Teeth Classification Using GLCM and Support Vector Machine

Abstract

Teeth classification is a significant step for dental illness, which distinguishes the teeth as incisor, canine, pre-molar and molar. Precise teeth classification can soothe the agony of patients as well as help dental specialists for treatment. Tooth classification is a center step in numerous oral clinical research forms and is the reason for PC helped dental conclusion and treatment. Treatment of dental caries relies upon whether it includes enamel, dentine or pulp. At the point when dental caries includes pulp, they may prompt jaw blisters. Gray-Level Co-occurrence Matrix (GLCM) is one of the most well-known technique for surface examination. The co-occurrence matrix portraying the surface data is gotten from GLCM are entropy, homogeneity, complexity, vitality and connection. These properties of GLCM can be utilized in the arrangement of sores and tumors. Through this method dental caries and cyst in the teeth are found. GLCM and Support Vector Machine (SVM) are used in proposed work for correct classification of Normal teeth, Cross teeth, and Teeth spots. Segmentation separates teeth, gingiva and unwanted parts from images. Next from the portioned picture just teeth are taken and includes are separated from it utilizing Gray Level Co-Occurrence Matrix. Features extracted are given to Support vector machine and characterized with come about because of training images. SVM returns label that correctly identifies whether the given image has Cross teeth or Spot or Normal teeth.