Color Contrast Enhancement on Pap Smear Images Using Statistical Analysis

In the conventional cervix cancer diagnosis, the Pap smear sample images are taken by using a microscope,causing the cells to be hazy and afflicted by unwanted noise. The captured microscopic images of Pap smear may suffer from some defects such as blurring or low contrasts. These problems can hide and obscure the important cervical cell morphologies, leading to the risk of false diagnosis. The quality and contrast of the Pap smear images are the primary keys that could affect the diagnosis' accuracy. The paper's main objective is to propose the best contrast enhancement to eliminate contrast problems in images and correct them in color images to ensure smooth segmentation. In this paper, the median and standard deviation are used for the image's global and local data where the problem region is normalized by using a special proposed formula. The expected resulting image shows only the object (nuclei and cytoplasm), and a background without any noise. The results were compared with CLAHE, HE, and Gray World, and the performance was evaluated based on PSNR, RMSE, and MAE. Proposed method shows higher PSNR and RMSE value while lower value for MAE compared to other methods. This paper's main impact will help doctors in identifying the patient's disease, such as cervical cancer, based on a Pap smear analysis, and increase the accuracy percentages as compared to the conventional method.