基于偏移场的核磁共振脑图像分割算法研究
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摘要
医学图像分割是医学图像进一步处理和分析的前提条件和基础，在医学图像处理中是一个经典的问题，在临床诊断、病理分析以及治疗等方面具有非常重要的意义。对于脑组织图像而言，对脑部组织的正确分割在测量病变组织的尺寸、外观的量化信息和实现脑部结构的三维重构等问题中起到了关键作用。核磁共振脑图像本身具有模糊性，而且成像过程中容易受到噪声、局部体效应和亮度不均匀性等因素的影响，使图像中某一个特定组织的亮度分布变得平滑，从而导致相邻组织间的亮度直方图交叠分布，成为脑组织图像自动分割技术研究中一个主要障碍，对分割结果的准确性和可靠性造成了很大影响。
为了提高核磁共振脑图像分割算法的准确性，针对脑图像分割中的噪声问题和偏移场问题，本文根据核磁共振脑图像的特点，建立了基于聚类区域的图像模型，综合利用模糊C均值算法、信息熵理论、基于梯度的分水岭算法、基于小波的去噪算法和脑组织图像分布规律等，分别提出一种基于局部熵最小化的核磁共振脑图像二次分割算法、基于区域动态搜索和基于二次去噪的核磁共振脑图像分割算法。本文使用的模拟数据和真实数据来自蒙特利尔神经学院的脑组织图像中心和国际脑组织图库，并通过Matlab语言编程实现。通过大量的实验分析和比较，证明了本文算法的准确性和可靠性。
The medical image segmentation technique is the key technology in medical image processing and analysis, which is one of the classical problems in medical image processing. The medical image segmentation is the precondition and basis for the understanding and explication of the medical image, and be of great importance in the clinical diagnosis, the pathological analysis and the treatment. It is commonly used in image analysis, registration, fusion, the measurement of anatomical structure and image reconstruction. Specifically, the results of image segmentation can be used to measure the volume of human organs, tissues or lesions. According to the quantitative measurement and analysis of the volume before and after the treatment, the doctor can predict, diagnose and develop the patient's treatment plan; the results can be used on the reconstruction of3D data, visualization, the formulation and simulation of the surgical program; the results of the image segmentation can also be used for the remote medical expert system. In the case of network bandwidth resources are limited, the accurate image segmentation is critical for improving the local image classification based on the region of interest(ROI) and the progressive transmission speed. In order to obtain the quantitative information of the size and the appearance of brain lesions, and realize the three-dimensional reconstruction of brain structure, the segmentation of brain image is particularly critical.During medical image segmentation, noise, partial volume effects and intensity inhomogeneity are three mainly considerable difficulties. The existence of noise makes the segmentation region becomes discontinuous, directly affects the accuracy of segmentation results. The intensity inhomogeneity (also known as non-uniformity field), is a phenomenon of the brightness change slowly, in the same physiological organization or structure. It includes both shading artifacts and inherent non-uniform of tissue properties. And in the whole image, the bias field is continuous, smooth and slowly varying. It is mainly caused by the imperfection of the magnetic resonance imaging equipment, including the RF field, the static field and gradient field. The inherent non-uniform of tissue properties is that, there exist quite a few spatially different substructures with functions within each tissue class in the human brain. Due to the inherent regional differences in imaging-related properties across substructures, the intensities in different substructures, even in the same tissue class, are also more or less different. The imaging-related properties that cause the inherent intensity variation include the composition, density, and magnetic properties (spin-lattice relaxation time T1, spin-spin relaxation time T2) of different tissues at different positions.
Although the affect of intensity inhomogeneities on MR images is not obvious on visually, because the human visual system can automatically correct the inhomogeneity. However, intensity inhomogeneities in MR images, which can change the local statistical characteristics of the image, and cause the brightness distribution of different physiological organizations overlap, and the segmentation of MR images is more difficulty than other image, are a major obstacle to any automatic methods for MR image.
In order to improve the accuracy of the segmentation algorithm for Magnetic resonance brain image, we research the problem of the noise and intensity inhomogeneity in the brain image segmentation, in-depth research on the segmentation algorithm for Magnetic resonance brain images corrupted by intensity inhomogeneity. The main work of this paper is as follows:
(1) Based on local image model, we propose a Secondary segmentation algorithm for Magnetic Resonance Brain image based on Local Entropy Minimization(SLEM), to overcome the impact of the intensity inhomogeneity. The tissue-based block method meets the local image model, which makes it is possible to overcome the impact of intensity inhomogeneity by using the idea of segmentation based on local region. Secondly, the use of information entropy theory realizes the optimization of segmentation region, makes the algorithm to keep the localized and find the minimum area of the region affected by the intensity inhomogeneity at the same time, so improve the accuracy and computing time of the algorithm. Then, for each segmentation region, the use of FCM algorithm is not only suitable for brain tissue images with fuzzy characteristics, but also run fast. Finally, the regional dynamic search of secondary segmentation algorithm, achieves the secondary segmentation for the misclassification pixels in the first segmentation result, further improve the accuracy of the algorithm.
(2) We use the over-segmentation of watershed algorithm as an advantage to overcome the intensity inhomogeneity in the image, and proposed a segmentation algorithm based on Regional Dynamic Search (RDS) for MR brain images corrupted by intensity inhomogeneity. Regional dynamic search is established on the basis of two concepts of search window and segmentation region(also segmentation environment), which achieves the dynamic corresponding relationship between the window and region, not only improves the ability of the algorithm to overcome the intensity inhomogeneity, but also solves the problem of boundary effects appear in the existing segmentation algorithm based on region. We use the characteristics of over-segmentation region and tag matrix obtained by the watershed algorithm, to find the region segmentation environment for search window which satisfy the conditions continuously. Finally, the FCM algorithm is independently performed in the final segmentation regions corresponding to each search window, to determine the segmentation results of the pixels in the search window, thus completing the fast and accurate segmentation of the whole image.
(3) We use the "denoising" operation on the image of segmentation result, and propose a secondary denoising algorithm for the segmentation of magnetic resonance brain tissue image with noise and intensity inhomogeneity. Firstly, in order to reduce the impact of noise, we use a half-soft threshold denoising algorithm based on wavelet to remove the noise in image. Secondly, the segmentation algorithm based on regional dynamic search for MR brain images proposed in the previous chapter, is used to reduce the impact of the intensity inhomogeneity for segmenting the image after denoising. Due to the influence of noise, there are significant misclassification points in the segmentation result, we call "noise points". Therefore, we proposed the secondary denoising algorithm for the segmented image, to segment the pixels which meet the conditions of noise point again. The experimental results show that when the noise level is high in the image, the secondary denoising algorithm improves the accuracy of the segmentation results significantly better than the denoising algorithm before segmentation.
Above all, this thesis makes an in-depth study on the segmentation algorithm of Magnetic Resonance brain image, the main consideration is the impact of the noise and bias field on the segmentation results. The proposed methods overcome the impact of the noise and bias field, and realize the segmentation of MR brain images rapidly and accurately at the same time. The experiment is implemented on both simulated and real MR brain images, and compared with other published algorithm, prove the validity and accuracy of our proposed methods in terms of the accuracy and run time of the segmentation results.
