Automatic liver localization based on classification random forest with KNN for prediction

Abstract

Robust localization of liver in 3D-CT images is a prerequisite for automatic liver segmentation. Accurate, robust liver localization is challenging due to the variation in appearance and shape, and the ambiguous boundaries between the liver and its neighbor organs. A fully automatic approach was proposed: in the first stage, the interface between the thoracic cavity and the abdomen was detected with a differential model, and the relative structural prior of liver region was derived; in the second stage, random forest is constructed, each testing sample was predicted with a k nearest neighbor (KNN) model based on the relative structural in the same leaf node of the random forest. Experiment results showed that the proposed method obtained comparable or better performance in liver localization.