[1]王大溪,陈鑫鑫. 基于形态学的脑部MRI图像颅骨剥离算法[J].计算机技术与发展,2015,25(12):206-209.
 WANG Da-xi,CHEN Xin-xin. Algorithm of Brain MRI Image Skull Stripping Based on Morphology[J].,2015,25(12):206-209.
点击复制

 基于形态学的脑部MRI图像颅骨剥离算法()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年12期
页码:
206-209
栏目:
应用开发研究
出版日期:
2015-12-10

文章信息/Info

Title:
 Algorithm of Brain MRI Image Skull Stripping Based on Morphology
文章编号:
1673-629X(2015)12-0206-04
作者:
 王大溪陈鑫鑫
 广西科技大学 电气与信息工程学院
Author(s):
 WANG Da-xiCHEN Xin-xin
关键词:
 各向异性扩散最大类间方差法形态学二值图
Keywords:
 anisotropic diffusionOtsumorphologybinary image
分类号:
TP391
文献标志码:
A
摘要:
 近年来,脑部MRI图像分割问题渐渐成为医学图像处理领域一个新的研究热点. 脑部磁共振图像分割是对图像分析的必要准备,能否很好地对图像进行分析,一定程度上取决于图像分割的准确性. 由此可知,图像的准确分割对医学临床诊断有着重要意义. 然而,脑部MRI图像中的头皮、颅骨、肌肉、血管等非脑组织会严重影响脑组织的准确分割. 文中采用最大类间方差法和形态学算子剥离脑部MRI图像的颅骨部分. 首先,利用一种基于各向异性扩散方程的方法抑制MRI图像的噪声. 然后,运用最大类间方差法将图像转化为二值图,接着对二值图进行一系列形态学处理,最后,将得到的二值化脑组织模板映射到原图像. 实验结果表明该算法的分割效果良好.
Abstract:
 In recent years,the segmentation of MRI brain images is becoming a new hotspot of medical image processing. The segmenta-tion of MRI brain images is a necessary preparation for image analysis. How well the image analysis,to some extent,depends on the accu-racy of image segmentation. So,accurate image segmentation is important for clinical diagnosis. Scalp,skull,muscle,blood vessels and other non-brain tissues contained in the brain MRI image will seriously affect the accuracy of segmentation of brain tissues. In this paper, strip skull from the MRI brain images with Otsu and morphological operators. Firstly,a method using the anisotropic diffusion equation was applied to suppress the noise of the MRI image. After that,the image was converted to binary image and then a series of morphologi-cal processing were applied to the binary image. Finally,the obtained binary brain tissue was mapped back to the original image. The test result has proved that the algorithm has a good performance in image segmentation.

相似文献/References:

[1]汪继文 林胜华 沈玉峰 邱剑锋.一种基于各向异性扩散的图像处理方法[J].计算机技术与发展,2008,(08):98.
 WANG Ji-wen,LIN Sheng-hua,SHEN Yu-feng,et al.An Approach for Image Restoration Based on Anisotropic Diffusion[J].,2008,(12):98.
[2]柯丹丹 蔡光程 曹倩倩.基于形态学算子的各向异性扩散去噪方法[J].计算机技术与发展,2012,(04):81.
 KE Dan-dan,CAI Guang-cheng,CAO Qian-qian.An Anisotropic Diffusion Denoising Method Based on Morphological Operator[J].,2012,(12):81.
[3]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
 ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(12):1.
[4]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
 LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(12):5.
[5]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
 HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(12):13.
[6]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
 HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(12):21.
[7]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
 LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(12):25.
[8]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
 WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(12):29.
[9]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
 LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(12):34.
[10]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
 SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(12):38.

更新日期/Last Update: 2016-01-29