Cervical Cancer Detection and Classification using Texture Analysis

Soumya M. K¹, Sneha K² and Arunvinodh C³

Department of Computer Science and Engineering, Royal College of Engineering and Technology, Akkikavu, Calicut University, India.

Corresponding Author E-mail: soumiya.aravind@gmail.com

Abstract: Cervical cancer is one of the deadliest cancer among women. The main problem with cervical cancer is that it cannot be identified in its early stages since it doesn’t show any symptoms until the final stages. Therefore the accurate staging will help to give the accurate treatment volume to the patient. Some diagnosing tools like X-ray, CT, MRI, etc. can be used with image processing techniques to get the staging of disease. Transform features such as contourlet and Gabor features mainly based on energy are used for the prediction of output. Second-order statistical features based on contrast, correlation, energy and homogeneity are significantly used to predict outcome from pre-treatment MR images of cervical cancer tumors. This paper proposes a classification technique using Magnetic Resonance Images(MRI) to obtain the staging of cervical cancer patients.

Keywords: cancer; Magnetic resonance imaging (MRI); Texture analysis; GLCM; Gabor; Contourlet; classification; SVM

[ HTML Full Text]

Back to TOC