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Medical Image Segmentation using an Extended Active Shape Model

by Eustace Ebhotemhen
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 19
Year of Publication: 2013
Authors: Eustace Ebhotemhen
10.5120/12079-8173

Eustace Ebhotemhen . Medical Image Segmentation using an Extended Active Shape Model. International Journal of Computer Applications. 69, 19 ( May 2013), 24-29. DOI=10.5120/12079-8173

@article{ 10.5120/12079-8173,
author = { Eustace Ebhotemhen },
title = { Medical Image Segmentation using an Extended Active Shape Model },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 19 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number19/12079-8173/ },
doi = { 10.5120/12079-8173 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:41.552720+05:30
%A Eustace Ebhotemhen
%T Medical Image Segmentation using an Extended Active Shape Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 19
%P 24-29
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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[9]. 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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Active Shape Model K-Nearest Neighbour lateral Ventricles

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