Combining Machine Learning Techniques with Statistical Shape Models in Medical Image Segmentation

Abstract

In this thesis, we implemented Point Distribution Model and basic Active Shape Model algorithm and contributed this to the AUST Computer Vision and Machine Learning code library. We applied the Active Shape Model to segmenting lateral ventricles of 2D brain images and used machine learning – specifically K-Nearest Neighbour algorithm- to improve segmentation results. 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 Haar filters. 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 lateral ventricles of the brain.