为了提高核磁共振脑图像分割算法的准确性，针对脑图像分割中的噪声问题和偏移场问题，本文根据核磁共振脑图像的特点，建立了基于聚类区域的图像模型，综合利用模糊C均值算法、信息熵理论、基于梯度的分水岭算法、基于小波的去噪算法和脑组织图像分布规律等，分别提出一种基于局部熵最小化的核磁共振脑图像二次分割算法、基于区域动态搜索和基于二次去噪的核磁共振脑图像分割算法。本文使用的模拟数据和真实数据来自蒙特利尔神经学院的脑组织图像中心和国际脑组织图库，并通过Matlab语言编程实现。通过大量的实验分析和比较，证明了本文算法的准确性和可靠性。
The medical image segmentation technique is the key technology in medical image processing and analysis, which is one of the classical problems in medical image processing. The medical image segmentation is the precondition and basis for the understanding and explication of the medical image, and be of great importance in the clinical diagnosis, the pathological analysis and the treatment. It is commonly used in image analysis, registration, fusion, the measurement of anatomical structure and image reconstruction. Specifically, the results of image segmentation can be used to measure the volume of human organs, tissues or lesions. According to the quantitative measurement and analysis of the volume before and after the treatment, the doctor can predict, diagnose and develop the patient's treatment plan; the results can be used on the reconstruction of3D data, visualization, the formulation and simulation of the surgical program; the results of the image segmentation can also be used for the remote medical expert system. In the case of network bandwidth resources are limited, the accurate image segmentation is critical for improving the local image classification based on the region of interest(ROI) and the progressive transmission speed. In order to obtain the quantitative information of the size and the appearance of brain lesions, and realize the three-dimensional reconstruction of brain structure, the segmentation of brain image is particularly critical.During medical image segmentation, noise, partial volume effects and intensity inhomogeneity are three mainly considerable difficulties. The existence of noise makes the segmentation region becomes discontinuous, directly affects the accuracy of segmentation results. The intensity inhomogeneity (also known as non-uniformity field), is a phenomenon of the brightness change slowly, in the same physiological organization or structure. It includes both shading artifacts and inherent non-uniform of tissue properties. And in the whole image, the bias field is continuous, smooth and slowly varying. It is mainly caused by the imperfection of the magnetic resonance imaging equipment, including the RF field, the static field and gradient field. The inherent non-uniform of tissue properties is that, there exist quite a few spatially different substructures with functions within each tissue class in the human brain. Due to the inherent regional differences in imaging-related properties across substructures, the intensities in different substructures, even in the same tissue class, are also more or less different. The imaging-related properties that cause the inherent intensity variation include the composition, density, and magnetic properties (spin-lattice relaxation time T1, spin-spin relaxation time T2) of different tissues at different positions.
Although the affect of intensity inhomogeneities on MR images is not obvious on visually, because the human visual system can automatically correct the inhomogeneity. However, intensity inhomogeneities in MR images, which can change the local statistical characteristics of the image, and cause the brightness distribution of different physiological organizations overlap, and the segmentation of MR images is more difficulty than other image, are a major obstacle to any automatic methods for MR image.
In order to improve the accuracy of the segmentation algorithm for Magnetic resonance brain image, we research the problem of the noise and intensity inhomogeneity in the brain image segmentation, in-depth research on the segmentation algorithm for Magnetic resonance brain images corrupted by intensity inhomogeneity. The main work of this paper is as follows:
(1) Based on local image model, we propose a Secondary segmentation algorithm for Magnetic Resonance Brain image based on Local Entropy Minimization(SLEM), to overcome the impact of the intensity inhomogeneity. The tissue-based block method meets the local image model, which makes it is possible to overcome the impact of intensity inhomogeneity by using the idea of segmentation based on local region. Secondly, the use of information entropy theory realizes the optimization of segmentation region, makes the algorithm to keep the localized and find the minimum area of the region affected by the intensity inhomogeneity at the same time, so improve the accuracy and computing time of the algorithm. Then, for each segmentation region, the use of FCM algorithm is not only suitable for brain tissue images with fuzzy characteristics, but also run fast. Finally, the regional dynamic search of secondary segmentation algorithm, achieves the secondary segmentation for the misclassification pixels in the first segmentation result, further improve the accuracy of the algorithm.
(2) We use the over-segmentation of watershed algorithm as an advantage to overcome the intensity inhomogeneity in the image, and proposed a segmentation algorithm based on Regional Dynamic Search (RDS) for MR brain images corrupted by intensity inhomogeneity. Regional dynamic search is established on the basis of two concepts of search window and segmentation region(also segmentation environment), which achieves the dynamic corresponding relationship between the window and region, not only improves the ability of the algorithm to overcome the intensity inhomogeneity, but also solves the problem of boundary effects appear in the existing segmentation algorithm based on region. We use the characteristics of over-segmentation region and tag matrix obtained by the watershed algorithm, to find the region segmentation environment for search window which satisfy the conditions continuously. Finally, the FCM algorithm is independently performed in the final segmentation regions corresponding to each search window, to determine the segmentation results of the pixels in the search window, thus completing the fast and accurate segmentation of the whole image.
(3) We use the "denoising" operation on the image of segmentation result, and propose a secondary denoising algorithm for the segmentation of magnetic resonance brain tissue image with noise and intensity inhomogeneity. Firstly, in order to reduce the impact of noise, we use a half-soft threshold denoising algorithm based on wavelet to remove the noise in image. Secondly, the segmentation algorithm based on regional dynamic search for MR brain images proposed in the previous chapter, is used to reduce the impact of the intensity inhomogeneity for segmenting the image after denoising. Due to the influence of noise, there are significant misclassification points in the segmentation result, we call "noise points". Therefore, we proposed the secondary denoising algorithm for the segmented image, to segment the pixels which meet the conditions of noise point again. The experimental results show that when the noise level is high in the image, the secondary denoising algorithm improves the accuracy of the segmentation results significantly better than the denoising algorithm before segmentation.
Above all, this thesis makes an in-depth study on the segmentation algorithm of Magnetic Resonance brain image, the main consideration is the impact of the noise and bias field on the segmentation results. The proposed methods overcome the impact of the noise and bias field, and realize the segmentation of MR brain images rapidly and accurately at the same time. The experiment is implemented on both simulated and real MR brain images, and compared with other published algorithm, prove the validity and accuracy of our proposed methods in terms of the accuracy and run time of the segmentation results.
引文
[1]罗述谦等.医学图像处理与分析[M],北京,科学出版社,2003.
[2]田捷等.医学影像处理与分析[M].北京：电子工业出版社,2003.
[3]Amit Chakraboriy, Lawrence H. Staib, James S. Duncan. Deformable Boundary Finding in Medical Image by Integrating Gradient and Region Information[J]. IEEE Trans. Medical Imaging,1996,1(6):859-870.
