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

Abdulkader Helwan,Dilber Uzun Ozsahin

doi:10.1155/2017/3048181

Abdulkader Helwan, Dilber Uzun Ozsahin

Open Access

https://doi.org/10.1155/2017/3048181

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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.

Highlights

Machine learning (ML) is a form of artificial intelligence (AI) which gives computers the skills to learn without being programmed
Since artificial neural network weights are usually randomly initialized at the start of training, it follows that trained backpropagation neural network (BPNN) is not always guaranteed to converge to the global minimum or good local minima
When the MATLAB script for the developed whole detection system is run, it is possible that the BPNN may be automatically retrained a couple of times before the detection task is executed

Summary

Introduction

Machine learning (ML) is a form of artificial intelligence (AI) which gives computers the skills to learn without being programmed It focuses on building computer programs which are subject to change when exposed to new data. Despite machine learning being virtually new, its concept has been applied to medical imaging for years, in areas of computer-aided diagnosis (CAD) and functional brain mapping [6]. Components of medical imaging (image analysis and reconstruction) tend to benefit from the merger of machine learning with medical imaging. From this perspective, new methods for image reconstruction and exceptional performance in both clinical and preclinical applications will be achieved [6]. Other applications of machine learning in medical imaging include but are not limited to tumour classification, tumour diagnosis, image segmentation, image reconstruction, and prediction [3, 6, 7]

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