| Medical image segmentation technology is one of the important subjects within medical image processing and analysis research field. The main purpose of medical image segmentation is to divide the image into different regions with special signification, and extract the correlative properties at the same, which can provide the credibility gist for clinic diagnose and pathology research. Besides the common properties of the image segmentation, as the complexity of human anatomic structure, the abnormity of the tissue shape and the difference among individuals, the common image segmentation methods are not fit for the medical images. Hence, to seek effective medical image segmentation method is all through the hotspot in medical image processing.The task of the image segmentation is to divid objects in an image that are touching each other into separate objects as homogeneous areas. The local characteristics of the pixels in a homogeneous area is similar to each other. Hence, it is significative to evalue the neigbourhoods of the pixels. In this thesis the segmentation methods by means of evaluing the neigbourhoods of the pixels are discussed. Differing from the existing"neigbourhoods", it concerns the spacial distribution of the neigbours in this neigbourhoods. By employing the neigbourhoods features proposed, the dissertation discusses the key algorithms and relative issues of medical image segmentation, involving image denoising, enhancement and segmentation. Its main contents include:(1) Medical images enhancement based on fuzzy logicA medical images enhancement algorithm based on fuzzy logic is proposed in order to improve the low contrast and blurring of the image. The nonlinear operator is applied to normalization operation to enlarge the contrast in boundary regions, followed by exponential transformation. The neighborhoods are adopted to control the enhancement in order to enhance the contrast between the regions and preserve the texture in homogenous regions. For multi-level greyscale images, they are partitioned into several fuzzy sets according to their statistical properties. The multilevel enhancement is implemented by combining enhancement on each fuzzy set. Comparing the classical methods, the results obtained using the proposed method are shown to have higher contrast, thereby better representing the anatomical structures of interrogated tissue. (2) Medical gray images segmentationPCNN model is robust to noise, but the performance of the classical pcnn is sensitive to the parameters. Unsuitable parameters will lead to deficient- segmentation or over-segmentation. A neighborhood inspiring PCNN is proposed. In the proposed model, if a neuron is captured or not depends on its intensity and neibourhood, which consists two aspects: the intensity of its neibourhood neurons and the distribution of the neurons whose intensity is higher than the threshold. The neibouthood of the neuron is modeled to control the internal activity and determine if it pulses or not. The experimental results shows that the performance of new model is less sensitive to the selection of parameters.(3) Medical color image segmentationA fast segmentation method is presented based on the analysis of the color Doplor ultrasound image to deal with the images with large background. The pending area is reduced by setting region of interest (ROI), consequently, the processing is accelerated markedly. The proposed algorithm relies on an introducedε-neighbor coherence segmentation criterion which is easy to interpret and implement. The pixels are divided into several equivalence classes according their neighbourhoods measured by difference of the color, then the equivalence classes grow into homogeneity classes by merging the outer-neighboring pixels which areε-similar. Each homogeneity class is processed as a region. The segmentation is completed by color clustering. Moreover, the method has a computational complexity nearly linear in the number of image pixels in ROI, and is wieldy for realtime application.
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