Preprocessing for Automating Early Detection of Cervical Cancer

TL;DR

This paper presents a novel preprocessing technique for digital colposcopy images that removes specular reflections and irrelevant regions, enhancing the accuracy of automated cervical cancer detection.

Contribution

The proposed method effectively eliminates specular reflections and isolates the cervix region, improving the readiness of images for segmentation in automated diagnosis.

Findings

Reduces specular reflections in cervigram images

Accurately isolates cervical region for analysis

Enhances image quality for segmentation algorithms

Abstract

Uterine 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, colposcopic images or cervigrams are acquired in raw form. They contain specular reflections which appear as bright spots heavily saturated with white light and occur due to the presence of moisture on the uneven cervix surface and. The cervix region occupies about half of the raw 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. Therefore we focus on the cervical borders, so that we have a geometric boundary on the relevant image area. Our novel technique eliminates the SR, identifies the region of interest and makes the cervigram ready for segmentation algorithms.