Classification of colposcopic cervigrams using EMD in R

Abstract

Cervical cancer is one of the most common cancer among women world-wide which can be cured if detected early. The current gold standard for cervical cancer diagnosis is clumsy and time consuming because it relies heavily on the subjective knowledge of the medical professionals which often results in false negatives and false positives. To reduce time and operational complexities associated with early diagnosis, we require a portable interactive diagnostic tool for early detection, particularly in developing countries where cervical cancer incidence and related mortality is high. Incorporation of digital colposcopy in place of manual diagnosis for cervical cancer screening can increase the precision and strongly reduce the chances of error in a time-specific manner. Thus, we propose a robust and interactive colposcopic image analysis and diagnostic tool, which can categorically process colposcopic images into Type I, Type II and Type III cervigrams and identify lesions in least amount of time. Furthermore, successful binning of diagnosed cervigrams into digital colposcopic library and incorporation of a set of specific parameters that are typically referred to for identification of transformation zone and SCJ (squamo columnar junction) with the help of open source Programming language - “R” is one of the major highlights of the application. The software has the ability to automatically identify and quantify the morphological features, color intensity, sensitivity and other parameters digitally which may improve and accelerate screening and early diagnosis, ultimately leading to timely treatment of cervical cancer.