Split With The Cancer Cell Identification Method Based On The Image Of Snake

Medical image processing has become an important area in image analysis and biomedical engineering fields due to the rapid development of medical imaging technology. As a hot issue of medical image processing, cell image analysis has attracted widely the attentions of researchers. The quantitative analysis and malignity detection of cell images obtained from smears is usually taken as a difficult problem, because of the diversity of contained tissues, uneven staining, and overlapped cell clusters in the cell images.This thesis focuses on the quantitative cytological analysis and automated cancerous cell recognition technology, aiming at the cell images obtained from esophageal smears. Based on the in depth research on image analysis, pattern recognition theories, and cytological pathology knowledge, we make a systematical and comprehensive study on the technologies of cell segmentation, cytological malignant feature description and cancerous cell recognition. The main contributions of this thesis are summarized as follows:(1) A growing snake based on fuzzy intensity consistency measure is proposed. To solve the initialization problem of the traditional snake, we improve the energy function by adding an adaptive growing energy term defined by the pixel's fuzzy intensity consistency measure. The growing snake has strong anti-noise ability and low computation cost. The experiments show that the proposed snake model has encouraging segmentation results and stable performance.(2) Aiming at the overlapped or blurred nucleus edges, we propose a novel information fusion based growing snake to segment color cell nucleus. Utilizing adequately the prior knowledge of cell images, we firstly perform ellipse fitting on the nucleus and give an estimation on the edge superposition status. Based on the detected ellipse and tristimulus distribution characteristics of different regions, we define several fuzzy measurements to describe the degrees of the pixel belonging to the nucleus geometrically and tinctorally. At last, we fuse these fuzzy measurements with different methods and build a new growing snake to segment the nucleus. The ellipse information enhances the boundary tracking ability for the overlapped or blurred edges. The fusion of various information improves the segmentation accuracy and performance stability.(3) Several feature description methods are proposed to analysis the malignant characteristics of nucleus. To effectively analysis the granularity of nucleus chromomere, we proposed a morphological granularity analysis method, called MSGF method. The MSGF method constructs a 2D granularity distribution graph for the bi-level image and performs granularity element decomposition on it. The size distribution and topological feature parameters of the granularity elements are used to replace the connective region parameters in the traditional SGF texture description method. In additional, we use the curvature entropy to measure the...