基于LBP和极限学习机的脑部MR图像分类

doi:10.6040/j.issn.1672-3961.1.2016.282

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 86-93.doi: 10.6040/j.issn.1672-3961.1.2016.282

基于LBP和极限学习机的脑部MR图像分类

何其佳,刘振丙^*,徐涛,蒋淑洁

桂林电子科技大学电子工程与自动化学院, 广西桂林 541004

收稿日期:2016-03-01 出版日期:2017-04-20 发布日期:2016-03-01
通讯作者: 刘振丙(1980— ),男,山东济宁人,研究员,硕导,博士,主要研究方向为模式识别,图像处理. E-mail: 3936924@qq.com E-mail:504182787@qq.com
作者简介:何其佳(1990— ),男,湖南长沙人,硕士研究生,主要研究方向为图像处理. E-mail: 504182787@qq.com
基金资助:
国家自然科学基金资助项目(61105004);广西高校图像图形智能处理重点实验室基金资助项目(LD16096X);桂林电子科技大学创新基金资助项目(GDYCSZ201428)

MR image classification based on LBP and extreme learning machine

HE Qijia, LIU Zhenbing^*, XU Tao, JIANG Shujie

School of Electrical Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China

Received:2016-03-01 Online:2017-04-20 Published:2016-03-01

摘要/Abstract

摘要： 为解决磁共振(magnetic resonance, MR)脑部图像来源不一以及病变位置和形态不固定造成MR脑部图像分类精度不高的问题,提出基于局部二值模式(local binary pattern, LBP)的纹理特征提取,并用极限学习机(extreme learning machine, ELM)对MR图像分类。计算图像感兴趣区域(region of interest, ROI)的掩码,将图像分成扇形的子区域,统计掩码坐标下各块子区域的LBP直方图,连接所有LBP直方图作为特征向量通过ELM进行分类。相比以前的方法,该方法能够计算颅脑内局部纹理特征,能分类来源不一以及多种病变的图像。对脑部MR图像分类进行试验,对所有样本分类正确率超过92%,正类样本正确率超过93%,负类样本正确率超过91%。试验结果表明,该方法能够对较为复杂的MR图像进行正确分类。

关键词: MR图像, 图像分块, 局部二值模式, 极限学习机, 图像分类

Abstract: To solve the problem that theMR brain images are collect from different sources and the pathological fields are varied, a method combining the texture feature extractor which was based on the local binary patterns(LBP)with the extreme learning machine(ELM)classifier was proposed. Mask for region of interest(ROI)was calculated, the image was divided into some sector subareas, LBP histograms were calculatedin every subarea, all the LBP histograms were connected as feature vector and then classified through ELM.Compared with previous methods, the new method could calculate local features, and it was feasible to classify the different sources of MR images and variously lesion images. Some experiments for MR image classification were done, and the accuracy was more than 92% for all samples, the accuracy was more than 93% for positive sample, the accuracy was more than 91% for negative sample. The results showed that the method was available for the varied MR images.

Key words: MR image, image classification, local binary patterns, image block, extreme learning machine

中图分类号:

TP393

何其佳,刘振丙,徐涛,蒋淑洁. 基于LBP和极限学习机的脑部MR图像分类[J]. 山东大学学报(工学版), 2017, 47(2): 86-93.

HE Qijia, LIU Zhenbing, XU Tao, JIANG Shujie. MR image classification based on LBP and extreme learning machine[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(2): 86-93.

