Automatic detection of specular reflections in uterine cervix images

Specular reflections strongly affect the appearance of images, and usually hinder the computer vision algorithms applied to them. This is particularly the case with uterine cervix images. The highlights created by specular reflections are a major obstacle in the way of automatic segmentation of such images. We propose a method for the detection of specularities in cervix images that utilizes intensity, saturation and gradient information. A two-stage segmentation process is proposed for the identification of highlights. First, coarse regions that contain the reflections are defined. Second, probabilistic modeling and segmentation is used to achieve a precise segmentation inside the coarse regions. The resulting regions are filled by propagating the surrounding color information. The efficiency of the method for cervix images is demonstrated.