مرزبندی نواحی سایه‌دار در تصاویر فراصوت داخل عروقی به کمک کانتور‌های فعال
محورهای موضوعی : مهندسی برق و کامپیوترمریم بسیج 1 , محمدرضا یزدچی 2 , پیمان معلم 3 , آرش تاکی 4
1 - دانشگاه اصفهان
2 - دانشگاه اصفهان
3 - دانشگاه اصفهان
4 - دانشگاه صنعتی مونیخ آلمان
کلید واژه: آستانه‌گذاری اتسو تصویربرداری فراصوت داخل عروقی تشخیص سایه کانتور‌های فعال,
چکیده مقاله :
تصویربرداری فراصوت داخل عروقی (IVUS)، به منظور دریافت اطلاعات دقیق‌تری از اندازه و جنس پلاکهای تشکیل‌شده داخل عروق کرونری نسبت به آنژیوگرافی انجام می‌شود. گاهی در این تصاویر مناطقی تاریک در پشت پلاکهای کلسیم تشکیل می‌شود که پردازش این تصاویر را مشکل می‌سازد. این مقاله به ارائه یک روش جدید جهت تشخیص و مرزبندی سایه‌ها در تصاویر IVUS می‌پردازد. در الگوریتم پیشنهادی از آستانه‌گذاری اتسو جهت شناسایی محل تشکیل سایه و از کانتور‌های فعال جهت تشخیص مرز‌ها در این ناحیه استفاده می‌شود. بر طبق آزمایش انجام‌شده روی 30 تصویر نمونه، این الگوریتم با حساسیتی برابر با 86% به درستی قادر به تشخیص مناطق سایه‌دار می‌باشد.
Intra vascular imaging is used for extracting more accurate information about the size and characteristics of plaques than coronary angiography. Sometimes shadows appear behind the calcification plaques that it makes some problem to process these images automatically. This paper describes a new approach for shadows region and border detection in Intra Vascular Ultrasound images. In the proposed algorithm, Otsu thresholding is utilized for identification of shadows location and the Active contours without edge is used for shadows border detection. According to experiments conducted on 30 samples, this proposed algorithm can able to detect shadow regions correctly with sensitivity of 86%.
[1] G. Mendizabal - Ruiz, M. Rivera, and I. A. Kakadiaris, "A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 8 pp., Jun. 2008.
[2] D. Haiyan and L. Hong, "Texture analysis and characteristic identification about plaque tissues of IVUS," in Proc. Inter Conf. on Computational and Information Sciences, pp. 873-876, Dec. 2010.
[3] A. Ashoori, B. Moshiri, and S. K. Setarehdan, "Fuzzy image fusion application in detecting coronary layers in IVUS pictures," in Proc. 3rd Int. Symp. on Communications, Control, and Signal Processing, pp. 20-24, Mar. 2008.
[4] M. Jourdain, J. Meunier, J. Sequeira, G. Cloutier, and J. C. Tardif, "Intravascular ultrasound image segmentation: a helical active contour method," in Proc. 2nd Inter Conf. on Image Processing Theory Tools and Applications, IPTA, pp. 92-97, Jul. 2010.
[5] M. E. Plissiti, D. I. Fotiadis, L. K. Michalis, and G. E. Bozios, "An automated method for lumen and media - adventitia border detection in a sequence of IVUS frames," IEEE Trans. on Information Technology in Biomedicine, vol. 8, no. 1, pp. 131-141, Jun. 2004.
[6] G. Unal, S. Bucher, G. Slabaugh, T. Fang, and K. Tanaka, "Shape - driven segmentation of the arterial wall in intravascular ultrasound images," IEEE Trans. on Information Technology in Biomedicine, vol. 12, no. 3, pp. 335-347, May 2008.
[7] R. Sanz - Requena, M. David, D. R. Garcia - Sanchez, V. Bodi, J. J. Rieta, and J. M. Sanchis, "Automatic segmentation and 3D reconstruction of intravascular ultrasound," Computerized Medical Imaging and Graphics, vol. 31, no. 2, pp. 71-80, Jan. 2007.
[8] X. Zhu, P. Zhang, J. Shao, Y. Cheng, Y. Zhang, and J. Bai, "A snake - based method for segmentation of intravascular ultrasound images and its in vivo validation," Ultrasonics, vol. 51, no. 2, pp. 181-189, Feb. 2011.