参考文献

[1] 刘岳, 王小鹏, 于挥,等. 基于形态学多尺度修正的模糊C均值脑肿瘤分割方法[J]. 计算机应用, 2014, 34(9): 2711-2715. LIU Yue, WANG Xiaopeng, YU Hui, et al. Brain tumor segmentation based on morphological multi-scale modification and fuzzy C-means clustering[J]. Journal of Computer Applications, 2014, 34(9): 2711-2715.
[2] 沈晔, 李敏丹, 夏顺仁. 基于内容的医学图像检索技术[J]. 计算机辅助设计与图形学学报, 2010, 22(4):569-578. SHEN Ye, LI Mindan, XIA Shunren. A survey on content-based medical image retrieval[J]. Journal of Computer-Aided Design & Computer Graphics, 2010, 22(4):569-578.
[3] MAGNIN B, MESROB L. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI[J]. Neuroradiology, 2009, 51(2):73-83.
[4] LI X, XIA H, ZHOU Z, et al. 3D texture analysis of hippocampus based on MR images in patients with Alzheimer disease and mild cognitive impairment[C] //Biomedical Engineering and Informatics, 2010 3rd International Conference on IEEE. Yantai, China:IEEE, 2010:1-4.
[5] CHAUDHARI A, KULKARNI J V. Local entropy based brain MR image segmentation[C] //2013 IEEE Third International Advance Computing Conference(IACC). Ghaziabad, India:IEEE, 2013:1229-1233.
[6] ZULPE N, PAWAR VP. GLCM textural features for brain tumor classification[J]. International Journal of Computer Science Issues, 2012, 9(3): 354-359.
[7] 夏宇. 基于不对称脑图像特征的阿尔兹海默病自动识别方法研究[D]. 重庆:重庆大学, 2013. XIA Yu. The Alzheimers disease automatic recognition method based on the asymmetry brain MR image features[D]. Chongqing: Chongqing University, 2013.
[8] 李昕, 童隆正, 周晓霞,等. 基于MR图像三维纹理特征的阿尔茨海默病和轻度认知障碍的分类[J]. 中国医学影像技术, 2011, 27(5):1047-1051. LI Xin, TONG Longzheng, ZHOU Xiaoxia, et al. Classification of 3D texture features based on MR image in discrimination of Alzheimer's disease and mild cognitive impairment from normal controls[J]. Chinese Journal of Medical Imaging Technology, 2011, 27(5):1047-1051.
[9] CHAPLOT S, PATNAIK L M,JAGANNATHAN N R. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network[J]. Biomedical Signal Processing and Control, 2006, 1(1): 86-92.
[10] ZHANG Y D, DONG Z C, WU L N, et al. A hybrid method for MRI brain image classification[J]. Expert Systems with Applications, 2011, 38(8): 10049-10053.
[11] MANGAT S, JOSEPH P, MATHEW A T. Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network[J]. Pattern Recognition Letters, 2013, 34(16): 2151-2156.
[12] ZHU X, SUK H I, WANG L, et al. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis[J]. Human Immunology, 2014, 75(6):570-577.
[13] ZHANG D, WANG Y, ZHOU L, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment[J]. Neuroimage, 2011, 55(3):856-867.
[14] LIU F, WEE C Y, CHEN H, et al. Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimers disease and mild cognitive impairment identification[J]. Neuroimage, 2014, 84:466-475.
[15] OJALA T, PIETIKAINEN M, HARWOOD D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions[C] //IEEE 12th IAPR International Conference on Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing. Jerusalem, Israel:IEEE, 1994: 582-585.
[16] HAFIANE A, SEETHARAMAN G, ZAVIDOVIQUE B. Median binary pattern for textures classification[J]. Lecture Notes in Computer Science, 2007:387-398.
[17] PETPON A, SRISUK S. Face recognition with local line binary pattern[C] //International Conference on Image and Graphics, ICIG 2009. Xi'an, China: IEEE, 2009:533-539.
[18] LORIS N, ALESSANDRA L, SHERYL B. Local binary patterns variants as texture descriptors for medical image analysis[J]. Artificial Intelligence in Medicine, 2010, 49(2):117-125.
[19] GUO Z, ZHANG L, ZHANG D, et al. Rotation invariant texture classification using adaptive LBP with directional statistical features[C] //IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010: 285-288.
[20] ZHANG W, SHAN S, GAO W, et al. Local gabor binary pattern histogram sequence(LGBPHS): a novel non-statistical model for face representation and recognition[C] //IEEE TenthInternational Conference on Computer Vision. Beijing,China:IEEE Computer Society, 2005:786-791.
[21] 吴义根, 李可. SPM软件包数据处理原理简介——第一部分:基本数学原理[J]. 中国医学影像技术, 2004, 20(11):1768-1772. WU Yigen, LI Ke. Basic principle of SPM: an introduction—part Ⅰ: review in basic mathematic principle[J]. Chinese Journal of Medical Imaging Technology, 2004, 20(11):1768-1772.
[22] MADABHUSHI A, UDUPA J K. New methods of MR image intensity standardization via generalized scale[J]. Medical Physics, 2006, 33(9):3426-3434.
[23] NYUL L G, UDUPA J K, ZHANG X. New variants of a method of MRI scale standardization[J]. IEEE Transactions on Medical Imaging, 2000, 19(2):143-150.
[24] OJALA T, PIETIKÄINEN M, MÄENPÄÄ T. Multiresolution gray-scale and rotation invariant texture classification with localbinary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[25] HUANG G B, ZHU Q Y, SIEWC K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1-3):489-501.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于LBP和极限学习机的脑部MR图像分类

MR image classification based on LBP and extreme learning machine

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

多维度评价

本文评价

推荐阅读 0

[1]	李璐,范文涛,杜吉祥. 基于Markov随机场的Student's t混合模型的脑MR图像分割[J]. 山东大学学报(工学版), 2017, 47(3): 49-55.
[2]	王斌,常发亮,刘春生. 基于多特征融合的交通标志分类[J]. 山东大学学报(工学版), 2016, 46(4): 34-40.
[3]	翟俊海,张素芳,胡文祥,王熙照. 核心集径向基函数极限学习机[J]. 山东大学学报(工学版), 2016, 46(2): 1-5.
[4]	王晓初, 王士同, 包芳. 基于概率密度分布一致约束的最小最大概率机图像分类算法[J]. 山东大学学报(工学版), 2015, 45(5): 13-21.
[5]	李春雷, 张兆翔, 刘洲峰, 廖亮, 赵全军. 基于纹理差异视觉显著性的织物疵点检测算法[J]. 山东大学学报(工学版), 2014, 44(4): 1-8.
[6]	安春霖1,陆慧娟1,2*,郑恩辉3,王明怡1,陆羿4. 嵌入误分类代价和拒识代价的极限学习机基因表达数据分类[J]. 山东大学学报(工学版), 2013, 43(4): 18-25.
[7]	曹红根1,袁宝华1,朱辉生2. 结合对比度信息与LBP的分块人脸识别[J]. 山东大学学报(工学版), 2012, 42(4): 29-34.
[8]	尹建川1,2, 邹早建1,3, 徐锋1. 一种基于Akaike信息准则的极限学习机[J]. 山东大学学报(工学版), 2011, 41(6): 7-11.
[9]	刘成云陈振学常发亮. 基于平稳小波的自适应阈值MR图像去噪法[J]. 山东大学学报(工学版), 2009, 39(5): 58-61.