Contrast enhancement of MRI images frequently needs considerable pre-processing to provide accurate data for disease diagnosis and proper treatment. Enhancing the appearance of medical images becomes a difficult task owing to the uncertainty of the obtained image quality. In this study, Alzheimer’s MRI images are subjected to a contrast enhancement algorithm for easy diagnosis. A noise reduction and contrast enhancement technique for MRI images is discussed in this research. Histogram-based algorithms are used to solve the problems of de-noising and enhancing the contrast of images for identification of the infected region. The proposed method is based on contrast-limited adaptive histogram equalization (CLAHE) and the comparison with Histogram Equalization (HE). The suggested enhancement technique's performance can be evaluated using several metrics, including Structure Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Observational studies revealed that the suggested approach is significantly more efficient than the basic enhancement techniques such as HE.

Contrast Enhancement Alzheimer’s MRI Contrast-Limited Adaptive Histogram Equalization Histogram Equalization Performance Metrics