Intravascular ultrasound (IVUS) is the most favorable imaging modality that often used in coronary artery catheterization procedures and provides cross-sectional images of arterial wall structures and extend of atherosclerosis disease. Although several techniques have been developed to classify atherosclerotic tissues, deploying IVUS radiofrequency (RF) backscattered signals and/or grayscale images their clinical applications have seen limited success. In this paper, we propose a unified methodological framework from data collection, histology preparation, registration, feature extraction, and classification to achieve a reliable in vitro trained tissue characterization classifier for in vivo applications. Finally, the results from proposed algorithm is compared with state of the art virtual histology (VH) technique.     «

Intravascular ultrasound (IVUS) is the most favorable imaging modality that often used in coronary artery catheterization procedures and provides cross-sectional images of arterial wall structures and extend of atherosclerosis disease. Although several techniques have been developed to classify atherosclerotic tissues, deploying IVUS radiofrequency (RF) backscattered signals and/or grayscale images their clinical applications have seen limited success. In this paper, we propose a unified methodo...     »