MRI的脑部肿瘤分割及其三维重建

doi:10.3969/j.issn.0255-8297.2018.05.008

应用科学学报 ›› 2018, Vol. 36 ›› Issue (5): 808-818.doi: 10.3969/j.issn.0255-8297.2018.05.008

MRI的脑部肿瘤分割及其三维重建

侯东奥¹, 鲁宇明¹, 汪宇玲², 刘武³

1. 南昌航空大学航空制造工程学院, 南昌 330063;
2. 东华理工大学信息工程学院, 南昌 330013;
3. 耶鲁大学医学院, 美国康涅狄格州 06511

收稿日期:2017-07-30 修回日期:2017-09-08 出版日期:2018-09-30 发布日期:2018-09-30
通信作者: 鲁宇明,教授,研究方向:进化算法、模式识别,E-mail:luyuming_tougao@163.com E-mail:luyuming_tougao@163.com
基金资助:
国家自然科学基金（No.61866025）；江西省教育厅科技项目（No.GJJ170572）资助

MRI-Based Segmentation and Three-Dimensional Reconstruction of Brain Tumor

HOU Dong-ao¹, LU Yu-ming¹, WANG Yu-ling², LIU Wu³

1. College of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang 330063, China;
2. School of Information Engineering, East China University of Technology, Nanchang 330013, China;
3. School of Medicine, Yale University, Connecticut 06511, United States

Received:2017-07-30 Revised:2017-09-08 Online:2018-09-30 Published:2018-09-30

摘要/Abstract

摘要： 脑部肿瘤精确分割和三维重建的研究对脑部肿瘤疾病的诊断具有重要意义.提出一种全自动的脑肿瘤分割方法，该方法首先利用人脑结构信息的对称性，通过区域生长法实现脑肿瘤的粗略分割，然后将粗分割区域作为初始水平集轮廓，利用改进的测地线活动轮廓（geodesic active contours，GAC）模型进一步精确分割.经实验分析可知，该模型具有良好的分割灰度不均匀的弱边缘能力.最后对分割出的脑部肿瘤序列图像进行面绘制重建及其可视化，以此为脑肿瘤研究提供更多维度的信息.

关键词: 区域生长法, 面绘制, 脑肿瘤, 测地线活动轮廓模型

Abstract: Analysis on the accurate segmentation and 3D reconstruction of brain tumors are important for the diagnosis of brain tumor diseases. This paper presents an automatic segmentation method for brain tumors. According to the symmetry of human brain structure information, a rough segmentation of brain tumor can firstly be realized by region growing method, and then taken as the initial level set contour for the further accurate segmentation by means of geodesic active contour (GAC) model. Experimental analysis shows that the model has good weak-edge ability on segmenting uneven gray scale. Finally, surface rendering, reconstruction and visualization of the segmented brain tumor sequence images are carried out to provide more dimensional information for brain tumor research.

Key words: geodesic active contour (GAC) model, surface rendering, brain tumor, region growing method

中图分类号:

TP391

侯东奥, 鲁宇明, 汪宇玲, 刘武. MRI的脑部肿瘤分割及其三维重建[J]. 应用科学学报, 2018, 36(5): 808-818.

HOU Dong-ao, LU Yu-ming, WANG Yu-ling, LIU Wu. MRI-Based Segmentation and Three-Dimensional Reconstruction of Brain Tumor[J]. Journal of Applied Sciences, 2018, 36(5): 808-818.

