CLASSIFICATION OF INTRAVASCULAR ULTRASOUND IMAGES BASED ON NON-NEGATIVE MATRIX FACTORIZATION FEATURES AND MAXIMUM LIKELIHOOD CLASSIFIER
DOI:
https://doi.org/10.29284/ijasis.4.1.2018.16-22Keywords:
Intravascular ultrasound image classification, frost filtering, non-negative matrix factorization, maximum likelihood classifierAbstract
The amount of plaque in coronary arteries in any particular point is identified by the IntraVascular UltraSound (IVUS) images. The classification of IVUS images is very important to diagnose various coronary artery diseases. In this study, the classification of IVUS images based on Non-negative Matrix Factorization (NMF) technique and Maximum Likelihood Classifier (MLC) is presented. Initially, the IVUS images are given to frost filter to remove speckle noise as the imaging technique uses ultrasound waves. Then, NMF technique is employed to extract the features and stored in database. Then MLC is used for classification of IVUS images for both normal and abnormal categories. The IVUS Image Classification (IIC) system obtains 98% classification accuracy by using NMF features and MLC classification.
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References
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K.L. Caballero, J. Barajas, O. Pujol, O. Rodriguez, and P. Radeva, “Using reconstructed ivus images for coronary plaque classification”, International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, pp. 2167-2170.
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S. Dehnavi, M.P. Babu, M. Yazchi, and M. Basij, “Automatic soft and hard plaque detection in IVUS images: A textural approach”, Information & Communication Technologies, 2013, pp. 214-219.
D. Haiyan, and L. Hong, “Texture analysis and characteristic identification about plaque tissues of IVUS”, International Conference on Computational and Information Sciences, 2010, pp. 873-876.
D. China, P. Mitra, C. Chakraborty, and K.M. Mandana, “Wavelet based non local means filter for despeckling of intravascular ultrasound image”, International Conference on Advances in Computing, Communications and Informatics, 2015, pp. 1361-1365.
M. Basij, P. Moallem, M. Yazdchi, and S. Mohammadi, “Automatic shadow detection in intra vascular ultrasound images using adaptive thresholding”, International Conference on Systems, Man, and Cybernetics, 2012, pp. 2173-2177.
X. Zhang, C.R. McKay, and M. Sonka, “Tissue characterization in intravascular ultrasound images”, IEEE Transactions on Medical Imaging, Vol.17, No.6, 1998, pp. 889-899.
M. Alberti, S. Balocco, C. Gatta, F. Ciompi, O. Pujol, J. Silva, X. Carrillo, and P. Radeva, “Automatic bifurcation detection in coronary IVUS sequences”, IEEE transactions on biomedical engineering, Vol.59, No.4, 2012, pp.1022-1031.
C.S. Josephus, and S. Remya, “Multilayered contrast limited adaptive histogram equalization using frost filter”, IEEE Recent Advances in Intelligent Computational Systems, 2011, pp. 638-641.
G. Chen, X. LIVUS, and Z. Zhou, “Modified frost speckle filter based on anisotropic diffusion”, IET International Conference on Radar Systems, 2007, pp. 1-4.
Y. Bao, Z. Qibing, and H. Min, “Image identification of glass defects based on Non-Negative Matrix Factorization and Sparse Representation Classification”, Chinese Control and Decision Conference, 2012, pp. 3225-3229.
B. Gao, W.L. Woo, and S.S. Dlay, “Adaptive sparsity non-negative matrix factorization for single-channel source separation”, IEEE journal of selected topics in signal processing, Vol. 5, No. 5, 2011, 989-1001.
K.S. Thyagarajan, T. Nguyen, and C.E. Persons, “A maximum likelihood approach to texture classification using wavelet transform”, International Conference on Image Processing, 1994, pp. 640-644.
X. Tan, X. Su, and Q. Qian, “The classification of SSH tunneled traffic using maximum likelihood classifier”, International Conference on Electronics, Communications and Control, 2011, pp. 2347-2350.
V.G. Giannoglou, D.G. Stavrakoudis, and J.B. Theocharis, “IVUS-based characterization of atherosclerotic plaques using feature selection and SVM classification”, International Conference on Bioinformatics & Bioengineering, 2012, pp. 715-720.
