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Applied Computational Intelligence and Soft Computing
Volume 2017 (2017), Article ID 3048181, 9 pages
https://doi.org/10.1155/2017/3048181
Research Article

Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images

Department of Biomedical Engineering, Near East University, Near East Boulevard, 99138 Nicosia, Northern Cyprus, Mersin 10, Turkey

Correspondence should be addressed to Abdulkader Helwan

Received 9 March 2017; Revised 12 April 2017; Accepted 30 April 2017; Published 4 June 2017

Academic Editor: Mourad Zaied

Copyright © 2017 Abdulkader Helwan and Dilber Uzun Ozsahin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing the object of interest to be detected. Generally, the different backgrounds in which the objects of interest are contained significantly dwindle the performance of vision systems. In this work, we design a sliding windows machine learning system for the recognition and detection of left ventricles in MR cardiac images. We leverage on the capability of artificial neural networks to cope with some of the inevitable scene constraints encountered in medical objects detection tasks. We train a backpropagation neural network on samples of left and nonleft ventricles. We reformulate the left ventricles detection task as a machine learning problem and employ an intelligent system (backpropagation neural network) to achieve the detection task. We treat the left ventricle detection problem as binary classification tasks by assigning collected left ventricle samples as one class, and random (nonleft ventricles) objects are the other class. The trained backpropagation neural network is validated to possess a good generalization power by simulating it with a test set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained backpropagation neural network is used to determine if the sampled region in a target image contains a left ventricle or not. Lastly, we show the effectiveness of the proposed system by comparing the manual detection of left ventricles drawn by medical experts and the automatic detection by the trained network.