参考文献

[1] Joseph R P, Singh C S, Manikandan M. Brain tumor MRI image segmentation and detection in image processing[J]. Internation Journal of Research in Engineering and Technology, 2014, 3(1):1-5.
[2] 戴修斌,邓黄健,刘代富,刘可,周青蓉. 利用群体CT计划图像的多任务前列腺自动分割[J]. 应用科学学报, 2017, 35(1):99-106. Dai X B, Deng H J, Liu D F, Liu K, Zhou Q R. Using the group image automatic segmentation of multitasking prostate CT plan[J]. Journal of Applied Sciences, 2017, 35(1):99-106. (in Chinese)
[3] Gupta M, Gayatri K S, Harika K. Brain tumor segmentation by integrating symmetric property with region growing approach[C]//IEEE India Conference. IEEE, 2015:1-5.
[4] Ben R A, Benzarti F, Amiri H. Segmentation of brain MRI using active contour model[J]. International Journal of Imaging Systems & Technology, 2017, 27(1):3-11.
[5] Pydi V, Josyula S B. Detection of brain tumor using PCA with SVM[J]. International Journal of Research in Computer and Communication Technology, 2015, 4(8):535-539.
[6] Bendib M M, Merouani H F, Diaba F. Automatic segmentation of brain MRI through stationary wavelet transform and random forests[J]. Pattern Analysis & Applications, 2015, 18(4):829-843.
[7] Pereira S, Pinto A, Alves V, Silva C A. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Transactions on Medical Imaging, 2016, 35(5):1240-1251.
[8] Sustersic T, Peulic M, Filipovic N, Peulic A. Application of active contours method in assessment of optimal approach trajectory to brain tumor[C]//International Conference on Bioinformatics and Bioengineering. IEEE, 2015:1-4.
[9] Dodin P, Martel-Pelletier J, Pelletier J P, Abram F. A fully automated human knee 3D MRI bone segmentation using the ray casting technique[J]. Medical & Biological Engineering & Computing, 2011, 49(12):1413-1424.
[10] Neves A M A, Ferreira A J M, Carrera E, Roque C M C, Cinefra M. A quasi-3D sinusoidal shear deformation theory for the static and free vibration analysis of functionally graded plates[J]. Composites Part B Engineering, 2012, 43(2):711-725.
[11] 刘思奇,张来峰,范立成,盛小明. 基于医学图像序列面绘制的骨组织快速成型方法[J]. 计算机应用, 2017, 37(5):1456-1459. Liu S Q, Zhang L F, Fan L C, Sheng X M. Based on medical image sequence of bone tissue rapid prototyping method[J]. Journal of Computer Applications, 2017, 37(5):1456-1459. (in Chinese)
[12] Gai L I. The comparison research of MC algorithm and MT algorithm[J]. Modern Computer, 2007, 251:19-23.
[13] 李丹仪,李卫锋,廖庆敏. 基于对称信息和主动轮廓模型的脑肿瘤分割系统[J]. 清华大学学报(自然科学版), 2013(7):995-1000. Li D Y, Li W F, Liao Q M. Based on the asymmetric information and active contour model of brain tumor segmentation system[J]. Journal of Tsinghua University (Science and Technology), 2013(7):995-1000. (in Chinese)
[14] Zabir I, Paul S, Rayhan M A, Sarker T, Fattah A S, Shahnaz C. Automatic brain tumor detection and segmentation from multi-modal MRI images based on region growing and level set evolution[C]//IEEE International Wie Conference on Electrical and Computer Engineering. IEEE, 2016:503-506.
[15] Caselles V, Kimmel R, Sapiro G. Geodesic active contours[J]. International Journal of Computer Vision, 1997, 22(1):61-79.

MRI的脑部肿瘤分割及其三维重建

MRI-Based Segmentation and Three-Dimensional Reconstruction of Brain Tumor

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	姚汝婧, 杨磊, 杨涛, 胡应鑫, 田强, 吴偶. 基于改进Ising模型的心理量表大数据分析[J]. 应用科学学报, 2020, 38(3): 339-352.
[2]	蒋沁吟, 张熙. 基于特定主题的社交网络影响力评估方法[J]. 应用科学学报, 2020, 38(3): 353-366.
[3]	张彤彤, 董军宇, 赵浩然, 李琼, 孙鑫. 基于知识蒸馏的轻量型浮游植物检测网络[J]. 应用科学学报, 2020, 38(3): 367-376.
[4]	魏子洋, 赵志宏, 赵敬娇. 改进Faster R-CNN算法及其在车辆检测中的应用[J]. 应用科学学报, 2020, 38(3): 377-387.
[5]	孙容海, 施林甫, 俞春强, 劳欢, 唐振军. 面向密文图像信息隐藏的随机插值方法[J]. 应用科学学报, 2020, 38(3): 419-430.
[6]	王大千, 崔荣一, 金璟璇. 基于视觉关注模型与多尺度MSER的自然场景文本检测[J]. 应用科学学报, 2020, 38(3): 496-506.
[7]	杨高强, 李晟, 张新鹏. 基于方向场的形变指纹检测[J]. 应用科学学报, 2019, 37(6): 775-782.
[8]	余淑真, 欧巧凤, 熊邦书, 陈垚锋. 基于散焦图像的大视场立体视觉标定方法[J]. 应用科学学报, 2019, 37(6): 795-805.
[9]	崔三帅, 毛毛雨, 林晓丹, 康显桂. 图像视频中ENF信号的分析及应用综述[J]. 应用科学学报, 2019, 37(5): 573-589.
[10]	刘伟, 章琬苓, 项世军. 基于LBP-MDCT和CNN的人脸活体检测算法[J]. 应用科学学报, 2019, 37(5): 609-617.
[11]	屈凌峰, 陈帆, 和红杰, 袁源. 基于位平面-块置乱的图像加密算法安全性分析[J]. 应用科学学报, 2019, 37(5): 631-642.
[12]	赵琰, 周晓炜, 沈麒. 基于对立色与显著区域的紧凑图像哈希算法[J]. 应用科学学报, 2019, 37(5): 691-703.
[13]	张永胜, 田华伟, 肖延辉, 郝昕泽, 张明旺. 基于三方加权稀疏编码模型的PRNU提取算法[J]. 应用科学学报, 2019, 37(5): 704-710.
[14]	朱翌明, 陈帆, 和红杰, 陈鸿佑. 基于秘密信息驱动的正交GAN信息隐藏模型[J]. 应用科学学报, 2019, 37(5): 721-732.
[15]	王俊祥, 毛宁雄, 赵怡, 王春桃. 面向全容量的高性能彩色图像可逆信息隐藏算法[J]. 应用科学学报, 2019, 37(5): 744-760.