K.L. Caballero, J. Barajas, O. Pujol, O. Rodriguez, and P. Radeva, “Using reconstructed ivus images for coronary plaque classification”, International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, pp. 2167-2170.
P. Brathwaite, A. Nagaraj, B. Kane, D.D. McPherson, and E.L. Dove, “Automatic classification and differentiation of atherosclerotic lesions in swine using IVUS and texture features”, Computers in Cardiology, 2002, pp. 109-112.
X. Zhang, S.C. DeJong, C.R. McKay, S.M. Collins, and M. Sonka, “Automated characterization of plaque composition from intravascular ultrasound images”, Computers in Cardiology, 1996, pp. 649-652.
V.G. Giannoglou, D.G. Stavrakoudis, J.B. Theocharis, and V. Petridis, “Genetic fuzzy rule-based classification systems for tissue characterization of intravascular ultrasound images”, International Conference on Fuzzy Systems, 2012, pp. 1-8.
S. Dehnavi, M.P. Babu, M. Yazchi, and M. Basij, “Automatic soft and hard plaque detection in IVUS images: A textural approach”, Information & Communication Technologies, 2013, pp. 214-219.
D. Haiyan, and L. Hong, “Texture analysis and characteristic identification about plaque tissues of IVUS”, International Conference on Computational and Information Sciences, 2010, pp. 873-876.
D. China, P. Mitra, C. Chakraborty, and K.M. Mandana, “Wavelet based non local means filter for despeckling of intravascular ultrasound image”, International Conference on Advances in Computing, Communications and Informatics, 2015, pp. 1361-1365.
M. Basij, P. Moallem, M. Yazdchi, and S. Mohammadi, “Automatic shadow detection in intra vascular ultrasound images using adaptive thresholding”, International Conference on Systems, Man, and Cybernetics, 2012, pp. 2173-2177.
X. Zhang, C.R. McKay, and M. Sonka, “Tissue characterization in intravascular ultrasound images”, IEEE Transactions on Medical Imaging, Vol.17, No.6, 1998, pp. 889-899.
M. Alberti, S. Balocco, C. Gatta, F. Ciompi, O. Pujol, J. Silva, X. Carrillo, and P. Radeva, “Automatic bifurcation detection in coronary IVUS sequences”, IEEE transactions on biomedical engineering, Vol.59, No.4, 2012, pp.1022-1031.
C.S. Josephus, and S. Remya, “Multilayered contrast limited adaptive histogram equalization using frost filter”, IEEE Recent Advances in Intelligent Computational Systems, 2011, pp. 638-641.
G. Chen, X. LIVUS, and Z. Zhou, “Modified frost speckle filter based on anisotropic diffusion”, IET International Conference on Radar Systems, 2007, pp. 1-4.
Y. Bao, Z. Qibing, and H. Min, “Image identification of glass defects based on Non-Negative Matrix Factorization and Sparse Representation Classification”, Chinese Control and Decision Conference, 2012, pp. 3225-3229.
B. Gao, W.L. Woo, and S.S. Dlay, “Adaptive sparsity non-negative matrix factorization for single-channel source separation”, IEEE journal of selected topics in signal processing, Vol. 5, No. 5, 2011, 989-1001.
K.S. Thyagarajan, T. Nguyen, and C.E. Persons, “A maximum likelihood approach to texture classification using wavelet transform”, International Conference on Image Processing, 1994, pp. 640-644.
X. Tan, X. Su, and Q. Qian, “The classification of SSH tunneled traffic using maximum likelihood classifier”, International Conference on Electronics, Communications and Control, 2011, pp. 2347-2350.
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2018-06-27
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How to Cite
[1]
R. A, “CLASSIFICATION OF INTRAVASCULAR ULTRASOUND IMAGES BASED ON NON-NEGATIVE MATRIX FACTORIZATION FEATURES AND MAXIMUM LIKELIHOOD CLASSIFIER ”, IJASIS, vol. 4, no. 1, pp. 16–22, Jun. 2018, doi: 10.29284/ijasis.4.1.2018.16-22.
