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.腦部MRI分割算法研究[D].廣州:華南理工大學,2011.
3.	李朝陽.基于邊界跟蹤算法的腦磁共振圖像分割[J].中國醫學物理學,2012,29(6):3769-3771.
4.	俞海平,鄔立保,陳昌沉,等.基于動態方向梯度矢量流模型的腦腫瘤圖像分割[J].現代生物醫學進展,2012,12(8):1093-1097.
5.	桑林瓊,邱明國,王莉,等.基于統計閾值的腦腫瘤MRI圖像的分割方法[J].生物醫學工程研究,2010,29(4):237-239.
6.	王莉,朱仁江,范嗣強,等.一種基于MRI圖像的腦腫瘤組合分割法[J].現代電子技術,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.	侯曉文,劉奇.基于小波的磁共振圖像聚類分割[J].中國醫學影像技術,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,錢曉亮,等.一種新的自適應雙邊濾波算法[J].應用科學學報,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.	汪少鋒,王文輝,馮前進,等.基于改進BET算法的MR顱腦圖像腦組織自動提取[J].中國圖像圖形學報,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.	宮改云.FCM算法參數研究及其應用[D].西安:西安電子科技大學,2004.
26.	李鄉儒,吳福朝,胡占義.均值漂移算法的收斂性[J].軟件學報,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.腦部MRI分割算法研究[D].廣州:華南理工大學,2011.
3. 李朝陽.基于邊界跟蹤算法的腦磁共振圖像分割[J].中國醫學物理學,2012,29(6):3769-3771.
4. 俞海平,鄔立保,陳昌沉,等.基于動態方向梯度矢量流模型的腦腫瘤圖像分割[J].現代生物醫學進展,2012,12(8):1093-1097.
5. 桑林瓊,邱明國,王莉,等.基于統計閾值的腦腫瘤MRI圖像的分割方法[J].生物醫學工程研究,2010,29(4):237-239.
6. 王莉,朱仁江,范嗣強,等.一種基于MRI圖像的腦腫瘤組合分割法[J].現代電子技術,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. 侯曉文,劉奇.基于小波的磁共振圖像聚類分割[J].中國醫學影像技術,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,錢曉亮,等.一種新的自適應雙邊濾波算法[J].應用科學學報,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. 汪少鋒,王文輝,馮前進,等.基于改進BET算法的MR顱腦圖像腦組織自動提取[J].中國圖像圖形學報,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. 宮改云.FCM算法參數研究及其應用[D].西安:西安電子科技大學,2004.
26. 李鄉儒,吳福朝,胡占義.均值漂移算法的收斂性[J].軟件學報,2005,16(3):365-374.

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