## 3 Proposed Approach
### Segmentation
#### 3.1.6 Pixel-Wise Classification Using Minimum the Euclidian Distance (mED) Algorithm  
To address the image segmentation problem, several unsupervised and supervised approaches have been described. The supervised learning requires appropriate labelled data as training set to build a predictive model [29]. In contrast, unsupervised techniques can be explored without knowing any prior information [74]. In our previous work, k-NN algorithm was applied for pixel wise classification [45]. Although k-NN algorithm can be used for large number of training sets, but the classification is slow and time-consuming. The choice of \(k\) also affects the performance of this algorithm. Also, the accuracy of this algorithm may be degraded in presence of noisy or irrelevant features [56]. Therefore, a new algorithm namely Minimum Euclidian distance (mED) is proposed to classify the pixels in testing images. This algorithm does not require any training data and uses the colour feature matrix CF. For pixel classification by mED algorithm, feature matrix \(CF\) is divided into four classes: NC, DC, FF, and FI. The minimum Euclidian distance between testing sample \(x_{i}\) and those classes are then calculated and represented as \(MinClass_{i}\) = {\(min_{NC}\), \(min_{DC}\), \(min_{FF}\), \(min_{FI}\)}. The least minimum value of \(MinClass\) are computed as \(Minf_{i}\) and sample \(x_{i}\) is assigned to the class which provides the \(Minf_{i}\). Figure 6 illustrates the pseudo-code of proposed pixel classification by mED algorithm. Dissimilar to the crisp K-means algorithm, FCM clustering permits partial membership in different tissue types, therefore it can be employed where a pixel may belong to the multiple tissue. In VH-IVUS image there is very often no sharp boundary between colours, including red, white, dark green, and light green. As a result, fuzzy method is often appropriate for clustering the likely colours [75]. Therefore, FCMPSO-mED model is also implemented, whereas FCM algorithm is utilised to generate the cluster centres. Four colour images representing the plaque components are provided using the assigned label during the pixel classification process. The area of each plaque component can be calculated based on the number of non-zero pixels in each image. The area of NC, DC, FF, and FI images can be represented as tissue area features of \(f_{NC},f_{DC},f_{NC},f_{FF}\), and \(f_{FI}\), respectively. These values are considered to be the primary features for TCFA detection. Figure 7 depicts the effectiveness of the KMPSO-mED model by segmenting the overlapping tissues in VH-IVUS images. To investigate the consistency of the proposed KMPSO-mED model using different plaque types, several real VH-IVUS images were tested and the results compared. Table 3 shows the segmentation results achieved by the KMPSO-mED model for 9 VH-IVUS images. Figure 6: Pseudo-code for the proposed pixel classification using the mED algorithm. Figure 7: Segmentation by KMPSO-mED: (**a**) overlapped tissue, and (**b**) segmented area. When the clustering processes are completed, the correctness of each clustering technique is validated by silhouette index [76]. The silhouette value of each pixel shows its similarity with other pixels belonging to this cluster in comparison with pixels belonging to other clusters. The range of silhouette value varies from \(-1\) to +1. Higher average silhouette values indicate higher clustering accuracy [77]. Based on Table 2, all obtained SW show the high accuracy rate of the clustering process. Images 1 to 6 involve four plaque components. Therefore, KMPSO-mED segmented these images into FF, FI, NC, and DC. Images 7 and 8 have three components; these images are segmented into