A neural network learned information measures for heart motion abnormality detection

M. S. Nambakhsh; Kumaradevan Punithakumar; Ismail Ben Ayed; Aashish Goela; Ali Islam; Terry Peters; Shuo Li

doi:10.1117/12.878028

11 March 2011 A neural network learned information measures for heart motion abnormality detection

M. S. Nambakhsh, Kumaradevan Punithakumar, Ismail Ben Ayed, Aashish Goela, Ali Islam, Terry Peters, Shuo Li

Author Affiliations +

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79621H (2011) https://doi.org/10.1117/12.878028
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

In this study, we propose an information theoretic neural network for normal/abnormal left ventricular motion classification which outperforms significantly other recent methods in the literature. The proposed framework consists of a supervised 3-layer artificial neural network (ANN) which uses hyperbolic tangent sigmoid and linear transfer functions for hidden and output layers, respectively. The ANN is fed by information theoretic measures of left ventricular wall motion such as Shannon's differential entropy (SDE), Rényi entropy and Fisher information, which measure global information of subjects distribution. Using 395×20 segmented LV cavities of short-axis magnetic resonance images (MRI) acquired from 48 subjects, the experimental results show that the proposed method outperforms Support Vector Machine (SVM) and thresholding based information theoretic classifiers. It yields a specificity equal to 90%, a sensitivity of 91%, and a remarkable Area Under Curve (AUC) for Receiver Operating Characteristic (ROC), equal to 93.2%.

Citation Download Citation

M. S. Nambakhsh, Kumaradevan Punithakumar, Ismail Ben Ayed, Aashish Goela, Ali Islam, Terry Peters, and Shuo Li "A neural network learned information measures for heart motion abnormality detection", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621H (11 March 2011); https://doi.org/10.1117/12.878028

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