## 3 Proposed Approach
### Feature Extraction
#### 3.2.2 Texture-Based Features  
Biomedical texture operations analyse the desired texture information in terms of spatial scales and the directions of a texture image, which are fundamental for visual texture discrimination. The most important characteristic of texture is that it is scale dependent [80]. Due to the fact that pixel-based grey-level-only methods are not sufficient to discriminate the complicated structures of tissue components, texture features are used [81]. It is an important and challenging problem to extract the desirable feature set for the classification step [56]. Texture features are widely used for analysing medical images. Texture is a primary visual feature, referring to the natural properties of the object in the image [82]. This approach analyses the spatial variations of pixel intensities and quantifies them into a numerical value [83,84]. In addition to geometric features, which depend on pixel intensity, texture-based approaches characterise the statistical and spatial relationships in the neighbourhood of the pixel [82]. Since geometrical features are often local in nature, the spatial information of the image will not be considered [15]. Therefore, texture features, as a global pattern, are defined to analyse the micro textures [16]. Low-level quantitative image analysis can be categorised based on intensity and texture. Image intensity describes the statistical distribution of the pixel values inside a Region of Interest (ROI) [80]. Many forms of texture analysis have been developed, including co-occurrence matrices, second-order statistics, Gauss-Markov random field, etc. These methods were limited to the analysis of spatial relations within small neighbourhoods and the analysis of micro textures [16]. However, texture information relates to the micro- and macro-structural context of biomedical tissue [80]. Classic examples of texture operators such as Gabor wavelets, GLCMs, Histogram of Oriented Gradients (HOG), LBPs, and Scattering Transforms (ST) need to be able to discriminate between types of image scales, directions, and transitions. Designing an ideal set of texture operators that is able to accurately identify the tissue type is a very challenging task. However, the comprehensive analysis of spatial scales and directions is computationally expensive for discrete texture functions. Therefore, combinations of image scales and directions with discriminative capabilities for each plaquecomponent should be considered. If the descriptor is insensitive to the transformation of the input image, the operator is invariant [80]. Previous studies have identified texture analysis as being useful in the analysis of ultrasound images. Plaque components appear with different texture patterns in IVUS images (Figure 11). Therefore, texture analysis methods are best suited to classifying pixels from plaque areas of IVUS images into three tissue classes, including dense calcium (DC), necrotic core (NC), and fibro-fatty (FF) [47]. Before extracting the texture features, it should be emphasised that some descriptors for texture classification could be used, such as Gabor filters, Hidden Markov trees, or the wavelet approach [85]. However, the effective classification of plaque type involves the generation of significant features. These discriminative features will be combined with geometric features as input data for the classifier. Such choices can made on the basis of a study of the literature. Grey-Level-based texture descriptors can be designed to reflect the different grey-level profile characteristics of the hard and soft plaque. Co-occurrence matrices describe the repeated occurrences of some grey-level configurations in the plaque texture classes. Run-length measures describe the maximum contiguous set of constant grey-level pixels located in a specified direction. A large number of neighbouring pixels of the same grey level represents a coarse texture, while a small number of these pixels represents a fine texture.