Real-time medical images enhancement technique using Adaptive Histogram Equalization model.

In low-contrast modalities like magnetic resonance imaging, medical image augmentation helps reveal anatomical structures and diseased locations. This paper presents an adaptive histogram-based contrast enhancement technique to improve medical picture quality while conserving structure. The suggested approach enhances tumor areas and tissue borders without artifacts using statistical normalization, adaptive histogram modeling, Gaussian-based intensity redistribution, and brightness-preserving contrast modification. T1, T2, FLAIR, and contrast-enhanced T1 MRI scans from the BraTS 2020 Brain Tumor Segmentation Dataset were used in experiments. The proposed method was compared to five state-of-the-art contrast enhancement methods: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gamma Correction (GC), Retinex-based enhancement, and Adaptive Gamma Correction. Quantitative metrics like PSNR, MSE, SSIM, and entropy were used to evaluate the method’s performance. The suggested technique outperforms HE (31.46 dB), CLAHE (34.15 dB), Gamma Correction (32.74 dB), Retinex (35.21 dB), and Adaptive Gamma Correction (36.04 dB) in experimental PSNR (37.82 dB). The suggested technique has an SSIM of 0.962, compared to 0.891, 0.915, 0.902, 0.934, and 0.947 for the competing approaches. The approach decreases reconstruction error with an MSE of 18.4, lower than HE (42.7) and CLAHE (29.6). Qualitative analysis shows better contrast and tumor borders without brightness amplification. These findings show that the adaptive histogram approach enhances medical image analysis and diagnostic assistance systems robustly and efficiently.