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 Li Qiang,Zhang Qijun,Guan Xin,et al.Segmentation of Carotid Intima Media in Ultrasound Images Using Improved Fuzzy C Means Algorithm[J].Journal of Tianjin University,2018,(01):95-102.[doi:10.11784/tdxbz201612044]



[1] Simon A, Gariepy J, Chironi G, et al, Intima-media thickness:A new tool for diagnosis and treatment of cardiovascular risk[J]. Journal of Hypertension, 2002, 20(2):159-169.
[2] Pignoli P, Longo T. Evaluation of atherosclerosis with B-mode ultrasound imaging[J]. The Journal of Nuclear Medicine and Allied Sciences, 1988, 32(3):166-173.
[3] Touboul P J, Prati P, Scarabin P Y, et al. Use of monitoring software to improve the measurement of carotid wall thickness by B-mode imaging[J]. Journal of Hypertension, 1992, 10(5):37-42.
[4] Cheng D C, Schmidt-Trucks?ss A, Cheng K S, et al. Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images [J]. Computer Methods & Programs in Biomedicine, 2002, 67(1):27-37.
[5] Loizou C P, Pattichis C S, Pantziaris M, et al. Snakes based segmentation of the common carotid artery intima media[J]. Medical & Biological Engineering & Computing, 2007, 45(1):35-49.
[6] Petroudi S, Loizou C, Pantziaris M, et al. Segmentation of the common carotid intima-media complex in ultrasound images using active contours[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(11):3060-3069.
[7] Faita F, Gemignani V, Bianchini E, et al. Real-time measurement system for evaluation of the carotid intima-media thickness with a robust edge operator[J]. Journal of Ultrasound Medicine, 2008, 27(9):1353-1361.
[8] Li Q, Zhang W, Guan X, et al. An improved approach for accurate and efficient measurement of common carotid artery intima-media thickness in ultrasound images[J]. BioMed Research International, 2014(1):740328-1-8.
[9] Xu X, Zhou Y, Cheng X, et al. Ultrasound intima-media segmentation using Hough transform and dual snake model[J]. Computerized Medical Imaging and Graphics, 2012, 36(3):248-258.
[10] Xiao L, Li Q, Bai Y, et al. Automated measurement method of common carotid artery intima-media thickness in ultrasound image based on Markov random field mod-els[J]. Journal of Medical and Biological Engineering, 2015, 35(5):651-660.
[11] Menchón-Lara R M, Bastida-Jumilla M C, Morales-Sánchez J, et al. Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks[J]. Medical & Biological Engineering & Computing, 2014, 52(2):169-181.
[12] Menchón-Lara R M, Sancho-Gómez J L. Fully automatic segmentation of ultrasound common carotid artery images based on machine learning[J]. Neurocomputing, 2015, 151:161-167.
[13] Rosati S, Balestra G, Molinari F. Rough set based approach for IMT automatic estimation[J]. International Journal of Bioelectromagnetism, 2012, 14(4):211-216.
[14] Otsu N. A threshold selection method from gray-scale histograms[J]. IEEE Trans on Systems Man & Cybernetics, 1979, 9(1):62-66.
[15] Tomasi C, Manduchi R. Bilateral filtering for gray and color images[C]// International Conference on Computer Vision. Bombay, India, 1998:839.
[16] Dunn J. A fuzzy relative of the ISODATA process and its use in detecting compact well-sparated cluster[J]. Journal of Cybern, 1973, 3(3):32-57.
[17] 张小峰. 基于模糊聚类算法的医学图像分割技术研究[D]. 济南:山东大学计算机科学与技术学院, 2014.
Zhang Xiaofeng. Research of Medical Image Segmentation Based on Fuzzy Clustering Algorithms[D]. Jinan:School of Computer Science and Technology, Shandong University, 2014(in Chinese).
[18] Chatzis S P, Varvarigou T A. A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation[J]. IEEE Transactions on Fuzzy Systems, 2008, 16(5):1351-1361.
[19] Clifford P. Markov random fields in statistics[J]. Disorder in Physical Systems, 1990, 14(1):19-32.
[20] Molinari F, Pattichis C S, Zeng G, et al. Completely automated multiresolution edge snapper—A new technique for an accurate carotid ultrasound IMT measurement:Clinical validation and benchmarking on a multi-institutional database[J]. IEEE Transactions on Image Processing, 2012, 21(3):1211-1222.


收稿日期: 2016-12-19; 修回日期: 2017-03-08.
作者简介: 李锵(1974—), 男, 博士, 教授, liqiang@tju.edu.cn.
通讯作者: 张琦珺, zqjyx@tju.edu.cn.
基金项目: 国家自然科学基金资助项目(61471263).
Supported by the National Natural Science Foundation of China(No.,61471263).

更新日期/Last Update: 2018-01-10