An Exper imental Compar ison and Quantitative Analysis on Conventional Stain Nor malization for Histopathology Images
Rubina Akter Rabeya, Subrata Bhattacharjee, Dongmin Kim, Hee‐Cheol Kim, Nam-Hoon Cho, Heung‐Kook Choi · Journal of Korea Multimedia Society · 2024
Today, the appropriate utilization of colors in histopathology tissue images is a major research area in the fields of medical imaging and digital pathology, particularly when cancerous tissues are examined.BR/This research intends to concentrate on experiments and analyzes the different color/stain normalization methods including histogram equalization, Reinhard, Macenko, Zheng, Gray world, and Vahadane that may impact and enhance the accuracy of computer-aided cancer diagnosis. The study additionally experi- ments with several pre- and post-processing steps with normalization approaches that aid the enhance- ment of image quality. In this study, 300 cancerous histopathology tissue images (256 × 256 pixels) were selected from different datasets on prostate, breast, and colon cancer. To allow quantitative comparison of the various approaches, several statistical measures were computed, including the Bhattacharyya dis- tance, mean square error (MSE), structural similarity index metric (SSIM), standard deviation (SD) and coefficient of variation (CV) of the normalized median intensity (NMI), and the weighted sum. The Reinhard method was the most effective for all three datasets in combination with bilateral filter, CLAHE, and gamma correction. The ultimate objective is to find out the best-fitted normalization method and suitable pre-and post-processing methods for three types of cancer datasets. Our comparative research study will greatly aid the future development of new strategies in the fields of digital pathology and medical imaging.