## 5 Discussion  
Spectrum analysis of IVUS-derived radiofrequency (RF) data enables a more detailed analysis of plaque morphology. Preliminary in vitro studies correlated four histological plaque components with a specific spectrum analysis of the RF data. The different components, consisting of fibrous, fibrofatty, necrotic core, and dense calcium, were colour coded. Coronary tissue maps were reconstructed from RF data using VH-IVUS software. VH- IVUS has the potential to detect high-risk lesions and can provide new insights into the pathophysiology of coronary artery disease [110]. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **TP** & **TN** & **FP** & **FN** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F-Score** \\ \hline CA\&CD & 9 & 38 & 2 & 9 & 81.03 & 50 & 95 & 81.81 & 62.07 \\ CA\&SVM & 11 & 35 & 5 & 7 & 79.31 & 61.11 & 87.50 & 68.75 & 64.70 \\ CD\&SVM & 8 & 39 & 3 & 8 & 81.03 & 84.81 & 84.81 & 84.81 & 75.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between CA, SVM, and CD methods for TCFA detection (TP: true positive, FP: false positive, FN: true negative). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**TP** & **TN** & **FP** & **FN** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F-Score** \\ \hline
41 & 33 & 0 & 2 & 97.36 & 95.34 & 100 & 100 & 97.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Diagnostic accuracy of VH-IVUS for detecting TCFA based on cardiologist decision and SVM (TP: true positive, FP: false positive, FN: false negative, TN: true negative). However, the overlapping tissue in VH-IVUS image causes difficulty in segmentation of atherosclerotic plaques [29]. Simple clustering methods based on the pixels' intensities are not reliable for segmenting the highly mixed tissue types involved in VH-IVUS images. To overcome this limitation, robust semi-supervised models were presented in this study. The main contribution of this paper is proposing the KMPSO-mED classification methodology based on semi-supervised learning theory to improve the accuracy of VH-IVUS image segmentation. This model does not require a training sample, and as a result, time and resources can be saved. Therefore, it improves the accuracy of pixel classification when sufficient training data are not available. It classifies the test image without any prior information regarding the number of clusters. The proposed model has the potential to discriminate overlapping tissue. The segmentation process was moderately fast due to the pixel-wise classification. The designed models attempt to take advantage of supervised learning in the segmentation of test images. These models can also enhance the ability of the clustering algorithm by removing outliers to provide more accurate segmentation results in the VH-IVUS image. Furthermore, these techniques are capable of dividing the clusters with different densities in the VH-IVUS image. In medical diagnosis, misclassifying plaque tissue as vulnerable plaque could be highly important. Plaque components consisting of NC, DC, FF, and FI have different textures in IVUS-based images [26]. To capture the varying heterogeneity of plaque components, multi-scale feature extraction is applied. Another novelty of the proposed method is that it is a linear combination of geometric and informative texture features to compute a discriminative index for plaque classification and TCFA detection. The present study proposes a computer-aided method for plaque type classification that is fast and accurate. To provide the correct classification, a fusion of different geometric and texture features has been applied, whereas existing research on TCFA detection has only focused on the geometric features. Nevertheless, it takes more time to extract the texture features and feature selection; also, the range of values of the geometric and texture features are different, and this affects the classification accuracy in fusion step.