A Non-negative Matrix Factorization for IVUS Image Classification Using Various Kernels of SVM

Abstract

AbstractIntravascular ultrasound (IVUS) is a medical methodology. It is a specially constructed catheter with a miniaturized Ultrasound Probe attached to the catheter’s distal end is a medical imaging technique. An efficient method for IVUS image classification using non-negative matrix factorization (NNMF) and various support vector machine (SVM) kernels are presented in this study. The input IVUS images are given to NNMF for feature extraction and stored in the feature database. Finally, SVM kernels like linear, polynomial, quadratic and radial basis function (RBF) are used for prediction. The system produces a classification accuracy of 94% by using NNMF and different SVM kernels.KeywordsIVUS image classificationNon-negative matrix factorizationSupport vector machineKernel functionRadial basis function