Detection of Artery Regions in Lower Extremity Arteries from Non-Enhanced MR Imaging Based on Particle Filter Algorithms
Yuiko Koga*, Akiyoshi Yamamoto*,**, Hyoungseop Kim*,
Joo Kooi Tan*, and Seiji Ishikawa*
*Kyusyu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka 804-8550, Japan
**Kyoaikai Tobata Kyoritu Hospital, 2-5-1 Sawami, Tobata-ku, Kitakyushu-shi, Fukuoka 804-0093, Japan
Recently, the arteries sclerosis obliterans (ASO) or called peripheral arterial disease (PAD) typically caused by chronic ischemia of limbs increases remarkably. As one of the diagnosis methods, the image diagnosis methods such as MR image are applied in medical fields. In this paper, we propose a vascular extraction method using fresh blood imaging (FBI) method, as well as apply it to computer aided diagnosis (CAD) system. Especially, to prevent the spread outside of the region and improve the segment accuracy of peripheral artery areas, we introduce particle filter algorithms. We performed our method on automatic artery regions detection using non-enhanced MR images. Furthermore, we compared the extracted results to gold standard data and analyzed accuracy by receiver operating characteristic (ROC). The effectiveness of our proposed method and satisfactory of its detected accuracy were confirmed.
Joo Kooi Tan, and Seiji Ishikawa, “Detection of Artery Regions in Lower Extremity Arteries from Non-Enhanced MR Imaging Based on Particle Filter Algorithms,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.2, pp. 318-323, 2013.
-  K. Yokoyama, T. Nitatori, S. Inaoka, T. Takahara, and J. Hachiya, “Non-contrast Enhanced MR Venography Using 3D Fresh Blood Imaging (FBI): Initial Experience,” Radiation Medicine, Vol.19, No.5, pp. 247-253, 2001.
-  Y. Morimoto, T. Sugimoto, M. Okada, Y. Okita, and T. Mukai, “Platelet Scintigraphy in the Diagnosis of Arteriosclerosis Obliterans,” Surgery Today, Vol.32, pp. 875-879, 2002.
-  K. Nakamura, A. Yamamoto, M. Miyazaki, S. Kawanami, Y. Shioya, and Y. Matsufuji, “Clinical usefulness of non-contrastenhanced MRDSA to evaluate hemodynamics of arterial diseases – initial experience –,” Proc. of the International Society of Magnetic Resonance in Medicine, Vol.11, p. 1356, 2003.
-  A. Yamamoto, K. Nakamura, M. Miyazaki, Y. Shioya, S. Kawanami, and Y. Matsufuji, “Non-contrast-enhanced MRDSA of peripheral arteries using continuous acquisitions of ECG-Triggered 2D half-Fourier FSE within a cardiac cycle,” Proc. of the Int. Society of Magnetic Resonance in Medicine, Vol.11, p. 1709, 2003.
-  J.M. Fitzpatrick, D. L. G. Hill, Y. Shyr et al., “Visual Assessment of the Accuracy of Retrospective Registration of MR and CT Images of the Brain,” IEEE Trans. on Med. Imaging, Vol.17, No.4, pp. 517-585, 1998.
-  Li. Q, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans,” Medical Physics, Vol.30, No.8, pp. 2040-2051, 2003.
-  N. Otsu, “A threshold selection method from gray-level histograms,”IEEE Trans. on Systems, Man, and Cybernetics, Vol.9, No.1, pp. 62-66, 1979.
-  Y. Koga, A. Yamamoto, H. Kim, J. K. Tan, and S. Ishikawa, “Detection of Blood Vessel Regions Using Weighted MIP from Nonenhanced MR Imaging,” Int. Conf. on Control, Automation and Systems, 2010.
-  B. Sugandi, H. Kim, J. K. Tan, and S. Ishikawa, “Occlusion Handling in Object Tracking Based on Particle Filter Approach,” 7th Int. Conf. & Expo on Emerging Technologies for a Smarter World, 2010.
-  B. Ristic, S. Arulampalam, and N. Gordon, “Beyond the Kalman filter: particle filters for tracking applications,” Artech House, 2004.
-  R. Matsumura and K. Okamura, “Object Detection and Tracking Using Particle Filter,” Oshima National College of Maritime Technology, bulletin No.41, pp. 75-85, 2008.
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