## 2 Related Works  
IVUS image segmentation is often an essential step for characterisation of vessels, comprising shape, area, eccentricity, and thickness [8]. Generally, segmentation is an essential process for identifying image structures that can be completed using a manual, semi-automatic, or automatic method [29]. IVUS images consist of three basic parts: intima, media, and adventitia. The intima layer refers to the vessel interior. However, the adventitia represents the vessel outer layer, also characterised by flexible tissue. The media part discriminates the intima from the adventitia [30].  
The segmentation of atherosclerotic plaques has been addressed in several studies. [PERSON] et al. [31] proposed the first semi-automated technique for IVUS border detection by dynamic search algorithm. Similarity, a primary work was presented by [PERSON] et al. [32] for detection of media and lumen borders by applying heuristic graph searching and global image information. A fully automated segmentation step and a user-guided refinement stage for segmentation of luminal and external elastic lamina surface was presented by [PERSON] et al. [33]. [PERSON] et al. [34] presented a user-assistedmethod for media-adventitia border segmentation in IVUS images. To detect the media-adventitia border, fully automatic approaches were presented in [35; 36]. Automatic techniques to detect the lumen border are also presented in [37; 38; 39; 40]. However, some studies have proposed fully automated segmentation methodologies for plaque border detection [8; 41; 42; 43]. Automatic approaches for lumen and media adventitia border detection and plaque characterisation have been adapted, as well [44]. A comparison of the above-described IVUS image segmentation methods is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
Although there are several existing works in the literature in the domain of TCFA detection, it still remains a challenging task [3]. In our previous work [45], hybrid Fuzzy C-means and K-Nearest Neighbour (HFCM-kNN) was proposed to accurately segment VH-IVUS images. The proposed technique was capable of eliminating outliers and detecting clusters with different densities in VH-IVUS images. The next process was extracting the geometric features to provide an accurate definition of the vulnerable plaque. Machine learning approaches were applied for training the in-vivo images obtained from different patients. The major contribution of this paper is to improve VH-IVUS image segmentation using a semi-supervised approach. The K-means and PSO (KMPSO) model is hybridised with the mED algorithm to more accurately segment the VH-IVUS image. Typically, texture features have proven to be useful in describing the medical images [46]. Due to the heterogeneity of the atherosclerotic tissues, a robust texture discriminator needs to be developed [26]. However, in our previous work [45], only geometric features were extracted and fed to the classifiers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Segmentation** & **Technique** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [35]} & \multirow{4}{*}{Holistic approach} & \multirow{4}{*}{Automatic} & \(\bullet\) & Many frames can be quickly analysed without any manual intervention. & \(\bullet\) & IVUS data is labelled manually. \\  & &  
Therefore, the second contribution of this work is with regard to hybrid geometric and texture features, whereas existing research on TCFA detection has only focused on geometric features. Moreover, classification is performed by machine learning algorithm to optimally discriminate TCFA and non-TCFA plaque. As VH-IVUS is commercially available, the methodology proposed in this study can be implemented with other methods, such as iMap (Boston Scientific, Marlborough, MA, USA) and IVUS image-based techniques.