Detection and Segmentation of Cancer Regions in Oral MRI images using ANFIS Classification Method
AG Arumugam1, M.Praveena Kirubabai2

1M.Praveena Kirubabai, Associate professor, Department of Computer Science, Lady Doak College, Madurai 2. (Tamil Nadu), India.
2Dr. G Arumugam, Senior Professor and Head of the Department (Retd) Department of Computer Science, Madurai Kamaraj University, Madurai 21. (Tamil Nadu), India.

Manuscript received on 17 August 2019. | Revised Manuscript received on 24 August 2019. | Manuscript published on 30 September 2019. | PP: 6376-6380 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5449098319/2019©BEIESP | DOI: 10.35940/ijrte.C5449.098319
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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.
Keywords: Oral, Cancer, Detection, Enhancement, Filtered Image.

Scope of the Article: Classification