Automatic Segmentation of Neoplastic Hepatic Disease Symptoms in CT Images

摘要

In this paper will be described a new method of automatic segmentation of inflammation and neoplastic hepatic disease symptoms, visible in computed-tomography (CT) images. The liver structure will be at first extracted from the image using the approximate contour model. Then, the appropriate histogram-based transformations will be proposed to enhance neoplastic focal changes in CT images. For segmentation stage of cancerous symptoms, the analyzed images will be processed using binary morphological filtration with the application of a parameterized mean defining the distribution of pixel gray-levels in the image. Then, the edges of neoplastic lesions situated inside the liver contour are localized. To assess the efficiency of the proposed processing procedures, experiments have been carried out for two types of tumours: hae-mangiomas and hepatomas. The experiments were conducted on 60 cases of various patients. In this set 30 images showed single and multiple focal hepatic neoplastic lesions, and the remaining 30 images show the healthy organ. Experimental results confirmed that the proposed method is an efficient tool which may be used in the diagnostic support procedures for normal and abnormal liver. The efficiency of proposed algorithm reach the level of over 83% of correct recognition of pathological changes.