Level Set Based Liver Segmentation and Classification by SVM

摘要

Liver segmentation from CT image is the key exploration works in representing a liver, which has incredible effect on the examination of liver issue. Hence, numerous computer-aided segmentation approaches have been proposed to partition liver locale from medical image automatically in the past numerous years. A method for liver segmentation system is proposed by consolidating level set based method with Pseudo Zenerike moment and GLDM Features. The objective of proposed algorithm is to solve the segmentation issue which is created by indistinguishable intensities between liver region and its adjacent tissues. Radial Basis Function SVM is used in this work to classify the type of the tumor.