Experimental Analysis of Four Gamma Correction Variants on Brain Tumor Images

Gamma correction is a technique used in medical imaging it enhances the visual quality of images and improves the differentiation of anatomical structures. Thus it assists clinicians in the accurate diagnosis of medical conditions. İn literature, various gamma correction variants like adaptive gamma correction, gamma correction with weighted distribution, dynamic gamma correction, etc., can be applied to medical images to improve their brightness and contrast. These variations include the widely used power-law or gamma correction and linear gamma correction, which offers straightforward brightness adjustments. The individual imaging requires, the clinical environment, and the trade-off between contrast enhancement and possible noise amplification all impact the choice of gamma correction variant. İn this research, four gamma correction variants are explored and evaluated on medical images. These techniques are Adaptive Gamma Correction Image Enhancement (AGCIE), Adaptive Gamma Correction with color preserving framework (AGCCPF), Adaptive Gamma Correction with Weighted Detail Enhancement (AGCWD), and Dynamic Contrast-Range Gamma Correction (DCRGC). Metrics such as MSE, PSNR, SSIM, and Entropy are used in order to compare the outcomes. AGCWD performs better than other techniques.