Visibility enhancement of brain tumor affected MRI images using adaptive heuristic process

Magnetic resonance imaging (MRI) is a frequently used system in medical imaging and disease interpretation. Most of the time, MRIs in humans show detailed tissue architecture. Low contrast in MRI images is a result of an unfavourable imaging environment. An image's contrast can be increased by employing the straightforward histogram equalization (HE) method. However, HE has numerous disadvantages that make it unsuitable for many applications, such as a significant change in brightness, artificial effects, and over-enhancement. This method suggests a new adaptive heuristic HE technique to address these problems. The image's probability distribution function (PDF) is computed first. The maximum and average values of the PDF are used to create an adaptive parameter in the second step. The adaptive parameter is then limited by adding a threshold to the PDF and cumulative distribution function (CDF). Finally, a novel CDF is attained by employing another adaptive parameter discovered by applying the updated CDF. The enhanced image is obtained by combining the new CDF with conventional HE. The suggested method performs better visually and quantitatively than equated state-of-the-art techniques and is equally effective for low-contrast MRI images. The application of the proposed method was assessed and contrasted using the standard performance measures. After considerable testing, it was discovered that the suggested method effectively increases visual contrast while retaining the original characteristics of the input photographs and avoiding overly or inadequately improved images.