[4]B Johnston, M S Atkins, B Mackiewich, etal. Segmentation of Multiple Sclerosis Lesions in Intensity Corrected Multispectral MRI [J]. IEEE Trans. Medical Imaging,1996,15(2):154-169.
[5]C Tsai. Automated segmentation of brain MR images [J]. Pattern Recognition,1995,28(12):1825-1837.
[6]Kevin P. Hinshaw. Shape-based models for interactive segmentation of medical images in Image Processing [J], Murrayh. loew, editor, Proc. SPIE,1995,2434:771-780.
[7]Jayaram K. Udupa and Supun Samarasekera. Fuzzy Connectedness and Object Definition:Theory, Algorithms, and Applications in Image Segmentation[J]. Graphical Models and Image Processing,1996,58(3):246-251.
[8]Norberto Ezquerra and Rakesh Mullick. Knowledge2guided segmentation of3D imagery [J]. Graphical Models and Image Processing,1996,58(6):510-523.
[9]王合.医学图像边缘检测技术的研究[D].上海交通大学博士学位论文,1996,1-4.
[10]B. Mackiewich. Intracranial boundary detection and radio frequency correction in magnetic resonance images. M. S. thesis, Simon Fraster Univ, Burnaby, B. C, Canada, Aug,1995.
[11]L P Clarke, R P Velthuizen, M A Camacho, etal. MRI segmentation:Methods and application [J]. Magnetic Resonance Imaging,1995,13(3):343-368.
[12]J C Bezdek, L O Hall, L P Clarke. Review of MR image segmentation techniques using pattern recognition [J]. Med. Phys.1993,20(4):1033-1048.
[13]N. R. Par, S. K. Par. A Review on Image Segmentation Techniques[J]. Pattern Recognition,1993,26(9):1277-1294.
[14]D. Zung, L. Pham, Chenyang xu. Current Methods in Medical Image Segmentation.Annu[J]. Rev. Biomed. Eng.2000,02:315-338.
[15]Niessen W., Vincken K., Weickert J., Haar Romeny B., Viergever M.:Multiscale segmentation of three dimensional MR brain images[J]. Internat. J. Comput. Vision31(2/3),(1999)185-202.
[16]Alan Wee-Chung Liew, Hong Yan:Current Methods in the Automatic Tissue Segmentation of3D Magnetic Resonance Brain Images [J]. Current Medical Imaging Reviews, Vol.2, No.1,2006,91-103.
[17]J S Suri, S Singh, L Reden. Computer Vision and Pattern Recognition Techniques for2D and3D MR Cerebral Cortical Segmentation (Part I):A State-of-the-Art Review. Pattern Analysis&Application[J].2002(5):46-76.
[18]Castleman KR. Digital image processing[M]. New Jersey:Prentice Hall,1996.
[19]Pikaz A, Averbuch A. Digital image thresholding based on topological stablestate [J].Pattern Recognition,1996,29(5):829-843.
[20]孔明,孙希平,王永骥.一种改进的基于类间方差的阈值分割法,华中科技大学学报(自然科学版)[J],2004年7期,46-47.
[21]G. Corneloup, J. Moysan. Bscan Image Segmentation by Thresholding Using Cooccurence Matrix Analysis [J]. Pattern Recognition,1996,29(2):281-296.
[22]N. Ostu. A Thresholding Selection Method from Gray-level Histogram[J]. IEEE Transon Systems, Man and Cybernetics,1978, SMC-9(1):62-66.
[23]Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram [J]. Computer Vision, Graphics and Image Processing,1985,29:273-285.
[24]Abutaleb A S. Automatic thresholding of gray-level pictures using two dimension entropy [J]. Computer Vision, Graphics and Image Processing,1989,47:22-32.
[25]刘文萍,吴立德.图像分割中阈值选取方法比较研究[J].模式识别与人工智能,1997,10(3)：274-277.
[26]J.F. Mangin, V. Frouin, I. Blach, et al. From3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations[J]. J. Math. Imaging Vis.,1995,5:297-318.
[27]J.K. Udupa, S. Samarasekera. Fuzzy connectedness and object definition: theory,algorithms and applications in image segmentation [J]. Graph. Models Image Process.1996,58(3):246-261
[28]Sijbers J, Scheunders P, Verhoye M, et al. Watershed-based segmentation of3D MR data for volume quantization [J]. Journal of Magnetic Resonance Imaging,1997,15:679-88.
[29]Chen CW, Luo J, Parker KJ, et al. A knowledge-based approach to volumetric medical image segmentation[C]. IEEE International Conference on Image Processing,1994,3:493-497.
[30]Zimmermann H J. Fuzzy set theory and its application [M]. Boston:Kluwer,1991, pp.217-240.
[31]叶秀清,顾伟康.快速模糊分割算法[J].模式识别与人工智能,1996,9(1)：66-70.
[32]Canny J. A Computational Approach to Edge Detection[J]. IEEE Trans on Pattern Analysis and Machine Intelligence.1986,8(6):679-698.
[33]汪军峰,郭佑民,金晨望,牛刚.图像分割在医学图像中的研究方法及应用[J],中国医学影像技术,2005年第21卷第10期,pp.1627-1630.
[34]Marr D著.姚国正,等译.视觉计算理论[M].北京：科学出版社,1988.
[35]Freeman W T. Adelson E H. The Design and Use of Steerable Filters[J]. IEEE Trans on Pattern Analysis and Machine Intelligence.1991,13(9):891-906.
[36]Liang K H, Tjahajadi T, Yang Y H. Roof Edge Detection Using Regularized Cubic B-Spline Filtering [J]. Pattern Recognition,1997,30(5):719-728.
[37]Dawant BM, Hartmann SL, Thirion JP, Maes F, Vandermeulen D, Demaerel P. Automatic3-D segmentation of internal structures of the head in MR images using a combination of similarity and freeform transformations [J]. IEEE Trans. Med. Imag.1999;18(10):909-16.
[38]宋晓东,肖李,罗迅.基于数学形态学的SAR图像分割方法[J].电脑与信息技术,2002,(5)：46-49.
[39]S. C. Amartur, D. Piraino, and Y. Takefuji. Optimization Neural Networks for the Segmentation of Magnetic Resonance Images [J]. IEEE Trans. Medical Imaging, 1992,11(2):215-220.
[40]袁野,仲崇权,秦绪佳.小波变换在三维医学图像分割中的应用[J].小型微型计算机系统,2003,24(6),1081-1083.
[41]Kashif M Rajpoot, Nasir M Rajpoot. Wavelet based segmentation of hyperspectral colontissue imagery [C]. Proceedings of IEEE INMIC,2003.
[42]李厚强,刘政凯,林峰.基于分形理论和Kohonen神经网络的纹理图像分割方法[J].计算机工程与应用,2001,(7)：44-46.
[43]BHANU B, LEE S, MING J. Adaptive Image Segmentation using a Genetic Algorithm [J]. IEEE Transactions on Systems, Man, and Cybernetics,1995,25(12):1543-1567.