[9] E. Russeau, C. L. de Korte, F. Mastik, J. A. Schaar, and A. F. W. van der Steen, "Fully automatic luminal contour segmentation in intracoronary ultrasound imaging - a statistical approach," IEEE Trans. on Medical Imaging, vol. 23, no. 5, pp. 554-566, May 2004.
[10] K. Sun, Z. Chen, and S. Jiang, "Local morphology fitting active contour for automatic vascular segmentation," IEEE Trans. on Biomedical Engineering, vol. 59, no. 2, pp. 464-473, Feb. 2012.
[11] E. D. Santos Filho, M. Yoshizawa, A. Tanaka, Y. Saijo, and T. Iwamoto, "Moment - based texture segmentation of luminal contour in intravascular ultrasound images," The Japan Society of Ultrasonics in Medicine, vol. 32, no. 3, pp. 91-99, Sep. 2005.
[12] S. Mohammadi, M. Yazdchi, A. Karimian, and H. Mohammadi, "Automatic plaque detection in IVUS images: a textural approach," in Proc. 3rd Inter Conf. on Modeling and Simulation, pp. 7-9, Jan. 2011.
[13] A. Roodaki, A. Taki, S. K. Setarehdan, and N. Navab, "Modified wavelet transform features for characterizing different plaque types in IVUS images, a feasibility study," in Proc. 9th Inter Conf. on Signal Processing, pp. 789-792, Jan. 2008.
[14] E. D. Santos Filho, Y. Saijo, T. Yambe, A. Tanaka, and M. Yoshizawa, "Segmentation of calcification regions in intravascular ultrasound images by adaptive thresholding," in Proc. 19th IEEE Int. Symp. on Computer-Based Medical Systems, CBMS, pp. 446-454, Jul. 2006.
[15] E. D. Santos Filho, Y. Saijo, A. Tanaka, and M. Yoshizawa, "Detection and quantification of calcifications in intravascular ultrasound images by automatic thresholding," Ultrasound in Medicine and Biology, vol. 34, no. 1, pp. 160-165, Jan. 2010.
[16] E. Fathi - Azar and S. K. Setarehdan, "Type2 fuzzy set based automatic shadow region segmentation in intra vascular ultrasound images," in Proc. IEEE Inter Conf. on Signal and Image Processing Applications, pp. 354-359, Nov. 2011.
[17] T. Koga, E. Uchino, and N. Suetake, "Automated boundary extraction and visualization system for coronary plaque in IVUS image by using fuzzy inference - based method," in Proc. IEEE Inter Conf. on Fuzzy Systems, pp. 1966-1973, Jun. 2011.
[18] F. Yousefi Rizi and S. K. Setarehdan, "Noise reduction in intravascular ultrasound images using curvelet transform and adaptive complex diffusion filter: a comparative study," in Proc. 20th Iranian Conf. on Electrical Engineering May 2012.
[19] S. Mssedi, M. Ben Salah, R. Abdelfattah, and A. Mitiche, "Extraction of road network using a modified active contour approach," in Proc. IEEE Inter Conf. on Image Processing, pp. 2929-2932,Sep.. 2011.
[20] R. S. Tavares, J. M. Chirinos, L. I. Abe, T. Gotoh, S. Kagei, T. Iwasawa, and M. de Sales Guerra Tsuzuki, "Temporal segmentation of lung region from MRI sequences using multiple active contours," in Proc. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 7985-7988, Sep. 2011.
[21] M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: active contour models," Int. J. of Computer Vision, vol. 1, no. 4, pp. 321-331, Jan. 1988.
[22] T. F. Chan and L. A. Vese, "Active contours without edges," IEEE Trans. on Image Processing, vol. 10, no. 2, pp. 266-277, Feb. 2001.
[23] R. O. Duda and P. E. Hart, "Use of the Hough transformation to detect lines and curves in picture," Commun. ACM, vol. 1, no. 15, pp. 11-15, 1972.
[24] X. Zhu and M. R. Rangayyan, "Detection of the optic disc in images of the retina using the Hough transform," in Proc. 30th Annual Inter Conf. of the Engineering in Medicine and Biology Society, pp. 3546–3549, Aug. 2008.
[25] T. N. Thuy, P. X. Dai, and J. J. Wook, "Rectangular object tracking based on standard hough transform," in Proc. Inter Conf. on Robotics and Biomimetics, pp. 2098–2103, Feb. 2009.
[26] N. Otsu, "A threshold selection method from gray level histogram," IEEE Trans. on Systems Man Cybernet, vol. 9, no. 1, pp. 62-66, Jan. 1979.
[27] Eagle Eye Gold, Volcano Inc.
