Elimination of Specular reflection and Identification of ROI: The First Step in Automated Detection of Cervical Cancer using Digital Colposcopy

TL;DR

This paper presents a novel method for removing specular reflections and identifying the region of interest in digital colposcopy images to improve automated cervical cancer detection.

Contribution

It introduces a new technique for eliminating specular reflections and a modified k-means clustering approach for ROI detection in cervigrams.

Findings

Effective SR removal enhances segmentation accuracy

Modified k-means accurately isolates cervical region

Preprocessing improves subsequent diagnostic algorithms

Abstract

Cervical Cancer is one of the most common forms of cancer in women worldwide. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. During Digital Colposcopy, Specular Reflections (SR) appear as bright spots heavily saturated with white light. These occur due to the presence of moisture on the uneven cervix surface, which act like mirrors reflecting light from the illumination source. Apart from camouflaging the actual features, the SR also affects subsequent segmentation routines and hence must be removed. Our novel technique eliminates the SR and makes the colposcopic images (cervigram) ready for segmentation algorithms. The cervix region occupies about half of the cervigram image. Other parts of the image contain irrelevant information, such as equipment, frames, text and non-cervix tissues. This irrelevant information can confuse automatic identification of the tissues within the cervix. The first step is, therefore, focusing on the cervical borders, so that we have a geometric boundary on the relevant image area. We have proposed a type of modified kmeans clustering algorithm to evaluate the region of interest.