[44]吴恩惠.医学影像学(第五版)[M],人民卫生出版社,2006年6月.
[45]白人驹,张雪林.医学影像诊断学(第三版)[M],人民卫生出版社,2010.
[46]汪红志,聂生东,MR脑图像组织分割的方法[J],国外医学生物医学工程分册,2005年10月,第28卷第5期,302-306.
[47]Arnold J B, Liow J-S, Schaper K A, et al. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects[J]. Neuro. Image.2001,13,931-934.
[48]Guillemaud R, Brady M. Estimating the bias field of MR images[J]. IEEE Trans. Med. Imaging1997,16,238-251.
[49]Rajapakse J C, Kruggel F. Segmentation of MR images with intensity inhomogeneities [J]. Image Vision Comput1998,16,165-180.
[50]Zijdenbos A, Dawant B. Brain segmentation and white matter lesion detection in MR images [J]. Crit. Rev. Biomed. Eng.1994,22(5-6),401-465.
[51]Tamraz, J.C., Comair, Y.G.,2000. Atlas of Regional Anatomy of the Brain Using MRI with Functional Correlations[C]. Springer-Verlag, Berlin/Heidelberg.
[52]Hornad J P.1996. The Basics of MRI, http://www.cis.rit.edu/htbooks/mri/inside.htm.
[53]Boubakeur Belaroussi, Julien Milles, Sabin Carme, etal. Intensity non-uniformity correction in MRI:Existing methods and their validation [J]. Medical Image Analysis2006,10(2)234-246.
[54]Axel L, Constantini J, Listerud J. Intensity correction in surface coil MR imaging. Am[J]. Roentgenol.1987,148,418-420.
[55]Mihara H, Iriguchi N, Ueno S. A method of RF inhomogeneity correction in MR imaging [J]. Magn. Reson. Mater. Phys.1998,7(2),115-120.
[56]Fan A, Wells W, Fisher J, et al. A unified variational approach to denoising and bias correction in MR. In:International Conference on Information Processing in Medical Imaging [C].2003,(IPMI_03).148-159.
[57]Murakami J, Hayes C, Weinberger E. Intensity correction of phased-array surface coil images[J]. Magnet. Reson. Med.1996,35(4),585-590.
[58]Deichmann R, Good C, Turner R. RF inhomogeneity compensation in structural brain imaging[J]. Magnet. Reson. Med.2002,47(2),398-402.
[59]Manjon J. V., Lull J., Carbonell-Caballero J., etal. A nonparametric MRI inhomogeneity correction method[J], Medical Image Analysis (2007), doi:10.1016/j. media.2007.03.001.
[60]Urox Vovk, Franjo Pernux, and Boxtjan Likar. Intensity inhomogeneity correction of multispectral MR images[J]. Neurolmage,2006,32(1),54-61.
[61]Meyer C, Bland P, Pipe J. Retrospective correction of intensity inhomogeneities in MRI [J]. IEEE Trans. Med. Imaging,1995,14(1),36-41.
[62]Styner M, Brechbuhler C, Szekely G, etal. Parametric estimate of intensity inhomogeneities applied to MRI [J]. IEEE Trans. Med. Imaging,2000,19(3):153-165.
[63]Lim K, Pfefferbaum A. Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter [J]. Comput. Assist. Tomo.1989,13(4):588-593.
[64]Zhou L, Zhu Y, Bergot C, Laval-Jeantet A-M, et al. A method of radio-frequency inhomogeneity correction for brain tissue segmentation in MRI [J]. Comput. Med. Imag. Grap.2001,25(5),379-389.
[65]Brinkmann B, Manduca A, Robb R. Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction [J]. IEEE Trans. Med. Imaging1998,17(2),161-171.
[66]Wald L, Carvajal L, Moyher S, etal. Phased array detectors and an automated intensity-correction algorithm for high-resolution MR imaging of the human brain[J]. Magnet. Reson. Med.1995,34(3),433-439.
[67]Sled J, Pike G. Standing-wave and RF penetration artifacts caused by elliptic geometry:an electrodynamic analysis of MRI [J]. IEEE Trans. Med. Imaging1998,17(4),653-662.
[68]Lin F-H, Chen Y-J, Belliveau J, etal. A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction [J]. Hum. Brain Mapp.2003,19(2),96-111.
[69]Van Leemput K, Maes F, Vandermeulen D, et al. Automated model-based bias field correction of MR images of the brain [J]. IEEE Trans. Med. Imaging1999,18(10),885-896.
[70]Wells III WM, Grimson WEL, Kikinis R, etal. Adaptive segmentation of MRI data.[J]. IEEE Trans. Med. Imaging1996,15(4),429-442.
[71]Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm [J]. IEEE Trans. Med. Imaging2001,20(1),45-57.
[72]Prima S, Ayache N, Barrick T, Roberts N. Maximum likelihood estimation of the bias field in MR brain images:investigating different modelings of the imaging process. In:International Conference on Medical Image Computing and Computer-Assisted Intervention[C].(MICCAI_01).2001.811-819.
[73]Pham D, Prince J. An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities[J]. Pattern Recogn. Lett.1999c.20(1),57-68.
[74]Gispert J, Reig S, Pascau J, etal. Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error[J]. Hum. Brain Mapp.2004,22(2),133-144.
[75]Shenghua Gu, Tianming Zhan, Yingchao Zhang. Brain MR Image Segmentation and Bias Correction Based on Gaussian mixture model and linear regression method[C]. The Proceedings of2010International Conference on Probability and Statistics of the International,2010,355-358.
[76]王海军,柳明.克服灰度不均匀性的脑MR图像分割及去偏移场模型[J].山东大 学学报(工学版),2011年6月,第41卷第3期,36-41.
[77]Decarli C, Murphy DGM, Teichberg D, et al. Local histogram correction of MRI spatially dependent image pixel intensity nonuniformity [J]. J Magnetic Resonance Imaging,1996,6(3),519-528.
[78]Sled JG, Zijdenbos A P, Evans AC. A nonparametric method for automatic correct ion of intensity nonuniformity in MRI data [J]. IEEE Trans Med Imaging,1998,17(1):87-97.
[79]Pham D, Prince J. An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities [J]. Pattern Recogn. Lett.1999,20(1),57-68
[80]罗述谦,唐宇.基于有偏场的适配模糊聚类分割算法[J].中国图象图形学报.2002,7(2)：111-114.
[81]Ahmed M, Yamany S, Mohamed N, et al. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data [J]. IEEE Trans. Med. Imaging.2002,21(3),193-199.
[82]Li Bin and Chen Wufan, Inhomogeneous magnetic resonance image segmentation by spatial model to fuzzy clustering [J], Chinese Medical Equipment Journal, vol.27, no.2,3-4,2006.
[83]Karan Sikka, Amit K. Mishra. A fully automated algorithm under modified FCM framework for improved brain MR image segmentation[J]. Magnetic Resonance Imaging.2009,27(7),994-1004.
