## 3 Proposed Approach
### Segmentation  
Segmentation techniques attempt to partition the different tissues into non-overlapping regions [48]. VH-IVUS image segmentation poses a challenge, since in this type of image, there is no sharp boundary between colours, including red, white, dark green, and light green [49]. As a result, reliable discrimination of these components can be more difficult [24]. Spectral overlapping of NC with DC makes the segmentation process difficult [22]. Accurate segmentation of DC and NC are also required, since when DC is next to the NC in IVUS images, DC is often coded as NC [50]. The existing technology for VH-IVUS image segmentation is based on simple thresholding approaches. This method is not always reliable for segmenting the highly mixed tissue types involved in VH-IVUS images. Developing a more accurate approach is required for classifying necrotic core, fibrotic, fibro fatty, and calcified tissues [51]. Generally, medical image segmentation can be classified into six main categories, consisting of thresholding, edge detection, region growing, shape constrained deformable model, active counter model, and clustering [29; 52]. These techniques have their own advantages and drawbacks. Hence, the ability of the segmentation scheme to characterise the overlapped tissue in VH-IVUS images is explored here. Thresholding is a common method to separate the foreground and background regions using the pixel intensity [52]. However, this technique does not guarantee the separation of regions with similar intensities, but belonging to different regions [52]. The thresholding method only uses the distribution of pixel intensity, neglecting the neighbourhood information [53]. In terms of overlap borders, determining the appropriate threshold for VH-IVUS image segmentation is rather difficult [54]. The edge detection method is generally used to detect the points with abrupt changes in the grey levels. However, this method has limitations in terms of detecting weak edges [55]. Moreover, a closed curve cannot be easily identified [52]. Region growing algorithms are able to correctly classify the regions with similar properties and performs well in segmentation of noisy images [56]. However, the conventional region growing approach has major drawbacks in terms of selecting the seed point and determining the order of regions [55]. Therefore, in order to provide satisfactory segmentation results, the initialisation should be very close to the object contour [57]. The active shape model is fast and robust for identifying object shapes. This model is robust in the presence of noise, as well as image artefacts such as missing or damaged parts. However, it can only consider the deformations in the training set and has limitations with regard to unpredicted shapes [58]. Furthermore, in order to move the contour towards the optimal boundaries, the contour should be initialised close enough to the object [54; 57]. Clustering algorithms classify image pixels into k groups, whereas pixels belonging to a cluster are homogeneous [59]. The clustering methodology is considered to be very efficient at dealing with image segmentation due to the less complex mathematical models and there being no need for training data [60]. Clustering approaches have been adapted for VH-IVUS image segmentation for the following reasons:  
_Segmentation of poor boundaries._ Clustering approaches can usually be categorised into hard and fuzzy clustering. In image segmentation, when the cluster boundaries are poorly defined, the fuzzy clustering method can be used [61]. _Segmentation of overlapped borders._ Based on the fuzzy concepts, the intensity value has a membership degree associated with each cluster centre. In fuzzy clustering, each data element can belong to more than one cluster with a certain membership level, whereas in hard clustering, the data belongs to only one cluster. Clustering approaches can be successfully applied to discriminate between the different tissues.