Improving And Achieving The Brain CT Images' Segmentation Algorithms

Taking CT image of the brain as the researched subject, under the environment of CT workstation of PACS, the author of this thesis studied and improved the LOG and watershed algorithm in the stage of image segmentation, achieved the results of CT image of the brain on skull edge and region.According to the feature and approaches, the thesis studied the principle, characteristics, noise, edge feature, morphological feature of multi-threshold values in CT image of the brain's skull segmentation. The main achivements are as follows:1. According to the feature of edge segmentating on skull, the LOG method is improved, and the binary result can be obtained flexiblely through the improved LOG agorithm, which is controlled by Gaussian function's smoothing factorσ, edge gray threshold value T, and pepper-and-salt filter's size m×n.2. According to the morphological feature of multi-threshold values, the watershed segmentation algorithm is improved by combining denoising, distance transform, gradient reconstruction with constraint conditions, and the segmentation image of brain skull which is obtained by the improved algorithm is good at noise suppression, region information accepting, and over segmentation controlling.3. The segmented images from the improved LOG and watershed algorithm realized with Matlab language can be achieved effectively, and the segmemting effect and algorithm efficiency are analyzed.4. Applied improved LOG and improved watershed algorithm in a group of brain CT slides, and then appraised segmented results, run time, efficiency and complexity and so on.The improved algorithms of this thesis are studied fully and deeply in theory, and can achieve effective segmentation results in application. So, the feasibility and validity of the improved algorithms are verified from theories and experimentations.