[84]Liang Liao, Tusheng Lin, MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach[J]. Pattern Recognition Letters.2008,29(10):1580-1588.
[85]Laszlo Szilagyi, Sandor M. Szilagyi, Balazs Bony and Zoltan Benyo, Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models[J]. Biomedical Signal Processing and Control,6(2011),3-12.
[86]A.W.C. Liew, Y. Hong, An adaptive spatial fuzzy clustering algorithm for3-D MR image segmentation[J], IEEE Transactions on Medical Imaging22(9),2003 1063-1075.
[87]Tianming Zhan,Zhihui Wei,Qi Ge,Liang Xiao. Brain MR Image Segmentation and Bias Field Correction Using Adaptive Fuzzy C Means Model[C]. International Conference on Computer,Mechatronics, Control and Electronic Engineering(CMCE),2010,151-154.
[88]Zhu Chaozhe, T Jiang. Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images [J]. Neurolmage.2003,18(3):685-696.
[89]Vikram Chalana and Yongmin Kim. A methodology for evaluation of boundary detection algorithms on medical images[J]. IEEE Trans, on Medical Imaging,1997,16(5):642-652.
[90]张石,董建威,余黎煌.医学图像分割算法的评价方法[J],中国图象图形学报,2009年9月第14卷第9期,1872-1880.
[91]Y. J. Zhang. A survey on evaluation methods for image segmentation[J], Pattern Recognition,1996,29(8):1335-1346.
[92]Chabrier S, Emile B, Rosenberger C, et al. Unsupervised performance evaluation of image segmentation [J]. EURASIP Journal on Applied Signal Processing,2006,15,1-12.
[93]Levine M D. Nazif A M. Dynamic measurement of computer generated image segmentations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1985,7(2):155-164.
[94]Chabrier S, Rosenberger C, Laurent H, et al. Evaluating the segmentation result of a gray-level image[A]. In:Proceedings of2th European Signal Processing Conference[C], Vienna:European Association for Signal, Speech and Image Processing,2004:953-956.
[95]Worth A. The Internet Brain Segmentation Repository (IBSR)[EB/OL]. http://www.cma.mgh.harvard.edu/ibsr/,2006-6-20/2007-6-15.
[96]BrainWeb:Simulated Brain Database [EB/OL]. http://www.Bic.mni.mcgill.ca/brainweb/,2006-6-12/2007-6-17.
[97]Zijdenbos A, Dawant B. Brain segmentation and white matter lesion detection in MR images [J]. Crit. Rev. Biomed. Eng.1994,22(5-6):401-465.
[98]L.A.Zadeh,Fuzzy sets[J], Information and Control,vol.8,338-353,1965.
[99]边肇祺,张学工.模式识别(第二版)[M],清华大学出版社,北京,2000年1月.
[100]Jayaram K. Udupa, Supun Samarasekera. Fuzzy Connectedness and Object Defination:Theory Algorithms and Applications in Image Segmentation[J]. Graphical Models and Image Processing,1996,58(3):246-241.
[101]Sai prasad ramy. Low-Level Segmentation of3D Magnetic Resonance Images-A Rule-Based System [J]. IEEE Trans. Medical Imaging,1990,9(3):327-337.
[102]Matthew C Clark, Lawrence O Hall, Dmirty B Goldgof, etal. Automatic Tumor Segmentation Using knowledge-Based Technique. IEEE Trans [J]. Medical Imaging,1998,17(2):187-201.
[103]M C Clark. MRI segmentation using fuzzy clustering techniques [J]. IEEE Engineering in Medicine and Biology,1994,13(5):730-742.
[104]Bezdek JC. Patter recognition with fuzzy objective function algorithm[M]. New York: Plenum Press,1981.
[105]L Nocera, J C Gee,"Robust partial volume tissue classification of cerebral MRI scans"[J]. Proc. SPIE:Med. Imaging,1997,3034,312-322.
[106]林盘,郑崇勋,杨勇等.一种鲁棒的人脑核磁共振图像分割算法研究[J].电子与信息学报.2005,27(9)：1420-1424.
[107]Likar B, Viergever MA, Pernus F. Retrospective correction of MR intensity inhomogeneity by information minimization [J]. IEEE Trans Med Imaging,2001,20(12):1398-1410.
[108]Kwan R-S, Evans A, Pike G. MRI simulation-based evaluation of image-processing and classification methods [J]. IEEE Trans. Med. Imaging.1999,18(11):1085-1097.
[109]DN Kennedy, PA Filipek, VS Caviness. Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging [J]. IEEE Transactions on Medical Imaging.1989,8(1),1-7.
[110]Jun Kong, Na Che. Local-based Fuzzy Clustering Algorithm for Magnetic Resonance Brain Images Corrupted by Intensity Heterogeneity [C]. International Multi-Symposiums on Computer and Computational Sciences.2007:150-157.
[111]Fuping Zhu,Jie Tian,Medical image segmentation using level set and watershed transform. In Proceedings of SPIE Advanced Biomedical and Clinical Diagnostic Systems2003,294-302.
[112]Tim W.Nattkemper, et al,"Automatic segmentation of digitalmicrographs:A survey,' in MEDINFO, San Francisco,USA,2004, AMIA/IMIA.
[113][美]冈萨雷斯Gonzalez R C等著.《数字图像处理》[M].阮秋琦等译.北京：电子工业出版社,2003.
[114]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京：清华大学出版社,2000.
[115]BHANU B, L EE S, MINGJ. Adaptive Image Segmentation using a Genetic Algorithm[J]. IEEE Transactions on Systems,Man,and Cybernetics,1995,25(12),1543-1567.
[116]郑宏,潘励.基于遗传算法的图像阈值的自动选取[J].中国图像图形学报,1999,4(4)327-330.
[117]Vincent L and Soille P.:Watersheds in digital spaces:An efficient algorithm based on immersion simulations[J]. IEEE Trans, on Pattern Analysis and Machine Intelligence, vol.13, no.6,583-598,1991.
[118]汪新凡,小基选择及其优化,珠江工学院学报[J],2003.9,第5期第17卷,33-35.
[119]胡敏,陈强洪.多尺度分析方法中四种典型小波基的选择与比较[J].微机发展,2002,第3期,41-44.
[120]D.L.Donoho:'De-noising by soft thresholding', IEEE Trans. On Inform,1995,613-627.
[121]Y.Bentoutou, et al. An invariant approach for image registration in digital subtraction angiography[J]. Pattern Recognition,35(2002),2853-2865.
[122]F.L.Bookstein et al. Prioncipal warps. Thin-plate splines and the decompositions. IEEE-PAMI,1989(2),567-585.
[123]张相芬,肖蜀隽,叶宏.磁共振图像小波去噪方法的参数选择研究[J].鲁东大学学报(自然科学版),2010,26(4),318-321.
[2]田捷等.医学影像处理与分析[M].北京：电子工业出版社,2003.
