Detection and segmentation of cancer regions in oral MRI images using ANFIS classification method

Abstract

The detection of oral cancer is an important area of research in the literature today. About 82% of the patients are diagnosed at the early stage and 27% of the patients are diagnosed at the advanced stage. Early detection reduces the mortality rate of the patients. An automated approach is proposed to detect and segment the oral cancer in oral Magnetic Resonance Images (MRI). The quality of the image is improved using adaptive mean filter and enhanced using adaptive histogram equalization technique. The enhanced image is transformed using Gabor transform and the features of the oral image are extracted from this transformed image. These features are classified using ANFIS classification approach. Morphological approaches are used to segment the cancer region in the classified abnormal oral MRI images.