Contrast Enhancement of Medical Images Using Otsu’s Double Threshold

This research tackles challenges in contrast enhancement within medical imaging, addressing issues like over-enhancement, entropy loss, mean shift, and suboptimal contrast due to improper thresholds. Extracting micro-level diagnostic details from medical photos is hindered by these concerns. The study explores new histogram equalization techniques, assessing their advantages, limitations, and applications. The authors propose a robust design framework, integrating Otsu’s double threshold, range optimization, weighted distribution, adaptive gamma correction, and homomorphic filtering. Experimental results demonstrate efficacy in maximizing entropy, preserving brightness, and enhancing contrast in medical magnetic resonance images. The refined system ensures visually appealing images crucial for disease diagnosis precision. This research contributes by addressing why existing systems fall short, and how an improved framework can successfully overcome these challenges.