A Survey on Different Methods for Liver Segmentation

Liver Cancer is one in every of the speediest growing cancer in the world. The early detection and diagnosing of liver tumor growth is vital for the hindrance of liver tumor growth. More than 30% of cancer deaths may be prevented by avoiding risk factor, early detection, accurate diagnosis, and effective treatment. Segmentation of liver from medical images from the abdominal space is vital for diagnosing of tumor and for surgical procedures. Accurate detection of the type of the liver abnormality is very essential for treatment designing which may minimize the fatal results. However accurate results can only be obtained by computer aided automation systems. Many different techniques are developed for the detection of liver cancer the abnormal lesion size and form. This paper reviews various liver tumor detection algorithms and methodologies used for liver tumor diagnosis. A comparative analysis is performed. Also explores the applicability of the techniques in liver segmentation of CT images. KeywordsLevel set Segmentation, Kmeans clustering, liver cancer, CT image.