Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

Abstract

Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Renyi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Renyi entropy, and MRI Kidney deep segmentation. The proposed enhancement model exploits the pixel's probability representations for image enhancement. Since fractional Renyi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels, yielding an overall better details of the kidney MRI scans. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. The experimental results showed an average of 95.60% dice similarity index coefficient, which indicates best overlap between the segmented bodies with the ground truth. It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.