A generalized active shape model for segmentation of liver in low-contrast CT volumes
In this paper, an approach for improving active shape segmentation of medical images using machine learning techniques which can achieve high segmentation accuracy is described. A statistical shape model is created from a training dataset which is used to search for an object of interest in an image. Active shape model has shown over time to be a reliable image segmentation methodology, but its segmentation accuracy is hindered especially by poor initialization which can't be guaranteed to always be perfect. In our methodology, we extract features for each landmark using haarfilters. We train a classifier with these features and use the classifier to classify points around the final points of an Active shape model search. The aim of this approach is to better place points that might have been wrongly placed from the ASM search. We have used the simple, yet effective K-Nearest Neighbour machine learning algorithm, and have demonstrated the ability of this method to improve segmentation accuracy by segmenting 2d images of the lateral ventricles of the brain.