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

The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing theobject 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 back propagation neural network on samples of left and non left ventricles. We reformulate the left ventricles detection task as a machine learning problem and employan intelligent system (back propagation 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 back propagation neural network is validated to possess a good generalization power by simulating it with atest set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained back propagation 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.