Abstract
Atherosclerosis is a type of cardiovascular disease which may cause stroke. It is due to the deposition of fatty plaque in the artery walls resulting in the reduction of elasticity gradually and hence restricting the blood flow to the heart. Hence, an early prediction of carotid plaque deposition is important, as it can save lives. This paper proposes a novel data mining framework for the assessment of atherosclerosis in its early stage using ultrasound images. In this work, we are using 1353 symptomatic and 420 asymptomatic carotid plaque ultrasound images. Our proposed method classifies the symptomatic and asymptomatic carotid plaques using bidimensional empirical mode decomposition (BEMD) and entropy features. The unbalanced data samples are compensated using adaptive synthetic sampling (ADASYN), and the developed method yielded a promising accuracy of 91.43%, sensitivity of 97.26%, and specificity of 83.22% using fourteen features. Hence, the proposed method can be used as an assisting tool during the regular screening of carotid arteries in hospitals.
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References
WHO, CVD. [Online]. Available: http://www.who.int/cardiovascular_diseases/about_cvd/en/
Carr S, Farb A, Pearce WH, Virmani R, Yao JS (1996) Atherosclerotic plaque rupture in symptomatic carotid artery stenosis. J Vasc Surg 23(5):755–765. https://doi.org/10.1016/S0741-5214(96)70237-9
Brott TG, Hobson RW, Howard G, Roubin GS, Clark WM, Brooks W, Mackey A, Hill MD, Leimgruber PP, Sheffet AJ, Howard VJ, Moore WS, Voeks JH, Hopkins LN, Cutlip DE, Cohen DJ, Popma JJ, Ferguson RD, Cohen SN, Blackshear JL, Silver FL, Mohr JP, Lal BK, Meschia JF (2010) Stenting versus endarterectomy for treatment of carotid-artery stenosis. N Engl J Med 363(1):11–23. https://doi.org/10.1056/NEJMoa0912321
European Carotid Surgery Trialists’ Collaborative Group (1998) Randomised trial of endarterectomy for recently symptomatic carotid stenosis: final results of the MRC European Carotid Surgery Trial (ECST). Lancet 351(9113):1379–1387
Polak JF, Shemanski L, O’Leary DH, Lefkowitz D, Price TR, Savage PJ, Brant WE, Reid C (1998) Hypoechoic plaque at US of the carotid artery: an independent risk factor for incident stroke in adults aged 65 years or older. Cardiovasc Health Study Radiol 208(3):649–654. https://doi.org/10.1148/radiology.208.3.9722841
Inzitari D, Eliasziw M, Gates P, Sharpe BL, Chan RK, Meldrum HE, Barnett HJ (2000) The causes and risk of stroke in patients with asymptomatic internal-carotid-artery stenosis. North American Symptomatic Carotid Endarterectomy Trial Collaborators. N Engl J Med 342(23):1693–1700
Faust O, Acharya UR, Sudarshan VK, San Tan R, Yeong CH, Molinari F, Ng KH (2017) Computer aided diagnosis of coronary artery disease, myocardial infarction and carotid atherosclerosis using ultrasound images: a review. Phys Med 33:1–15. https://doi.org/10.1016/j.ejmp.2016.12.005
UR Acharya OF, Molinari F, Saba L, Nicolaides A, Suri JS (2012) An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans. IEEE Trans Instrum Meas 61(4):1045–1053. https://doi.org/10.1109/TIM.2011.2174897
Szekely N, Toth N, Pataki B (2006) A hybrid system for detecting masses in mammographic images. IEEE Trans Instrum Meas 55(3):944–952. https://doi.org/10.1109/TIM.2006.870104
Stoitsis J, Golemati S, Nikita KS (2006) A modular software system to assist interpretation of medical images—application to vascular ultrasound images. IEEE Trans Instrum Meas 55(6):1944–1952. https://doi.org/10.1109/TIM.2006.884348
Suri JS, Kathuria C, Molinari F (2011) Atherosclerosis disease management. Springer-Verlag, New York. https://doi.org/10.1007/978-1-4419-7222-4
Kyriacou EC, Pattichis C, Pattichis M, Loizou C, Christodoulou C, Kakkos SK, Nicolaides A (2010) A review of noninvasive ultrasound image processing methods in the analysis of carotid plaque morphology for the assessment of stroke risk. IEEE Trans Inf Technol Biomed 14(4):1027–1038. https://doi.org/10.1109/TITB.2010.2047649
Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A (2003) Texture based classification of atherosclerotic carotid plaques. IEEE Trans Med Imag 22(7):902–912. https://doi.org/10.1109/TMI.2003.815066
Kyriacou E, Pattichis MS, Christodoulou CI, Pattichis CS, Kakkos S, Griffing N, Nicolaides A (2005) Ultrasound imaging in the analysis of carotid plaque morphology for the assessment of stroke. Stud Health Technol Inform 113:241–275
Kyriacou E, Pattichis M, Pattichis CS, Mavrommatis A, Christodoulou CI, Kakkos S, Nicolaides A (2009) Classification of atherosclerotic carotid plaques using morphological analysis on ultrasound images. J Appl Intell 30(1):3–23. https://doi.org/10.1007/s10489-007-0072-0
