Texture Enhancement of Medical Images for Efficient Disease Diagnosis with Optimized Fractional Derivative Masks

For the past two decades, fractional-order derivatives have been used to model many systems in science and engineering with more accuracy than existing integer-order derivatives. Many of these applications have been employed in the image processing field. It is undeniable that an image enhancement algorithm is very much desirable for medical image analysis to diagnose various kinds of diseases more efficiently. These requirements demand that the image should be of high quality. Hence, accurate edge-detection and denoising models are required in medical image processing, improving, and enhancing the contrast of an image to attain a better texture and avoid noise. In this study, we employ and compare the conventional methods and recent and most popular fractional-order-based methods for medical image analysis texture enhancement. To make a fair comparison, the fractional-order operators are optimized for all images with gray wolf optimizer while considering the performance metric mean squared error. The results showed that fractional differential-based operators perform better than conventional integer-order operators for texture enhancement of medical images.