling step and a comparison between the hierarchy (without sampling) and the pyramid (with sampling) is presented. The significant implication of these techniques consists of the coarse-to-fine approach. First the high level information is preferred against the local one to allow an easy detection of the positions for the interest objects. Next, a more detailed analysis of the hierarchical representations is performed in order to obtain an accurate segmentation. The evaluation has been done by comparison against ground-truth segmentations or by visual inspecting by a human expert. The results confirmed that the proposed methods could efficiently solve the segmentation problems of microscopic images. 2 3 Acknowledgments I would like to thank my supervisor, Prof. Vasile Manta, for his guidance, patience, for the valuable advices and for giving me the opportunity to push my limits within this …