COMPARATIVE STUDY OF CLUSTERING ALGORITHMS IN ORDER TO VIRTUAL HISTOLOGY (VH) IMAGE SEGMENTATION

Authors

  • Zahra Rezaei Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Mohd Daud Kasmuni Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Ali Selamat Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Mohd Shafry Mohd Rahim Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Golnoush Abaei Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Mohammed Rafiq Abdul Kadir Faculty of Biomedical Engineering & Health Sciences, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v75.4994

Keywords:

Atherosclerosis plaque, VH-IVUS, TCFA, segmentation, clustering

Abstract

Atherosclerosis is the deadliest type of heart disease caused by soft or “vulnerable†plaque (VP) formation in the coronary arteries.  Recently, Virtual Histology (VH) has been proposed based on spectral analysis of Intravascular Ultrasound (IVUS) provides color code of coronary tissue maps. Based on pathophysiological studies, obtaining information about existence and extension of confluent pool’s component inside plaque is important. In addition, plaque components’ localization respect to the luminal border has major role in determining plaque vulnerability and plaque–stent interaction. Computational methods were applied to prognostic the pattern's structure of each component inside the plaque. The first step for post-processing of VH methodology to get further information of geometrical features is segmentation or decomposition. The medical imaging segmentation field has developed to assist cardiologist and radiologists and reduce human error in recent years as well. To perform color image clustering, several strategies can be applied which include traditional hierarchical and nonhierarchical. In this paper, we applied and compared four nonhierarchical clustering methods consists of Fuzzy C-means (FCM), Intuitionistic Fuzzy C-means (IFCM), K-means and SOM artificial neural networks in order to automate segmentation of the VH-IVUS images.  

References

Qian, Z., G. Vazquez and S. Voros. 2011. Intermodal Registration of CTA and IVUS-VH, and Its Application on CTA-based Plaque Composition Analysis. Biomedical Imaging. 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro. Chicago, IL. 30 March - 2 Apr. 2011. 419-423.

AlSheikh-Ali, A.A., G.D. Kitsios, E.M. Balk, J. Lau and S. Ip. 2010. The Vulnerable Atherosclerotic Plaque: Scope of the Literature. Annals of Internal Medicine. 153(6): 387-396.

Selvathi, D., N. Emimal and H. Selvaraj. 2012. Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images using Neuro Fuzzy Classifier. International Journal of Electronics and Telecommunications. 58(4): 425-432.

Mishra, T., C. Mishra and B. Das. 2013. An Approach to the Classification, Diagnosis and Management of Vulnerable Plaque. Journal of Indian College of Cardiology. 3(2): 57-66.

Garcia-Garcia, H. M., M. A. Costa and P. W. Serruys. 2010. Imaging of Coronary Atherosclerosis: Intravascular Ultrasound. European Heart Journal. 31(20): 2456-2469.

Logeswari, T. and M. Karnan. 2010. Hybrid Self Organizing Map for Improved Implementation of Brain MRI Segmentation. ICSAP'10. International Conference on Signal Acquisition and Processing. Bangalore. 9-10 Feb. 2010. 248-252.

Sales, F. J. R., J. Falcao, P. Lemos, S. Furuie, R. Cabral and R. Silva. 2008. Post-Processing Analysis of Virtual Histology Images-A New Tool for Intra-Plaque Component Assessment. IV Latin American Congress on Biomedical Engineering 2007, Bioengineering Solutions for Latin America Health IFMBE Proceedings. 18: 377-380.

Huang, J. J., G.H. Tzeng and C. S. Ong. 2007. Marketing Segmentation using Support Vector Clustering. Expert Systems with Applications. 32(2): 313-317.

Rickard, H. E., G. D. Tourassi, N. Eltonsy and A. S. Elmaghraby. 2004. Breast Segmentation in Screening Mammograms using Multiscale Analysis and Self-Organizing Maps. Conference Proceedings IEEE on Engineering in Medicine and Biology Society. 3: 1786-1789.

