Enhancing Background Luminance for Colorectal Cancer H and E Stained Images using Modified Reinhard Technique

With the advent of AI and Machine learning based learning, the overall process of cancer diagnosis became much smoother and faster through automated techniques. Because of the presence of artefacts that cause color changes in H&E stained histopathology images, color normalization is an important pre-processing step for cancer identification. However, the existing color normalization methods suffers from two major issues: Loss of information that leads to poor background luminance and huge computational complexity. To address this issue, we developed a modified Reinhard approach for color normalizing on the CRC dataset in order to improve the background luminance of H&E stained colorectal cancer histopathology photographs. Our proposed algorithm not only mitigate the limitations of the previous reinhard method but statistically satisfy all four hypothesis of the color normalization by incorporating a global feature along with local one. Our algorithm's performance was also compared to that of other current color normalization algorithms, and it was shown to be superior in both quantitative and qualitative terms.