A novel computational method for plaque tissue characterization based on Intravascular Optical Coherence Tomography (IV-OCT) is presented. IV-OCT is becoming a powerful tool for the clinical evaluation of atherosclerotic plaques; however, it requires a trained expert for visual assessment and interpretation of the imaged plaques. Moreover, due to the inherit effect of speckle and the scattering attenuation of the optical scheme the direct interpretation of OCT images is limited. To overcome these difficulties, we propose to automatically identify the A-line profiles of the most significant plaque types (normal, fibrotic, or lipid-rich) and their respective abundance by using a probabilistic framework and blind alternated least squares to achieve the optimal decomposition. In this context, we present preliminary results of this novel probabilistic classification tool for intravascular OCT that relies on two steps. First, the B-scan is pre-processed to remove catheter artifacts, segment the lumen, select the region of interest (ROI), flatten the tissue surface, and reduce the speckle effect by a spatial entropy filter. Next, the resulting image is decomposed and its A-lines are classified by an automated strategy based on alternating-least-squares optimization. Our early results are encouraging and suggest that the proposed methodology can identify normal tissue, fibrotic and lipid-rich plaques from IV-OCT images.
Jose D. Rico-Jimenez, Daniel U. Campos-Delgado, Martin Villiger, Brett Bouma, and Javier A. Jo, "Automatic classification of atherosclerotic plaques imaged with intravascular OCT
(Conference Presentation)," Proc. SPIE 9697, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XX, 96970B (Presented at SPIE BiOS: February 15, 2016; Published: 26 April 2016); https://doi.org/10.1117/12.2214563.4848328227001.
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