Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems

Convolutional Neural Network for Denoising Left Ventricle Magnetic Resonance Images

Author(s): Zakarya Farea Shaaf, Muhammad Mahadi Abdul Jamil*, Radzi Ambar and Mohd Helmy Abd Wahab

Pp: 1-14 (14)

DOI: 10.2174/9781681089553122010004

* (Excluding Mailing and Handling)


 Medical image processing is critical in disease detection and prediction. For example, they locate lesions and measure an organ's morphological structures. Currently, cardiac magnetic resonance imaging (CMRI) plays an essential role in cardiac motion tracking and analyzing regional and global heart functions with high accuracy and reproducibility. Cardiac MRI datasets are images taken during the heart's cardiac cycles. These datasets require expert labeling to accurately recognize features and train neural networks to predict cardiac disease. Any erroneous prediction caused by image impairment will impact patients' diagnostic decisions. As a result, image preprocessing is used, including enhancement tools such as filtering and denoising. This paper introduces a denoising algorithm that uses a convolution neural network (CNN) to delineate left ventricle (LV) contours (endocardium and epicardium borders) from MRI images. With only a small amount of training data from the EMIDEC database, this network performs well for MRI image denoising.

Keywords: Deep learning, Image denoising, Image processing, Left ventricle, Neural network.

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