Liver Tumor Detection by Automated Thresholding and Image Segmentation

Abstract Cancer is the most prevalent disease, with higher death factor in human beings. The irregular growth of cells leads to liver cancer or hepatic cancer, where, Hepatocellular Carcinoma (HCC) is the affected liver cancer to 75% of cases. Due to risk factors, it is difficult to be diagnosing the liver cancer at the earlier stage. Since, manual segmentation of tumor from Computed Tomography (CT) image is associated with time consuming and the task requires expert radiologist. To overcome this problem we need an automatic segmentation and analysis process, to identify the prevalence of liver cancer in the early phase itself. This technique consists of various image processing techniques to detect, segment and analyse the tumor in the liver automatically. The clustering algorithm is applied on liver CT image for effective tumor detection. In order to segment the region of tumor, distance transform and watershed transform is used. Then, the intensity of the tumor can be measured by the process which involves edge detection, adaptive thresholding, hole – filling and background subtraction. This works aims to improve accurate analysis of tumor in the liver.