[3]Amit Chakraboriy, Lawrence H. Staib, James S. Duncan. Deformable Boundary Finding in Medical Image by Integrating Gradient and Region Information[J]. IEEE Trans. Medical Imaging,1996,1(6):859-870.
[4]B Johnston, M S Atkins, B Mackiewich, etal. Segmentation of Multiple Sclerosis Lesions in Intensity Corrected Multispectral MRI [J]. IEEE Trans. Medical Imaging,1996,15(2):154-169.
[5]C Tsai. Automated segmentation of brain MR images [J]. Pattern Recognition,1995,28(12):1825-1837.
[6]Kevin P. Hinshaw. Shape-based models for interactive segmentation of medical images in Image Processing [J], Murrayh. loew, editor, Proc. SPIE,1995,2434:771-780.
[7]Jayaram K. Udupa and Supun Samarasekera. Fuzzy Connectedness and Object Definition:Theory, Algorithms, and Applications in Image Segmentation[J]. Graphical Models and Image Processing,1996,58(3):246-251.
[8]Norberto Ezquerra and Rakesh Mullick. Knowledge2guided segmentation of3D imagery [J]. Graphical Models and Image Processing,1996,58(6):510-523.
[9]王合.医学图像边缘检测技术的研究[D].上海交通大学博士学位论文,1996,1-4.
[10]B. Mackiewich. Intracranial boundary detection and radio frequency correction in magnetic resonance images. M. S. thesis, Simon Fraster Univ, Burnaby, B. C, Canada, Aug,1995.
[11]L P Clarke, R P Velthuizen, M A Camacho, etal. MRI segmentation:Methods and application [J]. Magnetic Resonance Imaging,1995,13(3):343-368.
[12]J C Bezdek, L O Hall, L P Clarke. Review of MR image segmentation techniques using pattern recognition [J]. Med. Phys.1993,20(4):1033-1048.
[13]N. R. Par, S. K. Par. A Review on Image Segmentation Techniques[J]. Pattern Recognition,1993,26(9):1277-1294.
[14]D. Zung, L. Pham, Chenyang xu. Current Methods in Medical Image Segmentation.Annu[J]. Rev. Biomed. Eng.2000,02:315-338.
[15]Niessen W., Vincken K., Weickert J., Haar Romeny B., Viergever M.:Multiscale segmentation of three dimensional MR brain images[J]. Internat. J. Comput. Vision31(2/3),(1999)185-202.
[16]Alan Wee-Chung Liew, Hong Yan:Current Methods in the Automatic Tissue Segmentation of3D Magnetic Resonance Brain Images [J]. Current Medical Imaging Reviews, Vol.2, No.1,2006,91-103.
[17]J S Suri, S Singh, L Reden. Computer Vision and Pattern Recognition Techniques for2D and3D MR Cerebral Cortical Segmentation (Part I):A State-of-the-Art Review. Pattern Analysis&Application[J].2002(5):46-76.
[18]Castleman KR. Digital image processing[M]. New Jersey:Prentice Hall,1996.
[19]Pikaz A, Averbuch A. Digital image thresholding based on topological stablestate [J].Pattern Recognition,1996,29(5):829-843.
[20]孔明,孙希平,王永骥.一种改进的基于类间方差的阈值分割法,华中科技大学学报(自然科学版)[J],2004年7期,46-47.
[21]G. Corneloup, J. Moysan. Bscan Image Segmentation by Thresholding Using Cooccurence Matrix Analysis [J]. Pattern Recognition,1996,29(2):281-296.
[22]N. Ostu. A Thresholding Selection Method from Gray-level Histogram[J]. IEEE Transon Systems, Man and Cybernetics,1978, SMC-9(1):62-66.
[23]Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram [J]. Computer Vision, Graphics and Image Processing,1985,29:273-285.
[24]Abutaleb A S. Automatic thresholding of gray-level pictures using two dimension entropy [J]. Computer Vision, Graphics and Image Processing,1989,47:22-32.
[25]刘文萍,吴立德.图像分割中阈值选取方法比较研究[J].模式识别与人工智能,1997,10(3)：274-277.
[26]J.F. Mangin, V. Frouin, I. Blach, et al. From3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations[J]. J. Math. Imaging Vis.,1995,5:297-318.
[27]J.K. Udupa, S. Samarasekera. Fuzzy connectedness and object definition: theory,algorithms and applications in image segmentation [J]. Graph. Models Image Process.1996,58(3):246-261
[28]Sijbers J, Scheunders P, Verhoye M, et al. Watershed-based segmentation of3D MR data for volume quantization [J]. Journal of Magnetic Resonance Imaging,1997,15:679-88.
[29]Chen CW, Luo J, Parker KJ, et al. A knowledge-based approach to volumetric medical image segmentation[C]. IEEE International Conference on Image Processing,1994,3:493-497.
[30]Zimmermann H J. Fuzzy set theory and its application [M]. Boston:Kluwer,1991, pp.217-240.
[31]叶秀清,顾伟康.快速模糊分割算法[J].模式识别与人工智能,1996,9(1)：66-70.
[32]Canny J. A Computational Approach to Edge Detection[J]. IEEE Trans on Pattern Analysis and Machine Intelligence.1986,8(6):679-698.
[33]汪军峰,郭佑民,金晨望,牛刚.图像分割在医学图像中的研究方法及应用[J],中国医学影像技术,2005年第21卷第10期,pp.1627-1630.
[34]Marr D著.姚国正,等译.视觉计算理论[M].北京：科学出版社,1988.
[35]Freeman W T. Adelson E H. The Design and Use of Steerable Filters[J]. IEEE Trans on Pattern Analysis and Machine Intelligence.1991,13(9):891-906.
[36]Liang K H, Tjahajadi T, Yang Y H. Roof Edge Detection Using Regularized Cubic B-Spline Filtering [J]. Pattern Recognition,1997,30(5):719-728.
[37]Dawant BM, Hartmann SL, Thirion JP, Maes F, Vandermeulen D, Demaerel P. Automatic3-D segmentation of internal structures of the head in MR images using a combination of similarity and freeform transformations [J]. IEEE Trans. Med. Imag.1999;18(10):909-16.
[38]宋晓东,肖李,罗迅.基于数学形态学的SAR图像分割方法[J].电脑与信息技术,2002,(5)：46-49.
[39]S. C. Amartur, D. Piraino, and Y. Takefuji. Optimization Neural Networks for the Segmentation of Magnetic Resonance Images [J]. IEEE Trans. Medical Imaging, 1992,11(2):215-220.
[40]袁野,仲崇权,秦绪佳.小波变换在三维医学图像分割中的应用[J].小型微型计算机系统,2003,24(6),1081-1083.
[41]Kashif M Rajpoot, Nasir M Rajpoot. Wavelet based segmentation of hyperspectral colontissue imagery [C]. Proceedings of IEEE INMIC,2003.
[42]李厚强,刘政凯,林峰.基于分形理论和Kohonen神经网络的纹理图像分割方法[J].计算机工程与应用,2001,(7)：44-46.
