Region based Light Absorption Technique with Image segmentation for Tissue Classification Latha G. Maria Dhayana*, Dr. Raja K. Bommanna** *Associate Professor, Department of Electronics and Communication Engineering, Sathyam Engineering College, Tamil Nadu, India **Professor and Head, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India Online published on 15 September, 2016. Abstract The problem of tissue classification has been discussed in several articles and there are number of approaches has been discussed. But the earlier methods suffers with the accuracy in tissue classification and produces more false results. To overcome the issue of false classification an region based light absorption technique with gray scale approximation scheme has been proposed in this paper. The method deploys the light rays over the tissue to be classified using the spectra meter and the reflected light rays are collected through the light absorption panel. Whatever the light rays being absorbed are collected through the panel and the method performs the absorption and gray scale approximation scheme to perform tissue classification. The method splits the tissue into multiple region and for each region the method computes the amount of light being absorbed by the tissue, water particles, and melanin. Based on amount of light being passed and amount of light being received the light absorption technique estimates the amount of light being absorbed. Using light absorption method the region which is affected is identified and then the method performs gray scale approximation scheme which classifies the tissue towards number of classes. The gray scale approximation method performs the computation of gray level depthness of each pixel in different region and based on that the tissue classification is performed. The proposed method improves the performance of tissue classification and reduces the false classification ratio. Top Keywords Tissue Classification, Spectra meter, Light Absorption, Region Based Approach, Image Segmentation. Top |