Abstract
The amount of plaque in coronary arteries in any particular point is identified by the IntraVascular UltraSound (IVUS) images. The classification of IVUS images is very important to diagnose various coronary artery diseases. In this study, the classification of IVUS images based on Non-negative Matrix Factorization (NMF) technique and Maximum Likelihood Classifier (MLC) is presented. Initially, the IVUS images are given to frost filter to remove speckle noise as the imaging technique uses ultrasound waves. Then, NMF technique is employed to extract the features and stored in database. Then MLC is used for classification of IVUS images for both normal and abnormal categories. The IVUS Image Classification (IIC) system obtains 98% classification accuracy by using NMF features and MLC classification.
Highlights
IntraVascular UltraSound (IVUS) images are mainly used for the diagnosis of coronary artery disease
The performance of Image Classification (IIC) system is measured by classification accuracy
The input normal and abnormal IVUS images are de-noised by frost filter to remove speckle noise at preprocessing stage
Summary
IVUS images are mainly used for the diagnosis of coronary artery disease. The progression and regressed lesions in the arteries are identified by using IVUS images. IVUS images based blood region classification using three dimensional brushlet expansions is described in [1]. The input IVUS images are given to brushlet analysis. Features like combining image information and geometric constraints are extracted. Neural network classifier is used for classification. Characterization of IVUS images based on feature selection and Support Vector Machine (SVM) classification is presented in [2]. The borders of the IVUS image are detected. Features like first-order statistics, Local Binary Pattern (LBP), Gray-level Co-Occurrence Matrix (GLCM), run length features and wavelet features are extracted
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