Share Email Print

Proceedings Paper

Feature extraction and wall motion classification of 2D stress echocardiography with support vector machines
Author(s): Kiryl Chykeyuk; David A. Clifton; J. Alison Noble
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Stress echocardiography is a common clinical procedure for diagnosing heart disease. Clinically, diagnosis of the heart wall motion depends mostly on visual assessment, which is highly subjective and operator-dependent. Introduction of automated methods for heart function assessment have the potential to minimise the variance in operator assessment. Automated wall motion analysis consists of two main steps: (i) segmentation of heart wall borders, and (ii) classification of heart function as either "normal" or "abnormal" based on the segmentation. This paper considers automated classification of rest and stress echocardiography. Most previous approaches to the classification of heart function have considered rest or stress data separately, and have only considered using features extracted from the two main frames (corresponding to the end-of-diastole and end-of-systole). One previous attempt [1] has been made to combine information from rest and stress sequences utilising a Hidden Markov Model (HMM), which has proven to be the best performing approach to date. Here, we propose a novel alternative feature selection approach using combined information from rest and stress sequences for motion classification of stress echocardiography, utilising a Support Vector Machines (SVM) classifier. We describe how the proposed SVM-based method overcomes difficulties that occur with HMM classification. Overall accuracy with the new method for global wall motion classification using datasets from 173 patients is 92.47%, and the accuracy of local wall motion classification is 87.20%, showing that the proposed method outperforms the current state-of-the-art HMM-based approach (for which global and local classification accuracy is 82.15% and 78.33%, respectively).

Paper Details

Date Published: 4 March 2011
PDF: 7 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630H (4 March 2011); doi: 10.1117/12.878302
Show Author Affiliations
Kiryl Chykeyuk, BioMedIA Lab., Univ. of Oxford (United Kingdom)
David A. Clifton, BioMedIA Lab., Univ. of Oxford (United Kingdom)
J. Alison Noble, BioMedIA Lab., Univ. of Oxford (United Kingdom)

Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

© SPIE. Terms of Use
Back to Top