Stoitsis J, Golemati S, Nikita KS, Nicolaides AN (2004) Characterization of carotid atherosclerosis based on motion and texture features and clustering using fuzzy c-means, in Conf Proc IEEE Eng Med Biol Soc, pp 1407–1410
Acharya UR, Faust O, Alvin AP, Vinitha Sree S, Molinari F, Saba L, Nicolaides A, Suri JS (2012) Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J Med Syst 36(3):1861–1871. https://doi.org/10.1007/s10916-010-9645-2
Mougiakakou SG, Golemati S, Gousias I, Nicolaides A, Nikita K (2007) Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, Laws’ texture and neural networks. Ultrasound Med Biol 33(1):26–36. https://doi.org/10.1016/j.ultrasmedbio.2006.07.032
Seabra J, Pedro LM, Fernandes FE, Sanches J (2010) Ultrasonographic characterization and identification of symptomatic carotid plaques, in Proc. 32th Annu. Int. Conf. IEEE EMBS, pp 6110–6113
Tsiaparas NN, Golemati S, Andreadis I, Stoitsis JS, Valavanis I, Nikita KS (2011) Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed 15(1):130–137. https://doi.org/10.1109/TITB.2010.2091511
Carter-Monroe N, Yazdani SK, Ladich E, Kolodgie FD, Virmani R (2011) Introduction to the pathology of carotid atherosclerosis: histologic classification and imaging correlation, in Atherosclerosis Disease Management. Springer-Verlag, New York, pt. 1, pp 3–35
Griffin MB, Kyriacou E, Pattichis C, Bond D, Kakkos SK, Sabetai M, Geroulakos G, Georgiou N, Dore CJ, Nicolaides A (2010) Juxtaluminal hypoechoic area in ultrasonic images of carotid plaques and hemispheric symptoms. J Vasc Surg 52(1):69–76. https://doi.org/10.1016/j.jvs.2010.02.265
Lekadir K, Galimzianova A, Betriu A, del Mar Vila M, Igual L, Rubin DL, Fernandez E, Radeva P, Napel S (2017) A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform 21(1):48–55. https://doi.org/10.1109/JBHI.2016.2631401
Hamid H, Najmeh S, Salehi SMM (2015) Using morphological transforms to enhance the contrast of medical images. Egypt J Radiol Nucl Med 46(2):481–489
Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160. https://doi.org/10.1109/TASSP.1981.1163711
Pizer SM, Amburn EP, Austin JD, Cromarrtie R, Geselowitz A, Greer T, Romeny H, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vision, Graph, Image Process 39(3):355–368. https://doi.org/10.1016/S0734-189X(87)80186-X
Wang X, Wong BS, Guan TC (2004) Image enhancement for radiography inspection, Proc. SPIE 5852, Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, vol. 462
Nunes JC, Bouaoune Y, Delechelle E, Niang O, Bunel P (2003) Image analysis by bidimensional empirical mode decomposition. Image Vis Comput 21(12):1019–1026. https://doi.org/10.1016/S0262-8856(03)00094-5
Nunes JC, Guyot S, Deléchelle E (2005) Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Mach Vision Appl 16(3):177–188
Shao Y, Celenk M (2001) Higher-order spectra (HOS) invariants for shape recognition. Pattern Recogn 34(11):2097–2113. https://doi.org/10.1016/S0031-3203(00)00148-5
Acharya UR, Dua S, Du X, Vinitha Sree S, Chua CK (2011) Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inf Technol Biomed 15(3)
Shannon CE (1948) A mathematical theory of communication. Bell Syst Technol J 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Renyi A (1961) On measures of entropy and information. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, vol. 1, pp 547–561
Chen WT, Wang ZZ, Xie HB, Yu WX (2007) Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans Neural Syst and Rehabil Eng 15(2):266–272. https://doi.org/10.1109/TNSRE.2007.897025
Kapur JN (1968) Information of order α and type β. Proc Ind Acad Sci 68:65–75
Ghosh M, Chakraborty C, Ray AK (2013) Yager's measure based fuzzy divergence for microscopic color image segmentation, in Indian Conference on Medical Informatics and Telemedicine, Kharagpur, pp 13–16
Yin P-Y (2002) Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization. Signal Process 82(7):993–1006. https://doi.org/10.1016/S0165-1684(02)00203-7
He H, Yang B, Garcia EA, Li S ADASYN: adaptive synthetic sampling approach for imbalanced learning, Proceedings of the International Joint Conference on Neural Networks,{IJCNN} 2008, part of the IEEE World Congress on Computational Intelligence,{WCCI} 2008, Hong Kong, China, pp 1–6
He X, Cai D, Yan S, Zhang H-J (2005) Neighborhood preserving embedding, Proceedings of the Tenth IEEE International Conference on Computer Vision—vol. 2, pp 1208—1213
Gudigar A, Chokkadi S, Raghavendra U, Acharya UR (2017) Multiple thresholding and subspace based approach for detection and recognition of traffic sign. Multimed Tools Appl 76(5):6973–6991. https://doi.org/10.1007/s11042-016-3321-6
Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37. https://doi.org/10.1109/34.824819
Kailath T (1967) The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans Commun Technol 15(1):52–60. https://doi.org/10.1109/TCOM.1967.1089532
Student t-test, Last Accessed: 26.02.2017. [Online]. Available: http://www.physics.csbsju.edu/stats/t-test.html
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83. https://doi.org/10.2307/3001968
Sundar N, Lipsitz SR, Fitzmaurice GM, Sinha D, Ibrahim JG, Haas J, Gellad W (2012) An extension of the Wilcoxon rank-sum test for complex sample survey data. J R Stat Soc: Ser C: Appl Stat 61(4):653–664
Obuchowski NA (2003) Receiver operating characteristic curves and their use in radiology. Radiology 229(1):3–8. https://doi.org/10.1148/radiol.2291010898
Dash M, Liu H (1999) Handling large unsupervised data via dimensionality reduction, In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery
Abe N, Kudo M (2005) Entropy criterion for classifier-independent feature selection, in knowledge-based intelligent information and engineering systems, ser. Lecture notes in computer science. Springer Berlin Heidelberg, vol. 3684, pp 689–695
Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans Electron Comput 14(3):326–334
Larose DT (2004) Discovering knowledge in data: an introduction to data mining. Wiley-Interscience, Hoboken. https://doi.org/10.1002/0471687545
Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118. https://doi.org/10.1016/0893-6080(90)90049-Q
Kecman DV (2001) Learning and soft computing. MIT Press, Cambridge
Acharya UR, Fujita H, Bhat S, Raghavendra U, Gudigar A, Molinari F, Vijayananthan A, Ng KH (2016) Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images. Inf Fusion 29:32–39. https://doi.org/10.1016/j.inffus.2015.09.006
Raghavendra U, Acharya UR, Gudigar A, Shetty R, Krishnananda N, Pai U, Samanth J, Nayak C (2017) Automated screening of congestive heart failure using variational mode decomposition and texture features extracted from ultrasound images, Neural Computing and Applications, Springer. https://doi.org/10.1007/s00521-017-2839-5
Seabra J, Ciompi F, Pujol O, Mauri J, Radeva P, Sanchez J (2011) Rayleigh mixture model for plaque characterization in intravascular ultrasound. IEEE Trans Biomed Eng 58(5):1314–1324. https://doi.org/10.1109/TBME.2011.2106498
Tsiaparas NN, Golemati S, Andreadis I, Stoitsis J, Valavanis I, Nikita KS (2012) Assessment of carotid atherosclerosis from B-mode ultrasound images using directional multiscale texture features. Meas Sci Technol 23(11):114004. https://doi.org/10.1088/0957-0233/23/11/114004
Acharya UR, Vinitha Sree S, Mookiah MRK, Molinari F, Saba L, Yee S, Ho S, Ahuja AT, Ho SC, Nicolaides A, Suri JS (2012) Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. Ultrasound Med Biol 38(6):899–915. https://doi.org/10.1016/j.ultrasmedbio.2012.01.015
Acharya UR, Mookiah MRK, Vinitha Sree S, Sanches J, Shafique S, Nicolaides A, Pedro LM, Suri JS (2013) Plaque tissue characterization and classification in ultrasound carotid scans: a paradigm for vascular feature amalgamation. IEEE Trans Instrum Meas 62(2):392–400. https://doi.org/10.1109/TIM.2012.2217651
Acharya UR, Mookiah MRK, Vinitha Sree S, Afonso D, Sanches J, Shafique S, Nicolaides A, Pedro LM, Fernandes e Fernandes J, Suri JS (2013) Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput 51(5):513–523. https://doi.org/10.1007/s11517-012-1019-0
Acharya UR, Faust O, Alvin APC, Krishnamurthi G, Seabra JCR, Sanches J, Suri JS (2013) Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. Comput Methods Prog Biomed 110(1):66–75. https://doi.org/10.1016/j.cmpb.2012.09.008
Afonso D, Seabra J, Pedro LM, Fernandes JF, Sanches JM (2015) An ultrasonographic risk score for detecting symptomatic carotid atherosclerotic plaques. IEEE J Biomed Health Inf 19(4):1505–1513. https://doi.org/10.1109/JBHI.2014.2359236
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All the images were acquired after the subjects signed an informed consent about the treatment of their data. The use of the images was approved by the institutional review board of the Gradenigo Hospital.
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Molinari, F., Raghavendra, U., Gudigar, A. et al. An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique. Med Biol Eng Comput 56, 1579–1593 (2018). https://doi.org/10.1007/s11517-018-1792-5
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DOI: https://doi.org/10.1007/s11517-018-1792-5