## 3 Proposed Approach
### Combination of Geometric and GLCM Features  
GLCM algorithms were applied to detect the uniform texture patterns of VH-IVUS images with angles of 0, 45, 90, and 135. Therefore, GLCM\({}_{0}\), GLCM\({}_{45}\), GLCM\({}_{90}\), and GLCM\({}_{135}\), with size 1 \(\times\) 20, were created. The first component of PCA was also selected for each feature set, so four additional feature sets were generated. Therefore, GLCM and geometric features consisted of 12 members: GLCM\({}_{0}\) (\(GLCM_{1}\)), GF_GLCM\({}_{0}\) (\(GLCM_{2}\)), GF_GLCM\({}_{0}\)_PCA (\(GLCM_{3}\)), GLCM\({}_{45}\) (\(GLCM_{4}\)), GF_GLCM\({}_{45}\) (\(GLCM_{5}\)), GF_GLCM\({}_{45}\)_PCA (\(GLCM_{6}\)), GLCM\({}_{90}\) (\(GLCM_{7}\)), GF_GLCM\({}_{90}\) (\(GLCM_{8}\)), GF_GLCM\({}_{90}\)_PCA (\(GLCM_{9}\)), GLCM\({}_{135}\) (\(GLCM_{10}\)), GF_GLCM\({}_{135}\) (\(GLCM_{11}\)), GF_GLCM\({}_{135}\)_PCA (\(GLCM_{12}\)). Figure 15 illustrates the results of SVM using a combination of geometric and GLCM features. Figure 14: Results of SVM using geometrical and LBP features (\(LBP_{1}\): \(LBP_{8,1}\), \(LBP_{2}\): \(GF\_LBP_{8,1}\), \(LBP_{3}\): \(GF\_LBP_{8,1}\_PCA\), \(LBP_{4}\): \(LBP_{16,2}\), \(LBP_{5}\): \(GF\_LBP_{16,1}\), \(LBP_{6}\): \(GF\_LBP_{16,1}\)_PCA_, Acc: accuracy, Sn: sensitivity, Sp: specificity, Pr: precision, and FS: F-score). As illustrated in Figure 15, \(GLCM_{6}\) achieved the best results in terms of accuracy and sensitivity, whereas \(GLCM_{11}\) achieved the worst results for accuracy and sensitivity. In addition, \(GLCM_{9}\) achieved the best results in terms of accuracy, specificity, precision, and F-score. \(GLCM_{5}\) and \(GLCM_{8}\) obtained the worst results for specificity and precision. \(GLCM_{11}\) achieved the worst result for F-score. The experimental results demonstrated that feature selection increased the classification accuracy. In this section, a combination of geometric and MRL features is explored. To extract the MRL features, the VH-IVUS image is taken as a region. MRL feature combination with angles 0, 45, 90, and 135 was performed, and four feature vectors of size \(1\times 11\) were obtained. The PCA algorithm was applied for dimension reduction, and then the first component was selected for each feature set. Therefore, the MRL and geometric features consisted of 12 members: MRL\({}_{0}\) (\(MRL_{1}\)), GF_MRL\({}_{0}\) (\(MRL_{2}\)), GF_MRL\({}_{0}\) PCA (\(MRL_{3}\)), MRL\({}_{45}\) (\(MRL_{4}\)), MRL\({}_{45}\) (\(MRL_{4}\)), GF_MRL\({}_{45}\) (\(MRL_{5}\)), GF_MRL\({}_{45}\) PCA (\(MRL_{6}\)), MRL\({}_{90}\) (\(MRL_{7}\)), GF_MRL\({}_{90}\) (\(MRL_{8}\)), GF_MRL\({}_{90}\) PCA (\(MRL_{9}\)), MRL\({}_{135}\) (\(MRL_{10}\)), GF_MRL\({}_{135}\) (\(MRL_{11}\)), GF_MRL\({}_{135}\)_PCA (\(MRL_{12}\)). The SVM classifier was employed to classify the different combination of GF and MRL feature sets. Figure 16 depicts the results of SVM classification using a combination of geometric and MRL features with angles 0, 45, 90, and 135. Figure 15: Results of SVM using combined geometric and GLCM features (\(GLCM_{1}\): GLCM\({}_{0}\), GF_GLCM\({}_{2}\): GLCM\({}_{0}\), \(GLCM_{3}\): GF_GLCM\({}_{0}\)_PCA, \(GLCM_{4}\): GLCM\({}_{45}\)), \(GLCM_{5}\): GF_GLCM\({}_{45}\), GF_GLCM\({}_{45}\)PCA (\(GLCM_{6}\)), GLCM\({}_{90}\) (\(GLCM_{7}\)), GF_GLCM\({}_{90}\) (\(GLCM_{8}\)), GF_GLCM\({}_{90}\)_PCA (\(GLCM_{9}\)), GLCM\({}_{135}\) (\(GLCM_{10}\)), GF_GLCM\({}_{135}\) (\(GLCM_{11}\)), GF_GLCM\({}_{135}\)_PCA (\(GLCM_{12}\)), Acc: accuracy, Sn: sensitivity, Sp: specificity, Pr: precision, and FS: F-score). As demonstrated in this figure, the best performance in terms of accuracy, sensitivity, precision, and F-score was achieved by applying \(MRL_{3}\), but the best result for sensitivity was obtained by \(MRL_{7}\). However, \(MRL_{4}\) provided the lowest accuracy and sensitivity results. Moreover, \(MRL_{2}\), \(MRL_{7}\), and \(MRL_{4}\) obtained the worst results in terms of specificity, precision, and F-score, respectively. Figure 17 illustrates the best accuracy of geometric and texture features.