Assessment of regional myocardial function via statistical features in MR images
Assessment of regional myocardial function via statistical features in MR images
Afshin, Mariam and Ben Ayed, Ismail and Punithakumar, Kumaradevan and Law, Max W.K. and Islam, Ali and Goela, Aashish and Ross, Ian and Peters, Terry and Li, Shuo
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011
Abstract : Early and accurate detection of Left Ventricle (LV) regional wall motion abnormalities significantly helps in the diagnosis and follow-up of cardiovascular diseases. We present a regional myocardial abnormality detection framework based on image statistics. The proposed framework requires a minimal user interaction, only to specify initial delineation and anatomical landmarks on the first frame. Then, approximations of regional myocardial segments in subsequent frames were systematically obtained by superimposing the initial delineation on the rest of the frames. The proposed method exploits the Bhattacharyya coefficient to measure the similarity between the image distribution within each segment approximation and the distribution of the corresponding user-provided segment. Linear Discriminate Analysis (LDA) is applied to find the optimal direction along which the projected features are the most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed method can be used as a promising diagnostic support tool to assist clinicians. © 2011 Springer-Verlag.
