Abstract
Coronary heart disease is the most common type of heart disease that leads to heart attacks. The identification of vulnerable plaques, especially the thin-cap fibroatheroma (TCFA), is crucial to the diagnosis of coronary artery disease. Intravascular optical coherence tomography (IVOCT), an emerging imaging modality, has been proven to be useful for the identification of vulnerable plaques. In this work, we propose an approach to identify the volumes with fibroatheroma frames automatically. In the proposed method, we first detect the lumen using a graph-search based method from unfolded images. Then a region of interest starting from the lumen boundary is cropped for feature extraction. We explore three texture features, Local Binary Patterns (LBP), Haar-like and Histograms of Oriented Gradients (HOG), for fibroatheroma identification. In order to reduce the amount of calculation, a bag of words (BoW) approach is utilized in the feature extraction. Finally, support vector machines are trained to classify the volumes with fibroatheroma frames from those without. A dataset with 41 volumes collected from 41 different subjects is used. Experimental results show that we can achieve a sensitivity of 0.88 and a specificity of 0.94, demonstrating the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Athanasiou L, Exarchos T, Naka K, Michalis L, Prati F, Fotiadis D (2011) Atherosclerotic plaque characterization in optical coherence tomography images. In: Proc of IEEE Engineering in Medicine and Biology Conference, pp 4485–4488
Athanasiou LS, Bourantas CV, Rigas GA, Exarchos TP, Sakellarios AI, Siogkas PK, Papafaklis MI, Naka KK, KMichalis L, Prati F (2013) Fully automated calcium detection using optical coherence tomography. In: Proc of IEEE Engineering in Medicine and Biology Conference, pp 1430–1433
Athanasiou LS, Bourantas CV, Rigas G, Sakellarios AI, Exarchos TP, Siogkas PK, Ricciardi A, Naka KK, Papafaklis MI, Michalis LK (2014) Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images. J Biomed Optics 19:026009
Ballard DH (1981) Generalizing the hough transform to detect arbitrary shapes. Pattern Recogn 13:111–122
Chiu SJ, Li XT, Nicholas P, Toth CA, Izatt JA, Farsiu S (2010) Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation. Optics Express 18:19413–19428
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Comput Vis Pattern Recogn 1:886–893
Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271
Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874
Furqan S, Gulistan R, Rehan A (2019) Artificial neural network based classification of lung modules in ct images using intensity, shape and texture. J Ambient Intell Human Comput 2019:1–15
Jang IK, Tearney GJ, MacNeill B, Takano M, Moselewski F, Iftima N, Shishkov M, Houser S, Taretz H, Halpern EF, BoumaIn BE (2005) Vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 111:1551–1555
Menezes P, Barreto JC, Dias J (2004) Face tracking based on haar-like features and eigenfaces. IFAC Proc Vol 37:304–309
Naghavi M, Libby P, Falk E (2003) From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: part i. Circulation 108:1664–1672
Ojala T, Pietikainen M, Maenpaa T (2012) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987
Prati F, Regar E, Mintz GS, Arbustini E, Mario CD, Jang IK, Akasaka T, Costa M, Guagliumi G, Grube E (2010) Expert review document on methodology, terminology, and clinical applications of optical coherence tomography: physical principles, methodology of image acquisition, and clinical application for assessment of coronary arteries and atherosclerosis. Eur Heart J 31:401–415
Prati F, Guagliumi G, Mintz GS, Costa M, Regar E, Akasaka T, Barlis P, Tearney GJ, Jang IK, Arbustini E (2012) Expert review document part 2: methodology, terminology and clinical applications of optical coherence tomography for the assessment of interventional procedures. Eur Heart J 33:2513–2520
Rico-Jimenez J, Campos-Delgado DU, Villiger M, Otsuka K, Bouma BE, Jo JA (2016) Automatic classification of atherosclerotic plaques imaged with intravascular oct. Biomed Opt Express 7:4069–4085
Sahu S, Singh H, Kumar B, Singh A (2018) Statistical modeling and gaussianization procedure based de-speckling algorithm for retinal oct images. J Ambient Intell Human Comput 2018:1–14
Tearney GJ, Regar E, Akasaka T, Adriaenssens T, Barlis P, Bezerra HG, Bouma B, Bruining N, Cho JM, Chowdhary S (2012) Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the international working group for intravascular optical coherence tomography standardization and validation. J Am Coll Cardiol 59:1058–1072
Tsantis S, Kagadis GC, Katsanos K, Karnabatidis D, Bourantas G, Nikiforidis GC (2012) Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography. Med Phys 39:503–513
Ughi GJ, Adriaenssens T, Sinnaeve P, Desmet W, D’Hooge J (2013) Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images. Biomed Opt Express 4:1014–1030
Wong ND (2010) Epidemiological studies of CHD and the evolution of preventive cardiology. Nature Rev 2010:89–276
Xu C, Schmitt JM, Carlier SG, Virmani R (2008) Characterization of atherosclerosis plaques by measuring both backscattering and attenuation coefficients in optical coherence tomography. J Biomed Opt 13:034003–034003
Xu M, Cheng J, Wong D, Taruya A, Tanaka A, Liu J (2014) Automatic atherosclerotic heart disease detection in intracoronary optical coherence tomography images. In: Proc of IEEE engineering in medicine and biology conference, pp 174–177
Xu M, Cheng J, Wong DWK, Liu J, Taruya A, Tanaka A (2015) Graph based lumen segmentation in optical coherence tomography images. In: Proc of international conference on information and communications security, pp 1–5
Xu M, Cheng J, Lee JA, Xu G, Wong WKD, Taruya A, Foin N, Wong P, Tanaka A (2016) Automatic fibroatheroma identification from intravascular optical coherence tomography images. In: MICCAI workshop
Xu M, Cheng J, Li A, Lee JA, Wong DWK, Taruya A, Tanaka A, Foin N, Wong P (2017) Fibroatheroma identification in intravascular optical coherence tomography images using deep features. In: Proc of IEEE engineering in medicine and biology conference, pp 1501–1504
Zhou P, Zhu T, He C, Li Z (2017) Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images. J Opt Soc Am A 34:1152–1159
Funding
This work was supported in part by the Agency for Science, Technology and Research, Singapore, under Grant 152-148-0026.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yan, Q., Xu, M., Wong, D.W.K. et al. Automatic fibroatheroma identification in intravascular optical coherence tomography volumes. J Ambient Intell Human Comput 14, 15477–15483 (2023). https://doi.org/10.1007/s12652-019-01549-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01549-y