Computer Science ›› 2018, Vol. 45 ›› Issue (5): 260-265.doi: 10.11896/j.issn.1002-137X.2018.05.045

Special Issue: Medical Imaging

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Automatic Characterization Study of Atherothrombotic Plaques Based on Intravascular Ultrasound Images

HUANG Zhi-jie, WANG Yi-nong and WANG Qing   

  • Online:2018-05-15 Published:2018-07-25

Abstract: In order to obtain the accurate information of atherothrombotic plaques in the cardiovasculars and assist the diagnosis and classification of the plaque tissues,this study applied apply a machine learning method to automatically characterize the atherothrombotic plaques in intravascular ultrasound(IVUS) grayscale images.In this study,207 plaque samples in the IVUS images were collected from 10 patients with cardiovascular disease in the hospital.Firstly,the size of a sliding patch is determined and then its centre pixel traverses in the plaque area.The values of the mean and entropy are calculated.Ten features of the patch along 4 directions are respectively obtained by using co-occurrence matrix method.Secondly,more texture features of the plaque region in the IVUS images are obtained by using Gabor filter and local binary pattern(LBP) methods.Finally,the classifiers of Liblinear,random forests and Harmonic to Minimum-Ge-neralized LVQ(H2M-GLVQ) are used to classify these pixels in the plaque tissues based on the features obtained through reducing dimension by using principal component analysis(PCA).The manual characterization by an experien-ced physician is considered as the gold standard.Results of the proposed automatic characterization method show the general identification rates of classifiers of random forests and H2M-GLVQ are over 80%.Compared with other two classifiers,the identification rate of random forests is relatively higher,i.e.89.04%,80.23% and 73.77% respectively for fibrotic,lipidic and calcified plaque tissues.

Key words: Intravascular ultrasound image,Automatic characterization,Texture features,Classification judgment

[1] HANSSON G K.Inflammation,atherosclerosis,and coronaryartery disease[J].The New England Journal of Medicine,2005,352(16):1685-1695.
[2] VIRMANI R,KOLODGIE F D,BURKE A P,et al.Atherosclerotic plaque progression and vulnerability to rupture-angiogenesis as a source of intraplaque hemorrhage[J].Arteriosclerosis Thrombosis and Vascular Biology,2005,25(10):2054-2061.
[3] JEERS S,NARBUTE I,ERGLIS A.Use of intravascular imaging in managing coronary artery disease[J].World Journal of Cardiology,2014,6(6):393-404.
[4] NAIR A,KUBAN B D,OBUCHOWSKI N,et al.Assessingspectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data[J].Ultrasound in Medicine Biology,2001,27(10):1319-1331.
[5] KATOUZIAN A,SATHYANARAYANAN S,BASERI B,et al.Challenges in atherosclerotic plaque characterization with intravascular ultrasound:From data collection to classification[J].IEEE Transactions on Information Technology in Biomedicine:A Publication of IEEE Engineering in Medicine and Biology Society,2008,12(3):315-327.
[6] NAIR A,KUBAN B D,TUZCU E M,et al.Coronary plaque classification with intravascular ultrasound radiofrequency data analysis[J].Circulation,2002,106(17):2200-2206.
[7] MAURICE R L,FROMAGEAU J,BRUSSEAU L,et al.On the potential of the lagrangian estimator for endovascular ultrasound elastography:In vivo human coronary artery study[J].Ultrasound in Medicine and Biology,2007,33(8):1199-1205.
[8] ARAKI T,IKEDA N,SHUKLA D,et al.PCA-based pollingstrategy in machine learning framework for coronary artery di-sease risk assessment in intravascular ultrasound:A link between carotid and coronary grayscale plaque morphology[J].Computer Methods and Programs in Biomedicine,2016,128:137-158.
[9] LO VERCIO L,ORLANDO J I,DEL FRESNO M,et al.Assessment of image features for vessel wall segmentation in intravascular ultrasound images[J].International Journal of Computer Assisted Radiology and Surgery,2016,11(8):1397-1407.
[10] ATHANASIOU L S,KARVELIS P S,TSAKANIKAS V D,et al.A novel semiautomated atherosclerotic plaque characteri-zation method using grayscale intravascular ultrasound images:Comparison with virtual histology[J].IEEE Transactions on Information Technology in Biomedicine,2012,16(3):391-400.
[11] JABASON E.Performance analysis of contourlet features with SVM classifier for the characterization of atheromatous plaque in intravascular ultrasound images[J].International Journal of Engineering Research & Applications,2014,4(3):35-42.
[12] ZHANG J,TAN T,MA L.Invariant texture segmentation via circular gabor filters[C]∥IEEE International Conference on Pattern Recognition.2002:901-904.
[13] PIETIK O T,INEN M,et al.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2002,24(7):971-987.
[14] GIANNOGLOU V G,STAVRAKOUDIS D G,T HEOCHAIRS J B.IVUS-based characterization of atherosclerotic plaques using feature selection and SVM classification[C]∥IEEE International Conference on Bioinformatics & Bioengineering.2012:715-720.
[15] FAN R E,CHANG K W,HSIEH C J,et al.Liblinear:A library for large linear classification[J].Journal of Machine Learning Research,2008,9(9):1871-1874.
[16] BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32.
[17] QIN A K,SUGANTHAN P N.Rapid and brief communication:Initialization insensitive LVQ algorithm based on cost-function adaptation[J].Pattern Recognition,2005,38(5):773-776.
[18] Sato A.Generalized learning vector quantization[C]∥Confe-rence on Neural Information Processing Systems.1996:423-429.
[19] SUN Z,WANG L X,ZHOU Y.Automated tissue characterization of intravascular ultrasound gray-scale images[J].Journal of Biomedical Engineering,2016,33(2):287-294.(in Chinese) 孙正,王立欣,周雅.血管内超声灰阶图像的自动组织标定[J].生物医学工程学杂志,2016,33(2):287-294.
[20] HARALICK R M.Texture features for image classification[J].IEEE Transactions on Systems Man & Cybernetics,1990,3(6):610-621.
[21] SCHOENHAGEN P,CROWE T,NICHOLL S,et al.IVUSmake easy[M].America,Paul G.Informa Healthcare,2008.
[22] QIN A K,SUGANTHAN P N,LIANG J J.A new generalized LVQ algorithm via harmonic to minimum distance measure transition[C]∥IEEE International Conference on Systems,Man &Cybernetics.2004:4821-4825.

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