Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images

Abstract

Abstract-Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-densitybased image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.Index Terms-Arterial wall segmentation, calcification detection, intensity prior, intravascular ultrasound (IVUS), lumen segmentation, media adventitia segmentation, model-based segmentation, side branch detection, shape prior.

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

“…Recently, a shape-driven method for lumen and media-adventitia segmentation was introduced by Unal et al (Unal et al (2008)). In this work, the lumen and media-adventitia contours were constrained to a smooth, closed geometry.…”

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

“…An automated approach based on deformable models has been reported by Plissiti et al [11], who employed a Hopfield neural network for the modification and minimization of an energy function as well as a priori vessel geometry knowledge. Unal et al proposed in [12] a shape-driven approach to the segmentation of IVUS images, based on building a shape space using training data and consequently constraining the lumen and media-adventitia contours to a smooth, closed geometry in this space.…”

“…The latter points are defined in this work as those which satisfy the following equations: r = max {C( )} + 1 (11) r = max {C( )} 1 (12) For the above points in the 2D space, function f is defined as the signed Euclidean distance from the initialized contour for = const , i.e. f ( ,r C( )) = r C( ) (13) Following the definition of f , the FastRBF library [16] was used to generate the smooth contour approximation c ' by removing duplicate points where f has been defined (i.e.…”

Application of Segmentation Techniques on Atherosclerosis Images

“…Danijela et al presented a method for carotid artery vessel wall segmentation in computed tomography angiography (CTA) data [8]. Gozde Unal et al presented a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain [9]. Simon et al [10] presented a research paper on vulnerable atherosclerotic plaque elasticity reconstruction based on a segmentation-driven optimization procedure using strain measurements a theoretical framework approach.…”