# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 1 Introduction  
Atherosclerosis involves pathological structures (stenosis, vulnerable plaque, etc.) within the arterial wall, leading to a significant reduction in blood flow. Atherosclerotic plaque can become fragile, potentially leading to cardiovascular diseases, such as angina, myocardial infarction, stroke, and sudden cardiac death [1]. Intravascular ultrasound (IVUS) is an in-vivo medical imaging technique capable of providing tomographic grayscale images of the vessel in real-time [2]. IVUS is commonly used in the diagnosis of atherosclerosis. Two types of borders in IVUS images, the lumen-intima border (LIB) and the media-adventitia border (MAB), are of substantial clinical interest. Automatic identification of these two borders is crucial for assessing the plaque burden and mechanical properties of the coronary artery [2]. Reliable border detection is obstructed by complicated intravascular structures, various forms of impediments, and intrinsic artifacts. Many methodologies have been proposed for IVUS image segmentation. We briefly summarize some of the published algorithms next. Pixel classification followed by deformable models is the most common strategy for IVUS image segmentation. The classification took the morphological, textural, and grayscale features into consideration and was achieved by the extreme learning machine [3], dictionary learning in thesparse representation framework [4], support vector machines [5; 6; 7], or artificial neural networks [8]. An assessment of the capability of different features for segmenting IVUS images was given by [PERSON] et al. [7]. In other deformable model-based methods, a global intensity term and an oriented smooth gradient were developed for the initial border extraction [9]. The detected border of the previous frame was utilized as an initial estimation of the border of the current frame in an IVUS sequence [10]. A 3D helical contour initialized as a centered helix model with a fixed radius was deformed to complete the segmentation [11; 12]. Among these methods, feature-based classification automatically gives a relatively accurate initial border, but the extraction of textural features and the training of classifiers are time consuming. The fast marching method achieves segmentation by propagating an interface to the boundary under a speed function. Fast marching methods for IVUS sequences generally model the gray-level distribution of vessel anatomical structures as a mixture of probability density functions (PDFs). Rayleigh PDFs [13; 14], gamma PDFs [15], and the intensity gradient [14; 15; 16] were introduced to construct the velocity function of the propagating interface. However, these algorithms assume that regions in the image have a statistically distinctive distribution, which is not always accurate, as IVUS images contain various forms of diseased structures. Among other categories of methods, the graph-based approach [17; 18], the Grow-Cut algorithm [19], the nonparametric statistical approach [20], and the deep learning architecture [21; 22] were introduced to IVUS image segmentation. The main limitation of deep learning for IVUS image segmentation lies in the lack of enough labeled data, which must be provided by IVUS experts. Despite the considerable attempts that have been devoted to IVUS image segmentation, no approved universal method exists that guarantees a successful segmentation. In this paper, we propose a three-fold automatic border detection strategy. First, we conduct superpixel-wise fuzzy clustering modified by edges (SFCME). Then, the clustering maps are fed into a framework that assigns regions of interest (ROIs) to different vascular tissues. The initial border is extracted according to the assignment and finally converged by edge-based level set evolution (LSE). The proposed SFCME-LSE method has a simple and fast implementation without the need for labeled training data. It takes advantage of the useful information in images while reducing interferences.