## 3 Proposed Approach
### Classification
#### 3.3.4 TCFA Detection Using Geometric and Texture Features  
In this section, classification was performed using geometric and texture features. The geometric and texture features were concatenated horizontally to carry out the plaque classification. Table 6 illustrates the descriptions of the texture features. Figure 12: Accuracy measures of multiple classifiers obtained using geometrical features. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classifiers** & **Acc (\%)** & **Sn (\%)** & **Sp (\%)** & **Pr (\%)** & **FS (\%)** \\ \hline
**BPNN** & 87.04 & 98.91 & 18.75 & 87.50 & 92.86 \\
**KNN_5** & 95.37 & 98.37 & 78.13 & 96.28 & 97.31 \\
**KNN_10** & 92.59 & 97.83 & 62.50 & 93.75 & 95.74 \\
**KNN_15** & 93.06 & 96.20 & 75.00 & 95.68 & 95.94 \\
**KNN_20** & 87.50 & 96.74 & 34.38 & 89.45 & 92.95 \\
**KNN_25** & 88.43 & 97.83 & 34.38 & 89.55 & 93.51 \\
**KNN_30** & 88.43 & 97.83 & 34.38 & 89.55 & 93.51 \\
**SVM_MLP** & 82.87 & 85.81 & 75.41 & 89.86 & 87.79 \\
**SVM_Linear** & 95.83 & 96.77 & 93.44 & 97.40 & 97.09 \\
**SVM_Poly\_1** & 96.71 & 98.71 & 91.80 & 96.84 & 97.76 \\
**SVM_Poly\_2** & 96.76 & 97.42 & 95.08 & 98.05 & 97.73 \\
**SVM_Poly\_3** & 97.69 & 99.35 & 93.44 & 97.47 & 98.40 \\
**SVM_rbf\_0.80** & 95.83 & 98.71 & 88.52 & 95.63 & 97.14 \\
**SVM_rbf\_0.90** & 96.76 & 99.35 & 90.16 & 96.25 & 97.78 \\
**SVM_rbf\_1** & 95.83 & 98.06 & 90.16 & 96.20 & 97.12 \\
**SVM_rbf\_1.10** & 97.69 & 100 & 91.80 & 96.88 & 98.41 \\
**SVM_rbf\_1.20** & 97.22 & 98.06 & 95.08 & 98.06 & 98.06 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance of multiple classifiers using geometrical features (Acc: accuracy, Sn: sensitivity, Sp: specificity, Pr: precision, and FS: F-score). All experiments were conducted by applying the SVM_rbf_1.10. The following sub sections describe the experiments and analyses that were carried out for combined features. To investigate the power of texture features for TCFA detection, combined features were obtained by concatenating the texture features with the geometric features. The label of each geometric feature vector that was attached at the end of this vector must be moved to the end of the new feature vector. For every VH-IVUS image, geometric features (GF) and texture features (TF) were extracted. Figure 13 demonstrates the combined features using geometric and texture features. Combination of Geometric and LBP Features  
LBP features were extracted from VH-IVUS image for two different sets of P and R ((8, 1) and (16, 2)). Then, features of two vectors of size 1 \(\times\) 256 and 1 \(\times\) 65,536 were obtained for \(LBP_{8,1}\) and \(LBP_{16,2}\), respectively. The PCA technique was then applied, and the first component was selected for each feature set. Therefore, LBP and the geometric features consisted of 6 members: \(LBP_{8,1}\) (\(LBP_{1}\)), \(GF\_LBP_{8,1}\) (\(LBP_{2}\)), \(GF\_LBP_{8,1}\)_PCA_ (\(LBP_{3}\)), \(LBP_{16,2}\) (\(LBP_{4}\)), \(GF\_LBP_{16,1}\) (\(LBP_{5}\)), \(GF\_LBP_{16,1}\)_PCA_ (\(LBP_{6}\)). Performance measurements were obtained on the basis of accuracy, sensitivity, specificity, precision, and F-score. Figure 14 illustrates the results of the SVM classifier using geometrical and LBP features. \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Description** \\ \hline GF\_LBP & Combined geometric and LBP features. \\ GF\_LBP\_PCA & Combined geometric and the first principle components of LBP \\ GF\_GLCM & Combined geometric and GLCM features. \\ GF\_GLCM\_PCA & Combined geometric and first principle components of GLCM \\ GF\_MRL & Combined geometric and MRL features. \\ GF\_MRL\_PCA & Combined geometric and the first principle components of MRL \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Texture feature description. Figure 13: Combined features using geometric and texture features. As depicted in Figure 14, the best accuracy results were achieved by \(LBP_{2}\), \(LBP_{3}\), and \(LBP_{6}\), while the worst results were obtained by \(LBP_{1}\) and \(LBP_{5}\).