[43]BHANU B, LEE S, MING J. Adaptive Image Segmentation using a Genetic Algorithm [J]. IEEE Transactions on Systems, Man, and Cybernetics,1995,25(12):1543-1567.
[44]吴恩惠.医学影像学(第五版)[M],人民卫生出版社,2006年6月.
[45]白人驹,张雪林.医学影像诊断学(第三版)[M],人民卫生出版社,2010.
[46]汪红志,聂生东,MR脑图像组织分割的方法[J],国外医学生物医学工程分册,2005年10月,第28卷第5期,302-306.
[47]Arnold J B, Liow J-S, Schaper K A, et al. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects[J]. Neuro. Image.2001,13,931-934.
[48]Guillemaud R, Brady M. Estimating the bias field of MR images[J]. IEEE Trans. Med. Imaging1997,16,238-251.
[49]Rajapakse J C, Kruggel F. Segmentation of MR images with intensity inhomogeneities [J]. Image Vision Comput1998,16,165-180.
[50]Zijdenbos A, Dawant B. Brain segmentation and white matter lesion detection in MR images [J]. Crit. Rev. Biomed. Eng.1994,22(5-6),401-465.
[51]Tamraz, J.C., Comair, Y.G.,2000. Atlas of Regional Anatomy of the Brain Using MRI with Functional Correlations[C]. Springer-Verlag, Berlin/Heidelberg.
[52]Hornad J P.1996. The Basics of MRI, http://www.cis.rit.edu/htbooks/mri/inside.htm.
[53]Boubakeur Belaroussi, Julien Milles, Sabin Carme, etal. Intensity non-uniformity correction in MRI:Existing methods and their validation [J]. Medical Image Analysis2006,10(2)234-246.
[54]Axel L, Constantini J, Listerud J. Intensity correction in surface coil MR imaging. Am[J]. Roentgenol.1987,148,418-420.
[55]Mihara H, Iriguchi N, Ueno S. A method of RF inhomogeneity correction in MR imaging [J]. Magn. Reson. Mater. Phys.1998,7(2),115-120.
[56]Fan A, Wells W, Fisher J, et al. A unified variational approach to denoising and bias correction in MR. In:International Conference on Information Processing in Medical Imaging [C].2003,(IPMI_03).148-159.
[57]Murakami J, Hayes C, Weinberger E. Intensity correction of phased-array surface coil images[J]. Magnet. Reson. Med.1996,35(4),585-590.
[58]Deichmann R, Good C, Turner R. RF inhomogeneity compensation in structural brain imaging[J]. Magnet. Reson. Med.2002,47(2),398-402.
[59]Manjon J. V., Lull J., Carbonell-Caballero J., etal. A nonparametric MRI inhomogeneity correction method[J], Medical Image Analysis (2007), doi:10.1016/j. media.2007.03.001.
[60]Urox Vovk, Franjo Pernux, and Boxtjan Likar. Intensity inhomogeneity correction of multispectral MR images[J]. Neurolmage,2006,32(1),54-61.
[61]Meyer C, Bland P, Pipe J. Retrospective correction of intensity inhomogeneities in MRI [J]. IEEE Trans. Med. Imaging,1995,14(1),36-41.
[62]Styner M, Brechbuhler C, Szekely G, etal. Parametric estimate of intensity inhomogeneities applied to MRI [J]. IEEE Trans. Med. Imaging,2000,19(3):153-165.
[63]Lim K, Pfefferbaum A. Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter [J]. Comput. Assist. Tomo.1989,13(4):588-593.
[64]Zhou L, Zhu Y, Bergot C, Laval-Jeantet A-M, et al. A method of radio-frequency inhomogeneity correction for brain tissue segmentation in MRI [J]. Comput. Med. Imag. Grap.2001,25(5),379-389.
[65]Brinkmann B, Manduca A, Robb R. Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction [J]. IEEE Trans. Med. Imaging1998,17(2),161-171.
[66]Wald L, Carvajal L, Moyher S, etal. Phased array detectors and an automated intensity-correction algorithm for high-resolution MR imaging of the human brain[J]. Magnet. Reson. Med.1995,34(3),433-439.
[67]Sled J, Pike G. Standing-wave and RF penetration artifacts caused by elliptic geometry:an electrodynamic analysis of MRI [J]. IEEE Trans. Med. Imaging1998,17(4),653-662.
[68]Lin F-H, Chen Y-J, Belliveau J, etal. A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction [J]. Hum. Brain Mapp.2003,19(2),96-111.
[69]Van Leemput K, Maes F, Vandermeulen D, et al. Automated model-based bias field correction of MR images of the brain [J]. IEEE Trans. Med. Imaging1999,18(10),885-896.
[70]Wells III WM, Grimson WEL, Kikinis R, etal. Adaptive segmentation of MRI data.[J]. IEEE Trans. Med. Imaging1996,15(4),429-442.
[71]Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm [J]. IEEE Trans. Med. Imaging2001,20(1),45-57.
[72]Prima S, Ayache N, Barrick T, Roberts N. Maximum likelihood estimation of the bias field in MR brain images:investigating different modelings of the imaging process. In:International Conference on Medical Image Computing and Computer-Assisted Intervention[C].(MICCAI_01).2001.811-819.
[73]Pham D, Prince J. An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities[J]. Pattern Recogn. Lett.1999c.20(1),57-68.
[74]Gispert J, Reig S, Pascau J, etal. Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error[J]. Hum. Brain Mapp.2004,22(2),133-144.
[75]Shenghua Gu, Tianming Zhan, Yingchao Zhang. Brain MR Image Segmentation and Bias Correction Based on Gaussian mixture model and linear regression method[C]. The Proceedings of2010International Conference on Probability and Statistics of the International,2010,355-358.
[76]王海军,柳明.克服灰度不均匀性的脑MR图像分割及去偏移场模型[J].山东大 学学报(工学版),2011年6月,第41卷第3期,36-41.
[77]Decarli C, Murphy DGM, Teichberg D, et al. Local histogram correction of MRI spatially dependent image pixel intensity nonuniformity [J]. J Magnetic Resonance Imaging,1996,6(3),519-528.
[78]Sled JG, Zijdenbos A P, Evans AC. A nonparametric method for automatic correct ion of intensity nonuniformity in MRI data [J]. IEEE Trans Med Imaging,1998,17(1):87-97.
[79]Pham D, Prince J. An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities [J]. Pattern Recogn. Lett.1999,20(1),57-68
[80]罗述谦,唐宇.基于有偏场的适配模糊聚类分割算法[J].中国图象图形学报.2002,7(2)：111-114.
[81]Ahmed M, Yamany S, Mohamed N, et al. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data [J]. IEEE Trans. Med. Imaging.2002,21(3),193-199.
[82]Li Bin and Chen Wufan, Inhomogeneous magnetic resonance image segmentation by spatial model to fuzzy clustering [J], Chinese Medical Equipment Journal, vol.27, no.2,3-4,2006.