Chang, P. L. and W. G. Teng. 2007. Exploiting the Self-Organizing Map for Medical Image Segmentation. Twentieth IEEE International Symposium on Computer-based Medical Systems. Maribor. 20-22 June 2007. 281-288.

Iwamoto, T., Y. Saijo, A. Tanaka, S. Li and M. Yoshizawa. 2009. Radio Frequency Signal Analysis for Tissue Characterization of Coronary Artery: In Vivo Intravascular Ultrasound Study. Akiyama I. (Ed.). Springer Netherlands. 147-152.

Karnan, M. and T. Logeswari. 2010. Hybrid Self Organizing Map for Improved Implementation of Brain MRI Segmentation. ICSAP'10. International Conference on Signal Acquisition and Processing. Bangalore. 9-10 Feb. 248-252. (*same as [6]).

Dongxiang C., and C. Weizhong. 2010. A Hybrid Clustering Method for Automatic Medical Image Segmentation. Journal of Computational Information Systems. 6(6): 1983-1993.

Vijayakumar, C., G. Damayanti, R. Pant and C. Sreedhar. 2007. Segmentation and Grading of Brain Tumors on Apparent Diffusion Coefficient Images using Self-Organizing Maps. Computerized Medical Imaging and Graphics. 31(7): 473-484.

Grajciarova, L., J. Mares, P. Dvorak and A. Prochazka. 2012. Biomedical Image Analysis using Self-Organizing Maps.

Krinidis, S. and V. Chatzis. 2010. A Robust Fuzzy Local Information C-Means Clustering Algorithm. IEEE Transactions on Image Processing. 19(5): 1328-1337.

Mingoti, S. A. and J. O. Lima. 2006. Comparing SOM Neural Network with Fuzzy C-Means, K-Means and Traditional Hierarchical Clustering Algorithms. European Journal of Operational Research. 174(3): 1742-1759.

Giannoglou, V.G., D.G. Stavrakoudis and J.B. Theocharis. 2012. IVUS-based Characterization of Atherosclerotic Plaques using Feature Selection and SVM Classification. 2012 IEEE 12th International Conference on Bioinformatics and Bioengineering (BIBE). Larnaca. 11-13 Nov. 2012. 715-720.

Lazrag, H., K. Aloui and M.S. Naceur. 2013. Automatic Segmentation of Lumen in Intravascular Ultrasound Images using Fuzzy Clustering and Active Contours. International Conference on Control, Engineering and Information Technology (CEIT’13), Proceedings Engineering and Technology. 1: 58-63.

Dehnavi, S. M., M. Babu, M. Yazchi and M. Basij. 2013. Automatic Soft and Hard Plaque Detection in IVUS Images: A Textural Approach. 2013 IEEE Conference on Information and Communication Technologies (ICT). Jeju Island. 11-12 Apr. 2013. 214-219.

Biswas, D., P. Das, P. Maji, N. Dey and S.S. Chaudhuri. 2013. Visible Watermarking Within the Region of Non-Interest of Medical Images based on Fuzzy C-Means And Harris Corner Detection. Third International Conference on Computer Science, Engineering and Applications (ICCSEA 2013). Delhi, India. 24-26 May 2013. 161-168.

Chaira, T. 2011. A Novel Intuitionistic Fuzzy C Means Clustering Algorithm and Its Application to Medical Images. Applied Soft Computing. 11(2): 1711-1717.

Juang, L.H. and Wu M.N. 2010. MRI Brain Lesion Image Detection based on Color-Converted K-Means Clustering Segmentation. Measurement. 43(7): 941-949.

Katouzian, A., E. Angelini, S. Carlier, J. Suri, N. Navab and A. Laine. 2012. A State of the Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images. IEEE Transactions on Information Technology in Biomedicine. 16(5): 823-834.

