Tumor Segmentation of Brain MRI with Adaptive Bandwidth Mean Shift_Journal of Biomedical Engineering

Authors：

HOUXiaowen ,  LIUQi

Department of Medical Information, Sichuan University, Chengdu 610065, China;

Corresponding author：

LIUQi, Email: liuqi@scu.edu.cn

Keywords：

mean shift; brain tumor segmentation; spatial agglomeration; adaptive bandwidth

DOI：

10.7507/1001-5515.20140188

Video：

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In order to get the adaptive bandwidth of mean shift to make the tumor segmentation of brain magnetic resonance imaging (MRI) to be more accurate, we in this paper present an advanced mean shift method. Firstly, we made use of the space characteristics of brain image to eliminate the impact on segmentation of skull; and then, based on the characteristics of spatial agglomeration of different tissues of brain (includes tumor), we applied edge points to get the optimal initial mean value and the respectively adaptive bandwidth, in order to improve the accuracy of tumor segmentation. The results of experiment showed that, contrast to the fixed bandwidth mean shift method, the method in this paper could segment the tumor more accurately.

Citation： HOUXiaowen, LIUQi. Tumor Segmentation of Brain MRI with Adaptive Bandwidth Mean Shift. Journal of Biomedical Engineering, 2014, 31(5): 1001-1004,1010. doi: 10.7507/1001-5515.20140188 Copy

1.	ZHANG Y, MATUSZEWSKI B J, SHARK L K. A novel medical image segmentation method using dynamic programming[C]//International conference on medical information visualization-bioMedical visualization. Zurich: 2007: 69-74.
2.	梅玲.脑部MRI分割算法研究[D].广州:华南理工大学,2011.
3.	李朝阳.基于边界跟踪算法的脑磁共振图像分割[J].中国医学物理学,2012,29(6):3769-3771.
4.	俞海平,邬立保,陈昌沉,等.基于动态方向梯度矢量流模型的脑肿瘤图像分割[J].现代生物医学进展,2012,12(8):1093-1097.
5.	桑林琼,邱明国,王莉,等.基于统计阈值的脑肿瘤MRI图像的分割方法[J].生物医学工程研究,2010,29(4):237-239.
6.	王莉,朱仁江,范嗣强,等.一种基于MRI图像的脑肿瘤组合分割法[J].现代电子技术,2011,34(24):110-112.
7.	OSAMA M A, RAJESWARI M, MOHD E A. A hybrid harmony search algorithm for MRI brain segmentation[J]. Evol Intel,2011,4(1):31-49.
8.	CALDAIROU B, PASSAT N, HABAS P A, et al. A non-local fuzzy segmentation method Application to brain MRI [J].Pattern Recognition, 2011,44(9):1916-1927.
9.	WANG G Q, WANG D X. Segmentation of brain MRI image with GVF snake model[C]//Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First Interna-tional Conference. Harbin:2010:711-714.
10.	KAZEROONI A F, AHMADIAN A,SEREJ N D, et al. Segmentation of brain tumors in MRI images using multi-scale gradient vector flow[C]//Conf Proc IEEE Eng Med Biol Soc. Boston:2011:7973-7976.
11.	LLADÓ X, OLIVER A, CABEZAS M, et al. Segmentation of multiple sclerosis lesions in brain MRI a review of automated approaches[J]. Information Sciences,2012,186(1):164-185.
12.	BALAFAR M A,RAMLI A R,SARIPAN M I,et al. Review of brain MRI image segmentation methods [J]. Artif Intell Rev, 2010, 33(1): 261-274.
13.	DIPLAROS A, VLASSIS N, GEVERS T. A spatially constrained generative model and EM algorithm for image segmentation [J]. IEEE Trans Neural Netw, 2007, 18(3):789-808.
14.	TISDALL D,ATKINS M S. MRI denoising via phase error estimation [C]//Proceedings of SPIE. Burnaby:2007: 646-654.
15.	CHENG Y Z. Mean shift, mode seeking, and clustering [J]. IEEE Trans on Pattern Analysis Intelligence,1995,17(8):790-798.
16.	侯晓文,刘奇.基于小波的磁共振图像聚类分割[J].中国医学影像技术,2013,29(3):168-171.
17.	MAYER A,GREENSPAN H. An adaptive mean-shift framework for MRI brain segmentation[J]. IEEE Trans Med Imaging,2009,28(8):1238-1250.
18.	LAKSHMI G G, SURULIANDI A. Anatomical structure segmentation in MRI brain images[C]//Emerging Trends in Electrical and Computer Technology. Tamil Nadu:2011:786-791.
19.	余博,郭雷,钱晓亮,等.一种新的自适应双边滤波算法[J].应用科学学报,2012,30(5):517-523.
20.	WU Y Q, HOU W, WU S H. Brain MRI segmentation using KFCM and chan-vese model[J]. Trans Tianjin Univ,2011,17(3):215-219.
21.	SMITH S M. Fast robust automated brain extraction[J]. Hum Brain Mapp,2002,17(3):143-155.
22.	汪少锋,王文辉,冯前进,等.基于改进BET算法的MR颅脑图像脑组织自动提取[J].中国图像图形学报,2009,14(10):2029-2034.
23.	SHEIKN Y A, KHAN E A, KANADE T, et al. Mode-seeking by medoidshifts[C]//Proc CVPR. Rio de Janeiro:2007:1-8.
24.	VEDALDI A, SOATTO S. Quick shift and kernel methods for mode seeking[J]. Computer Vision,2008,10(4):978-1007.
25.	宫改云.FCM算法参数研究及其应用[D].西安:西安电子科技大学,2004.
26.	李乡儒,吴福朝,胡占义.均值漂移算法的收敛性[J].软件学报,2005,16(3):365-374.

