## 1 Introduction
It is often necessary to characterise the tissue appearance of medical images by smoothness, grain, regularity, and homogeneity by applying various texture descriptors; Run-length method describes the coarseness of a texture in the specified linear orientation [19]. [PERSON], [PERSON], [PERSON] and [PERSON] [7] described IVUS tissues by means of different types of features: radial frequency, texture-based features, and combined features. Three different texture descriptors were applied, including co-occurrence Matrix, LBP, and Gabor filters. [PERSON] et al. [24] proposed a semi-automated method for plaque characterisation. To classify the tissue type, textural features, comprising co-occurrence matrices, LBPs, mean value, entropy, intensity, and geometrical features, were extracted for each pixel. [PERSON] et al. [25] applied a set of different textural features consisting of first order (FO) statistics, GLCM, WF, RL, and LBP for tissue labeling. [PERSON] et al. [26] presented a texture-based method by applying discrete wavelet packet frame (DWPF) for labelling the atherosclerotic plaque. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [20] proposed an image-based method for characterisation of coronary plaques in IVUS images. A combination of grey-scale-based features and textural descriptors, including LBP, MRL, and Neighbouring Grey-Level (NGL) are extracted to categorise each pixel into one of the three classes (DC, NC, and FF). A fully automated method is presented by [PERSON] et al. [27] to classify the soft and hard plaque. After border detection, FCM method classified the GLCM features. [PERSON] and [PERSON] [22] extracted a set of textural features, including first order (FO) statistics, wavelet features (WF), GLCM, RL, and LBP with different scales to improve the plaque type classification. Therefore, in this study, three commonly used textural features, including LBP, GLCM, and MRL, are extracted from VH-IVUS images. Due to the increased noise, artefacts, and mixed tissue in VH-IVUS images, the studied methodologies may not be suitable for providing accurate results of segmentation. Furthermore, most of the presented approaches are semi-automated. Some approaches are time consuming and involve expert corrections to refine the results. Developing a robust image segmentation approach may be a complicated problem [28]. The aim of this research is to improve the identification of TCFA in VH-IVUS images by improving VH-IVUS image segmentation and vulnerability detection. Therefore, hybrid Fuzzy C-means with Particle Swarm Optimisation (PSO) and minimum Euclidean distance algorithm (FCMPSO-mED) and Hybrid K-means with PSO and minimum Euclidean distance (KMPSO-mED) models are adapted based on the semi-supervised learning theory. FCMPSO-mED and KMPSO-mED can classify the unlabelled data without requiring the training data. Therefore, it improves the accuracy of pixel classification when sufficient training data are not available. The rest of this paper is structured as follows. Section 2 describes the related works. The proposed approach for segmentation, feature extraction, and TCFA detection are described in Section 3. Validation and comparison with existing methods are described in Section 4. The proposed methodology is discussed in Section 5. Finally, the paper is concluded in Section 6.