Garcia-Garcia, H. M., M. A. Costa and P.W. Serruys. 2010. Imaging of Coronary Atherosclerosis: Intravascular Ultrasound. European Heart Journal. 31(20): 2456-2469. (*same as [5])

Liang, M., A. Puri and G. Devlin. 2011. The Vulnerable Plaque: The Real Villain in Acute Coronary Syndromes. The Open Cardiovascular Medicine Journal. 5: 123-129.

Konig, A., M.P. Margolis, R.Virmani, D.Holmes and V. Klauss. 2008. Technology Insight: In Vivo Coronary Plaque Classification by Intravascular Ultrasonography Radiofrequency Analysis. Nature Clinical Practice Cardiovascular Medicine. 5(4): 219-229.

Konig, A., Q. Bleif, J. Rieber, P. Jung,T.M. Schiele, H.Y. Sohn, M. Leibig, U. Siebert and V. Klauss. 2010. Intravascular Ultrasound Radiofrequency Analysis of the Lesion Segment Profile in ACS Patients. Clinical Research in Cardiology. 99(2): 83-91.

Vachkov, G., E. Uchino and S. Nakao. 2012. Moving Window-based Similarity Analysis and Its Application to Tissue Characterization of Coronary Arteries. Proceedings of the International Multi Conference of Engineers and Computer Scientists (IMECS 2012). Hong Kong. 14-16 March 2012.

Zhu, X., P. Zhang, J. Shao, Y. Cheng, Y. Zhang and J. Bai. 2011. A Snake-based Method for Segmentation of Intravascular Ultrasound Images and Its Vivo Validation. Ultrasonics 2011. 51(2): 181-189.

Lezoray O. and C. Charrier. 2009. Color Image Segmentation using Morphological Clustering and Fusion with Automatic Scale Selection. Pattern Recognition Letters. 30(4): 397-406.

Sowmya, B. and B.S. Rani. 2011. Colour Image Segmentation using Fuzzy Clustering Techniques and Competitive Neural Network. Applied Soft Computing. 11(3): 3170-3178.

Kono, K., H. Fujii, N. Miyoshi, H. Kawamori, J. Shite, K.I. Hirata and M. Fukagawa. 2011. Coronary Plaque Morphology using Virtual Histology–Intravascular Ultrasound Analysis in Hemodialysis Patients. Therapeutic Apheresis and Dialysis. 15(1): 44-50.

Rad, A. E., M. S. M. Rahim, A. Rehman, A. Altameen and T. Saba. 2013. Evaluation of Current Dental Radiographs Segmentation Approaches in Computer-Aided Applications. IETE Technical Review. 30(3): 210-222.

Kaur, P. 2012. Novel Intuitionistic Fuzzy C-Means Clustering for Linearly and Nonlinearly Separable Data. WSEAS Transactions on Computers. 11(3): 65-76.

Kaur, P. 2012. Novel Intuitionistic Fuzzy C-Means Clustering for Linearly and Nonlinearly Separable Data. WSEAS Transactions on Computers. 11(3): 65-76. (*same as [35]).

Rad, A.E., M.S.M. Rahim, R. Kumoi and A. Nourouzi. Dental X-Ray Image Segmentation and Multiple Feature Extraction. Information Technology and Computer Science. 2(2012): 188-197.

Chitade, A. Z. and S. K. Katiyar. 2010. Colour based Image Segmentation using k-Means Clustering. International Journal of Engineering Science and Technology. 2: 5319-5325.

Shin, E. S., H.M. Garcia-Garcia, P. W. Serruys. 2010. A New Method to Measure Necrotic Core and Calcium Content in Coronary Plaques using Intravascular Ultrasound Radiofrequency-based Analysis. The International Journal of Cardiovascular Imaging. 26(4): 387-396.

Downloads

Published

2015-07-13

Issue

Vol. 75 No. 2: Computer Graphic and Visions

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

COMPARATIVE STUDY OF CLUSTERING ALGORITHMS IN ORDER TO VIRTUAL HISTOLOGY (VH) IMAGE SEGMENTATION. (2015). Jurnal Teknologi, 75(2). https://doi.org/10.11113/jt.v75.4994