Abstract
The process of extraction of blood vessel boundaries in the case of Intravascular Ultrasound (IVUS) images is extremely indispensable in the quantitative examination of cardiovascular functions. The affected region of plaque in the IVUS image has to be measured quantitatively to fix the stent challenges. In this paper, the lumen and coronary plaque feature extraction are done by the adjacent pattern method. To get appropriate features, sequential feature selection is carried out and directed with the assistance of the area under the precision and recall value. Subsets of appropriate image characteristics for lumen, plaque, and adjoining tissue characterization acquired are trained with the assistance of Support Vector Machine (SVM) based Convolution neural network (CNN). These features were able to accurately recognize plaque regions of the predicted class label based on the weighted matrix and display the plaque severity level. The proposed SVM based CNN classifier is compared with CNN-Basic and SVM classifier and the performance of feature extraction and classification methods are evaluated with quantifiable metrics like true positive (TP), true negative (TN), false positive (FP) and false negative (FN). The performance of plaque feature detection evaluated with quantitative values for accuracy, sensitivity, specificity, precision, recall, and F-score.
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References
Mojsilovic, A., Popovic, M., Amodaj, N., Babic, R., Ostojic, M.: Automatic segmentation of intravascular ultrasound images: a texture-based approach ease. Ann. Biomed. Eng. 25, 1059–1071 (1997)
Gil, D., Hernandez, A., Rodriguez, O., Mauri, J., Radeva, P.: Statistical strategy for anisotropic adventitia modelling in IVUS. IEEE Trans. Med. Imaging 25(6), 768–778 (2006)
Frostegard, J.: SLE, atherosclerosis and cardiovascular disease. J. Intern. Med. 257, 13652796 (2005)
Lee, H., Grosse, R., Ranganath, R., Ng, A.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, p. 609–616 (2009)
Shiddieqy, H.A., Hariadi, F.I., Adiono, T.: Implementation of deep-learning based image classification on single board computer. In: International Symposium on Electronics and Smart Devices (ISESD), pp. 133–137 (2017)
Caballero, K.L., Barajas, J., Pujol, O., Rodriguez, O., Radeva, P.: Using reconstructed IVUS images for coronary plaque classification. In: IEEE, pp. 2167–2170 (2007)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1, 321–331 (1988)
Lo Vercio, L., Orlando, J.I., del Fresno, M., Larrabide, I.: Assessment of image features for vessel wall segmentation in intravascular ultrasound images. Int. J. Comput. Assist. Radiol. Surg. 11(8), 1397–1407 (2016). https://doi.org/10.1007/s11548-015-1345-4
Mahadevi, C., Sivakumar, D.S.: Evaluation of segmentation algorithms for the detection of plaque border and wall cross-sectional area in b-mode intravascular ultrasound images. J. Int. Pharm. Res. 46(4), 388–397 (2019)
Mendizabal-Ruiz, E.G., Rivera, M., Kakadiaris, I.A.: Robust segmentation of the luminal border in intravascular ultrasound using a probabilistic approach. Technical Report UH-CS-11-02, University of Houston, Houston, TX, USA (2011)
Mesejo, P., Ibanez, O., Cordon, O., Cagnoni, S.: A survey on image segmentation using meta heuristic-based deformable models: state of the art and critical analysis. Appl. Soft Comput. 44, 129 (2016)
Taki, A., Roodaki, A., Setarehdan, S.K., Avansari, S., Unal, G., Navab, N.: An IVUs image-based approach for improvement of coronary plaque characterization. Comput. Biol. Med. 43, 268–280 (2013)
Pazinato, D.V., et al.: Pixel-level tissue classification for ultrasound images. IEEE J. Biomed. Health Inform. 20, 256–267 (2016)
Rotger, D., Radeva, P., Fernández-Nofrerías, E., Mauri, J.: Blood detection in IVUS images for 3D volume of lumen changes measurement due to different drugs administration. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 285–292. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74272-2_36
Sridevi, S., Sundaresan, M.: Hybrid feature extraction techniques for accuracy improvement in IVUS image classification. Int. J. Sci. Technol. Res. 9(4), 720–724 (2020)
Turaga, S.C., et al.: Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22(2), 511–538 (2010)
Dehnavi, S.M., Babu, M.P., Yazchi, M., Basij, M.: Automatic soft and hard plaque detection in IVUS images: a textural approach. In: IEEE Conference on Information and Communication Technologies (ICT 2013), pp. 214–219. IEEE (2013)
Su, S., Hu, Z., Lin, Q., Hau, W.K., Gao, Z., Zhang, H.: An artificial neural network method for lumen and media-adventitia border detection in IVUS. Comput. Med. Imaging Graph. 57, 29–39 (2017)
Giannoglou, V.G., Theocharis, J.B.: Decision fusion of multiple classifiers for coronary plaque characterization from IVUS images. Int. J. Artif. Intell. Tools 23, 1460005 (2014)
LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, p. II97 (2004)
Zhang, L., Jing, J., Zhang, H.: Fabric defect classification based on LBP and GLCM. Appl. Soft Comput. 8, 81–89 (2015)
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Mahadevi, C., Sivakumar, S. (2020). Performance of Coronary Plaque Feature Extraction and Identification of Plaque Severity for Intravascular Ultrasound B-Mode Images. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_21
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