Spatial mutual information based detail preserving magnetic resonance image enhancement.

Medical imaging is a widespread method of envisioning the inside of the human anatomy without causing harm. In magnetic resonance imaging (MRI), poor contrast images may not support adequate information for visual reading of affected areas. As a result, image enhancement technique is essential to enhance image views and keep the image processing approach computationally low. Because of the anatomical complexity of the brain, low contrast is a challenging aspect to deal with in MRI imaging. The issue of conserving structural features while maintaining brightness is also a significant consideration. The histogram equalization (HE) based technique is frequently applied to enhance contrast in brain MRI images. A unique enhancing approach is presented to increase the brightness and contrast of the MRI picture. Spatial mutual information-based algorithm analyses a clinically gathered dataset of MRI images, producing good results. The proposed approach tested both healthy and unhealthy brain MRI pictures. Contrast and brightness improvement are the two divisions of the suggested technique. Adaptive gamma correction using weighted distribution method is applied on the value channel (V) in HSV color model. It provides the brightness gain matrix, which enhances the image brightness. Spatial mutual information methods act on the luminosity space (L) of the CIE 1976 L*a*b* color space for contrast enhancement. Finally, an efficacious brightness and contrast modification strategy for MRI images is provided, with its performance compared to several state-of-the-art approaches using a well-known performance measure.