1. ZHANG Y, MATUSZEWSKI B J, SHARK L K. A novel medical image segmentation method using dynamic programming[C]//International conference on medical information visualization-bioMedical visualization. Zurich: 2007: 69-74.
2. 梅玲.脑部MRI分割算法研究[D].广州:华南理工大学,2011.
3. 李朝阳.基于边界跟踪算法的脑磁共振图像分割[J].中国医学物理学,2012,29(6):3769-3771.
4. 俞海平,邬立保,陈昌沉,等.基于动态方向梯度矢量流模型的脑肿瘤图像分割[J].现代生物医学进展,2012,12(8):1093-1097.
5. 桑林琼,邱明国,王莉,等.基于统计阈值的脑肿瘤MRI图像的分割方法[J].生物医学工程研究,2010,29(4):237-239.
6. 王莉,朱仁江,范嗣强,等.一种基于MRI图像的脑肿瘤组合分割法[J].现代电子技术,2011,34(24):110-112.
7. OSAMA M A, RAJESWARI M, MOHD E A. A hybrid harmony search algorithm for MRI brain segmentation[J]. Evol Intel,2011,4(1):31-49.
8. CALDAIROU B, PASSAT N, HABAS P A, et al. A non-local fuzzy segmentation method Application to brain MRI [J].Pattern Recognition, 2011,44(9):1916-1927.
9. WANG G Q, WANG D X. Segmentation of brain MRI image with GVF snake model[C]//Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First Interna-tional Conference. Harbin:2010:711-714.
10. KAZEROONI A F, AHMADIAN A,SEREJ N D, et al. Segmentation of brain tumors in MRI images using multi-scale gradient vector flow[C]//Conf Proc IEEE Eng Med Biol Soc. Boston:2011:7973-7976.
11. LLADÓ X, OLIVER A, CABEZAS M, et al. Segmentation of multiple sclerosis lesions in brain MRI a review of automated approaches[J]. Information Sciences,2012,186(1):164-185.
12. BALAFAR M A,RAMLI A R,SARIPAN M I,et al. Review of brain MRI image segmentation methods [J]. Artif Intell Rev, 2010, 33(1): 261-274.
13. DIPLAROS A, VLASSIS N, GEVERS T. A spatially constrained generative model and EM algorithm for image segmentation [J]. IEEE Trans Neural Netw, 2007, 18(3):789-808.
14. TISDALL D,ATKINS M S. MRI denoising via phase error estimation [C]//Proceedings of SPIE. Burnaby:2007: 646-654.
15. CHENG Y Z. Mean shift, mode seeking, and clustering [J]. IEEE Trans on Pattern Analysis Intelligence,1995,17(8):790-798.
16. 侯晓文,刘奇.基于小波的磁共振图像聚类分割[J].中国医学影像技术,2013,29(3):168-171.
17. MAYER A,GREENSPAN H. An adaptive mean-shift framework for MRI brain segmentation[J]. IEEE Trans Med Imaging,2009,28(8):1238-1250.
18. LAKSHMI G G, SURULIANDI A. Anatomical structure segmentation in MRI brain images[C]//Emerging Trends in Electrical and Computer Technology. Tamil Nadu:2011:786-791.
19. 余博,郭雷,钱晓亮,等.一种新的自适应双边滤波算法[J].应用科学学报,2012,30(5):517-523.
20. WU Y Q, HOU W, WU S H. Brain MRI segmentation using KFCM and chan-vese model[J]. Trans Tianjin Univ,2011,17(3):215-219.
21. SMITH S M. Fast robust automated brain extraction[J]. Hum Brain Mapp,2002,17(3):143-155.
22. 汪少锋,王文辉,冯前进,等.基于改进BET算法的MR颅脑图像脑组织自动提取[J].中国图像图形学报,2009,14(10):2029-2034.
23. SHEIKN Y A, KHAN E A, KANADE T, et al. Mode-seeking by medoidshifts[C]//Proc CVPR. Rio de Janeiro:2007:1-8.
24. VEDALDI A, SOATTO S. Quick shift and kernel methods for mode seeking[J]. Computer Vision,2008,10(4):978-1007.
25. 宫改云.FCM算法参数研究及其应用[D].西安:西安电子科技大学,2004.
26. 李乡儒,吴福朝,胡占义.均值漂移算法的收敛性[J].软件学报,2005,16(3):365-374.

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