[83]Karan Sikka, Amit K. Mishra. A fully automated algorithm under modified FCM framework for improved brain MR image segmentation[J]. Magnetic Resonance Imaging.2009,27(7),994-1004.
[84]Liang Liao, Tusheng Lin, MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach[J]. Pattern Recognition Letters.2008,29(10):1580-1588.
[85]Laszlo Szilagyi, Sandor M. Szilagyi, Balazs Bony and Zoltan Benyo, Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models[J]. Biomedical Signal Processing and Control,6(2011),3-12.
[86]A.W.C. Liew, Y. Hong, An adaptive spatial fuzzy clustering algorithm for3-D MR image segmentation[J], IEEE Transactions on Medical Imaging22(9),2003 1063-1075.
[87]Tianming Zhan,Zhihui Wei,Qi Ge,Liang Xiao. Brain MR Image Segmentation and Bias Field Correction Using Adaptive Fuzzy C Means Model[C]. International Conference on Computer,Mechatronics, Control and Electronic Engineering(CMCE),2010,151-154.
[88]Zhu Chaozhe, T Jiang. Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images [J]. Neurolmage.2003,18(3):685-696.
[89]Vikram Chalana and Yongmin Kim. A methodology for evaluation of boundary detection algorithms on medical images[J]. IEEE Trans, on Medical Imaging,1997,16(5):642-652.
[90]张石,董建威,余黎煌.医学图像分割算法的评价方法[J],中国图象图形学报,2009年9月第14卷第9期,1872-1880.
[91]Y. J. Zhang. A survey on evaluation methods for image segmentation[J], Pattern Recognition,1996,29(8):1335-1346.
[92]Chabrier S, Emile B, Rosenberger C, et al. Unsupervised performance evaluation of image segmentation [J]. EURASIP Journal on Applied Signal Processing,2006,15,1-12.
[93]Levine M D. Nazif A M. Dynamic measurement of computer generated image segmentations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1985,7(2):155-164.
[94]Chabrier S, Rosenberger C, Laurent H, et al. Evaluating the segmentation result of a gray-level image[A]. In:Proceedings of2th European Signal Processing Conference[C], Vienna:European Association for Signal, Speech and Image Processing,2004:953-956.
[95]Worth A. The Internet Brain Segmentation Repository (IBSR)[EB/OL]. http://www.cma.mgh.harvard.edu/ibsr/,2006-6-20/2007-6-15.
[96]BrainWeb:Simulated Brain Database [EB/OL]. http://www.Bic.mni.mcgill.ca/brainweb/,2006-6-12/2007-6-17.
[97]Zijdenbos A, Dawant B. Brain segmentation and white matter lesion detection in MR images [J]. Crit. Rev. Biomed. Eng.1994,22(5-6):401-465.
[98]L.A.Zadeh,Fuzzy sets[J], Information and Control,vol.8,338-353,1965.
[99]边肇祺,张学工.模式识别(第二版)[M],清华大学出版社,北京,2000年1月.
[100]Jayaram K. Udupa, Supun Samarasekera. Fuzzy Connectedness and Object Defination:Theory Algorithms and Applications in Image Segmentation[J]. Graphical Models and Image Processing,1996,58(3):246-241.
[101]Sai prasad ramy. Low-Level Segmentation of3D Magnetic Resonance Images-A Rule-Based System [J]. IEEE Trans. Medical Imaging,1990,9(3):327-337.
[102]Matthew C Clark, Lawrence O Hall, Dmirty B Goldgof, etal. Automatic Tumor Segmentation Using knowledge-Based Technique. IEEE Trans [J]. Medical Imaging,1998,17(2):187-201.
[103]M C Clark. MRI segmentation using fuzzy clustering techniques [J]. IEEE Engineering in Medicine and Biology,1994,13(5):730-742.
[104]Bezdek JC. Patter recognition with fuzzy objective function algorithm[M]. New York: Plenum Press,1981.
[105]L Nocera, J C Gee,"Robust partial volume tissue classification of cerebral MRI scans"[J]. Proc. SPIE:Med. Imaging,1997,3034,312-322.
[106]林盘,郑崇勋,杨勇等.一种鲁棒的人脑核磁共振图像分割算法研究[J].电子与信息学报.2005,27(9)：1420-1424.
[107]Likar B, Viergever MA, Pernus F. Retrospective correction of MR intensity inhomogeneity by information minimization [J]. IEEE Trans Med Imaging,2001,20(12):1398-1410.
[108]Kwan R-S, Evans A, Pike G. MRI simulation-based evaluation of image-processing and classification methods [J]. IEEE Trans. Med. Imaging.1999,18(11):1085-1097.
[109]DN Kennedy, PA Filipek, VS Caviness. Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging [J]. IEEE Transactions on Medical Imaging.1989,8(1),1-7.
[110]Jun Kong, Na Che. Local-based Fuzzy Clustering Algorithm for Magnetic Resonance Brain Images Corrupted by Intensity Heterogeneity [C]. International Multi-Symposiums on Computer and Computational Sciences.2007:150-157.
[111]Fuping Zhu,Jie Tian,Medical image segmentation using level set and watershed transform. In Proceedings of SPIE Advanced Biomedical and Clinical Diagnostic Systems2003,294-302.
[112]Tim W.Nattkemper, et al,"Automatic segmentation of digitalmicrographs:A survey,' in MEDINFO, San Francisco,USA,2004, AMIA/IMIA.
[113][美]冈萨雷斯Gonzalez R C等著.《数字图像处理》[M].阮秋琦等译.北京：电子工业出版社,2003.
[114]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京：清华大学出版社,2000.
[115]BHANU B, L EE S, MINGJ. Adaptive Image Segmentation using a Genetic Algorithm[J]. IEEE Transactions on Systems,Man,and Cybernetics,1995,25(12),1543-1567.
[116]郑宏,潘励.基于遗传算法的图像阈值的自动选取[J].中国图像图形学报,1999,4(4)327-330.
[117]Vincent L and Soille P.:Watersheds in digital spaces:An efficient algorithm based on immersion simulations[J]. IEEE Trans, on Pattern Analysis and Machine Intelligence, vol.13, no.6,583-598,1991.
[118]汪新凡,小基选择及其优化,珠江工学院学报[J],2003.9,第5期第17卷,33-35.
[119]胡敏,陈强洪.多尺度分析方法中四种典型小波基的选择与比较[J].微机发展,2002,第3期,41-44.
[120]D.L.Donoho:'De-noising by soft thresholding', IEEE Trans. On Inform,1995,613-627.
[121]Y.Bentoutou, et al. An invariant approach for image registration in digital subtraction angiography[J]. Pattern Recognition,35(2002),2853-2865.
[122]F.L.Bookstein et al. Prioncipal warps. Thin-plate splines and the decompositions. IEEE-PAMI,1989(2),567-585.
[123]张相芬,肖蜀隽,叶宏.磁共振图像小波去噪方法的参数选择研究[J].鲁东大学学报(自然科学版),2010,26(4),318-321.