Automatic Segmentation of Single and Multiple Neoplastic Hepatic Lesions in CT Images
This paper describes an automatic method for segmenting single and multiple neoplastic hepatic lesions in computed-tomography (CT) images. The structure of the liver is first segmented using the approximate contour model. Then, the appropriate histogram transformations are performed to enhance neoplastic focal lesions in CT images. To segment neoplastic lesions, images are processed using binary morphological filtration operators with the application of a parameterized mean defining the distribution of gray-levels of pixels in the image. Then, the edges of neoplastic lesions situated inside the liver contour are localized. To assess the suitability of the suggested method, experiments have been carried out for two types of tumors: hemangiomas and hepatomas. The experiments were conducted on 60 cases of various patients. Thirty CT images showed single and multiple focal hepatic neoplastic lesions, and the remaining 30 images contained no disease symptoms. Experimental results confirmed that the method is a useful tool supporting image diagnosis of the normal and abnormal liver. The proposed algorithm is 78.3% accurate.
KeywordsCompute Tomography Image Neoplastic Lesion Automatic Segmentation Gaussian Smoothing Liver Structure
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