## 1 Introduction  
Heart disease is the most common cause of death worldwide. The usage of an automatic image processing method to diagnose heart disease is one of the most significant medical research areas [1]. The cause of most acute coronary syndromes (ACS) is often the rupturing of vulnerable plaques [2]. Although thin cap fibroatheroma (TCFA) lesions cause the most myocardial infarctions, reliable identification of TCFA remains a demanding challenge [3]. Plaque phenotype and composition appear to be related to plaque instability. Therefore, distinguishing between plaque types is necessary to detect vulnerable plaque [4]. The Virtual Histology-Intravascular Ultrasound (VH-IVUS) is an invasive imaging technique to analyse the internal morphology of coronary arteries. VH-IVUS has been proposed based on the 20 MHz IVUS platform for studying in-vivo plaque compositions [5]. Recent studies have shown that VH-IVUS can identify the high-risk plaques [6]. Apparently, development of quantitative techniques represents a practical approach to predict and quantify plaque compositions [7]. However, there are no proper automatic tools for feature extraction using VH-IVUS [8]. [PERSON] et al. [9] introduced the first commercially available IVUS, based on Volcano (Volcano Corporation, San Diego, CA, USA) IVUS clinical scanners [10]. Volcano's technology classifies four tissue types of plaque by means of spectral analysis methods with an accuracy of 93-97% [11]. However, the number of characterised IVUS frames is limited due to the Electrocardiogram (ECG)-gated acquisition. In fact, at each cardiac cycle, only one VH-IVUS image is characterised by RF spectrum. VH-IVUS images are generated at a rate of one frame per second; whereas, the rate of producing IVUS images is 30 frames per second [12]. It is worth noting that this commercial package has certain limitations in terms of extracting the significant features for TCFA detection. For instance, if the necrotic core (NC) is scattered, VIAS (Volcano Corporation, San Diego, CA, USA) can identify a single area measurement, but only the confluent NC in contact with the lumen is important [13]. Moreover, lumen border detection by employing VIAS software may provide unreliable results due to the border degradation caused by jpg image compression [13]. Extracting a useful feature set for classification is a challenging issue. Various techniques of feature analysis, such as brightness, contrast, colour, or texture, can be described comprehensively based on an image's appearance [14]. Geometrical features are often local in nature, so spatial information will not be considered [15]. For this purpose, texture features are defined for analysis of micro textures [16]. Texture can be considered to be a global pattern involving the repetition of local sub patterns [17]. This approach analyses spatial variations of pixel intensities and quantifies them into numerical values. Texture-based methods are very useful in providing descriptors for homogeneous regions [18]. VH-IVUS images comprise various granular patterns at different scales called \"texture\", related to the smoothness, roughness, and regularity of any patterns [19]. Based on previous studies, texture analysis is useful to measure the plaque compositions in ultrasound images. Due to the varying texture patterns of plaque components, texture descriptors have been applied for analysis of plaque compositions [20]. When considering statistical texture-based methods, Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run Length Matrix (GLRLM) descriptors remain among the top choices [21]. Based on the proposed criteria in [21], a subset of GLCM and Run Length (RL) descriptors maximises the classification accuracy of the training dataset. Furthermore, the efficacy of GLCM, Local Binary Patterns (LBP), Gabor filters, and RL method is demonstrated for plaque assessment [22]. To describe the local information of a pattern, LBP algorithms can be adopted, while the overall texture information can be described by GLCM algorithm [23].