Lumen and media-adventitia border detection in IVUS images using texture enhanced deformable model

Abstract

Lumen and media–adventitia (MA) borders in intravascular ultrasound (IVUS) images are critical for assessing the dimensions of vascular structures and providing plaque information in the diagnosis and navigation of vascular interventions. However, manual delineation of the lumen and MA borders is an intricate and time-consuming process. In this paper, a texture-enhanced deformable model (TEDM) is proposed to accurately detect these borders by incorporating texture information with the morphological factors of deformable model. An ensemble support vector machine classifier is used to classify IVUS pixels presented by texture features into different tissue types. The image regionalization maps of different tissue types are further used for texture enhancement modules in the TEDM. The proposed TEDM method has been tested on 1500 images from 15 clinical IVUS datasets by comparing with the manual delineations. Evaluation results demonstrate that our method can accurately detect lumen and MA surfaces with small surface distance errors of 0.17 and 0.19 mm, respectively. Accurate segmentation results provide 2D measurements of MA/lumen areas and 3D vessel visualizations for vascular interventions.