结合概率密度函数和主动轮廓模型的磁共振图像分割

为了提高大脑磁共振图像的分割精度, 提出了一种新的图像分割算法。首先, 分析了常用于大脑磁共振图像分割的高斯混合模型和主动轮廓模型的优缺点, 联合高斯混合模型的概率密度函数和主动轮廓模型的能量函数构造了一个新的能量函数。然后, 利用遗传算法和最大期望算法获取概率密度函数的参数值。最后, 利用水平集方法和梯度下降流法, 对获得的能量函数进行最小化, 从而得到最终的分割结果。与传统方法相比, 本文算法对脑组织中的白质和灰质的分割精度分别提高了6.73%和14.07%。该算法利用像素点的区域信息和概率值驱动主动轮廓曲线的演化, 能有效区分具有相近灰度值的不同区域, 从而提高了大脑磁共振图像的分割精度。

Abstract

To improve the segmentation precision of brain Magnetic Resonance(MR) imaging, a novel brain tissue automated segmentation method was proposed. Firstly, the merits and demerits of Gaussian mixture model and active contour model used for MR image segmentation were analyzed, and a new energy function was constructed through combining the probability density function of the Gaussian mixture model with the energy function of the active contour model. Then, the genetic algorithm and expectation maximization algorithm were used to get the parameter values of the probability density function. Finally, segmentation results were achieved through minimizing the novel energy function by using the level set method and the gradient descent algorithm. The experiment results clearly indicate that the segmentation accuracies of white matter and gray matter in brain tissue by the proposed method are increased by 6.73% and 14.07%, respectively as compared with that of the traditional methods. By using the area information and probability values of pixel points to drive the active contour curve, the proposed method automatically segments the brain MR image with high enough accuracy and improves the segmentation accuracy of brain MR images.

参考文献

[1] TIAN G, XIA Y, ZHANG Y,et al.. Hybrid genetic and variational expectation-maximization algorithm for Gaussian-mixture-model-based brain MR image segmentation ［J］. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(3): 373-380.

[2] SHYU K K, PHAM V T, TRAN T T, et al.. Unsupervised active contour driven by desity distance and local fiffting energy with applications to medical image segmentation ［J］. Machine Vision and Applications, 2012, 23(6): 1159-1175.

[3] 汪源源,原宗良,唐三.利用自适应纹理分布的活动形状分割前列腺磁共振图像［J］. 光学精密工程,2013,21(9): 2371-2380.

WANG Y Y, YUAN Z L, TANG S. Segmentation of prostate magnetic resonance image with active shape of adaptive texture distribution ［J］. Opt. Precision Eng., 2013,21(9): 2371-2380.(in Chinese)

[4] ZHOU Y, BAI J. Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI ［J］. IEEE Transactions on Biomedical Engineering, 2007,54(1): 122-129.

[5] BAZIN P L, PHAM D L. Topology preserving tissue classification of magnetic resonance brain images ［J］. IEEE Trans. Med. Imag., 2007, 26(4): 487-496.

[6] TOHKA J, KRESTYANNILOV E, DINOV, I D, et al.. Genetic algorithms for finite mixture model based voxel classification in neuroimaging ［J］. IEEE Transactions on Medical Imaging, 2007, 26(5): 696-711.

[7] 王醒策,文蕾,武仲科,等.面向时飞磁共振血管造影术的脑血管统计分割混合模型［J］. 光学精密工程,2014,22(2): 497-507.

WANG X C, WEN L, WU ZH K, et al.. Finiter mixtuer model of stochastic cerebrovascular segmentation based on TOF MRA［J］. Opt. Precision Eng., 2014,22(2): 497-507. (in Chinese)

[8] CHAN T, VESE L A. Active contours without edges ［J］. IEEE Transactions on Image Processing, 2001, 10(2): 266-277.

[9] HUANG A, ABUGHARBIEH R, TAM R, et al.. A hybrid geometric-statistical deformable model for automated 3-D segmentation in brain MRI ［J］. IEEE Transactions on Biomedical Engineering, 2009, 56(7): 1838-1847.

[10] CHOI H S, HAYNOR D R, KIM Y. Partial volume tissue classification of multichannel magnetic resonance images-A mixel model ［J］.IEEE Transactions on Medical Image,1991,10 (3): 395-407.

[11] 吴迪,曹洁,王进花.基于自适应高斯混合模型与静动态听觉特征融合的说话人识别［J］.光学精密工程, 2013,21(6): 1598-1604.

WU D, CAO J, WANG J H. Speaker recognition based on adapted Gaussian mixture model and static and dynamic auditory feature fusion ［J］. Opt. Precision Eng., 2013,21(6): 1598-1604. (in Chinese)

[12] 姜慧研,冯锐杰. 基于改进的变分水平集和区域生长的图像分割方法的研究［J］.电子学报,2012,40(8): 1659-1664.

JIANG H Y , FENG R J. Image segmentation method research based on improved variational level set and region growth ［J］. Acta Electronica Sinica., 2012,40(8): 1659-1664. (in Chinese)

[13] MORENO J C, SURYA P V B, PEROENCA H, et al.. Fast and globally convex multiphase active contours for brain MRI segmentation［J］. Computer Vision and Image Understanding, 2014, (125): 237-250.

[14] KIM W, KIM C. Active contours driven by the salient edge energy model ［J］. IEEE Transactions on Image Processing, 2013, 22(4): 1667-1673.

刘建磊, 隋青美, 朱文兴. 结合概率密度函数和主动轮廓模型的磁共振图像分割[J]. 光学精密工程, 2014, 22(12): 3435. LIU Jian-lei, SUI Qing-mei, ZHU Wen-xing. MR image segmentation based on probability density function and active contour model[J]. Optics and Precision Engineering, 2014, 22(12): 3435.

结合概率密度函数和主动轮廓模型的磁共振图像分割

关于本站 Cookie 的使用提示

全站搜索

结合概率密度函数和主动轮廓模型的磁共振图像分割

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索