A new medical image enhancement algorithm using adaptive parameters

Abstract

Abstract The quality of medical images plays a vital role in many image processing applications such as image segmentation, feature extraction, image classification, image recognition, and image fusion. Some of the common problems with Medical images are noise, blur, or low contrast. According to our observations, current image enhancement algorithms only focus on solving individual problems such as gray level adjustment, noise reduction, or sharpness enhancement. This paper proposes a novel algorithm to solve problems on images simultaneously. First, we propose an image decomposition algorithm. This algorithm allows decomposing the image into three components: structure ( I S ), texture ( I T ), and noise ( I N ). Second, the structural component ( I S ) is enhanced by the contrast‐limited adaptive histogram equalization method to obtain I CLAHE . We use the structure tensor salient detection operator and the Laplace edge detection operator to add structural and texture features. These operators are applied to the I CLAHE and I T components to obtain the I STS and I LED components, respectively. The I T and I LED components are used to generate the enhanced component (called ) by using the Max operator. Third, the Marine predators algorithm is used to find the optimal parameters β 1 , β 2 , β 3 , and β 4 corresponding to I CLAHE , I STS , , and I N . Finally, the enhanced image is made up of the sum of the I CLAHE , I STS , , and I N images multiplied by the optimal parameters β 1 , β 2 , β 3 , and β 4 , respectively. Six state‐of‐the‐art image enhancement approaches, seven medical image fusion algorithms, and six image quality metrics have been utilized to verify the proposed approach's effectiveness. The experimental results show that the proposed method significantly improves the quality of the input medical images as well as significantly improves the efficiency of current medical image synthesis algorithms.

Topics & Concepts