Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2012.19B.3.201

Automatic Identification of the Lumen Border in Intravascular Ultrasound Images  

Park, Jun-Oh (계명대학교 컴퓨터공학과)
Ko, Byoung-Chul (계명대학교 컴퓨터공학과)
Park, Hee-Jun (계명대학교 의용공학과)
Nam, Jae-Yeal (계명대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.19B, no.3, 2012 , pp. 201-208 More about this Journal
Abstract
Accurately segmenting lumen border in intravascular ultrasound images (IVUS) is very important to study vascular wall architecture for diagnosis of the cardiovascular diseases. After each of IVUS image is transformed to a polar coordinated image, initial points are detected using wavelet transform. Then, lumen border is initialized as the set of important points using non parametric probability density function and smoothing function by removing outlier initial points occurred by noises and artifacts. Finally, polynomial curve fitting is applied to obtain real lumen border using filtered important points. The evaluation of proposed method was performed with related method and the proposed method produced accurate lumen contour detection when compared to another method in most types of IVUS images.
Keywords
IVUS; Wavelet Transform; Non Parametric Probability Density Function; Smoothing Function; Polynomial Curve Fitting;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Papadogiorgaki, V. Mezaris, Y. S. Chatzizisis, G. D. Giannoglou, and I. Kompatsiaris, "Image analysis techniques for automated IVUS contour detection," Ultrasound in Medicine and Biology Journal, Vol.34, No.9, pp.1482-1498, 2008.   DOI   ScienceOn
2 Q. Zhang, Y. Wang, W. Wang, J. Ma, J. Qian, and J. Ge, "Contour Extraction from IVUS Images Based on GVF Snakes and Wavelet Transform", International Conforence on Complex Medical Engineering, pp.536-541, 2007.
3 M. Jourdain, J. Meunier, J. Séqueira, G. Cloutier, J. C. Tardif, "Intravascular Ultrasound Image Segmentation : A Helical Active Contour Method", Image Processing Theory Tools and Applications, pp.92-97, September, 2010.
4 C. Doulaverakis, M. Papadogiorgaki, V. Mezaris, A. Billis, E. Parissi, and I. Kompatsiaris, "IVUS image processing and semantic analysis for Cardiovascular Diseases risk prediction", International Journal of Biomedical Engineering and Technology, Vol.3, pp.349-374, 2010.   DOI
5 E. Loupias, N. Sebe, S. Bres, and J. M. Jolion, "Wavelet-based salient points for image retrieval," International Conference on Image Processing, Vancouver, Vol.2, pp.518-521, 2000.
6 B. C. Ko, J. Peng, and H. Byun, "Region-Based Image Retrieval Using Probabilistic Feature Relevance Feedback," Pattern Analysis and Application, Vol.4, pp.174-184, 2001.   DOI
7 S.C. Chapra and R.P. Canale, "Numerical Methods for Engineers," McGraw-Hill, 6th edn, pp.89-102, 2011.
8 J. M. Bland and D. G. Altman, "Statistical methods for assessing agreement between two methods of clinical measurement," The Lancet, Vol.327, Issue.8476, 307-10, 1986.   DOI   ScienceOn
9 M. Sonka, X. Zhang, M. Siebes, M. S. Bissing, S. C. DeJong, S. M. Collins, and C. R. McKay, "Segmantation of intravascular ultrasound images: A knowledge-based approach," IEEE Transactions on Medical Imaging, Vol.14, No.4, pp.719-732, 2002.
10 H. Zhu, Y. Liang, and M. H. Friedman, "IVUS image segmentation based on contrast," SPIE Medical imaging: Image Processing, Vol.4684, pp.1727-1733, 2002.
11 A. Taki, Z. Najafi, A. Roodaki, S.K. Starehdan, R.A.Zoroofi, A. Konig, N. Navab, "Automatic segmentation of calcified plaques and vessel borders in IVUS images," International Journal of Computer Assisted Radiology and Surgery, Vol.3, No.3-4, pp.347-354, 2008.   DOI
12 M. Zheng, W. Yubin, W. Yousheng, S. Xiaodi, and W. Yali, "Detection of the Lumen and Media-adventitia Borders in IVUS Imaging," International Conference on Signal Processing, pp.1059-1062, 2008.