Enhanced Power Law Transformation for Histopathology Images of Breast Cancer.

Different image enhancement techniques are applied to improve the visual quality of an image on a display device. Contrast stretching, intensity level slicing with and without background, histogram equalization, logarithmic transformation and power law transformation are some image enhancement techniques. Most of the research work focuses on adaptive gamma correcting factors for better visualization of extremely low contrast images, giving less importance to the constant for enhanced visualization. This research proposes an efficient and less complex enhanced power law transformation (EPLT) approach to improve the contrast of dimmed and extremely bright images. The approach is a quick way to compute the value of C, i.e. constant for enhanced visualization. For better picture quality, it is very important to determine C automatically and the gamma correcting factor. This technique offers a novel and unique perspective on image contrast manipulation. The proposed enhancement technique is experimented on histopathology images of breast cancer, bright images and extremely dark images. The average peak signal-to-noise ratio (PSNR) for clinical data and Break His dataset is high for the proposed method are 16.52487 and 17.69335 respectively. The average RMSE for clinical data and BreakHis dataset is low for the proposed method are 40.88251 and 44.2546 respectively. It is observed that the proposed method yields the most satisfactory contrast enhancements based on performance comparison with other state-of-art enhancement algorithms and works efficiently on all types of images. [ABSTRACT FROM AUTHOR]