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

Abstract

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

CLC Number:

TP393

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.

References

[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.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

MR image classification based on LBP and extreme learning machine

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 6

Metrics

Comments

Recommended 0

[1]	LI Lu, FAN Wentao, DU Jixiang. Brain MR image segmentation based on student's t mixture model with Markov random field [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 49-55.
[2]	ZHAI Junhai, ZHANG Sufang, HU Wenxiang, WANG Xizhao. Radial basis function extreme learning machine based on core sets [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(2): 1-5.
[3]	WANG Xiaochu, WANG Shitong, BAO Fang. Image classification algorithm based on minimax probability machine with regularized probability density concensus [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(5): 13-21.
[4]	AN Chun-lin1, LU Hui-juan1,2*, ZHENG En-hui3, WANG Ming-yi1, LU Yi4. Gene expression data classification of the extreme learning machine with misclassification cost and rejection cost [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(4): 18-25.
[5]	YIN Jian-chuan1,2, ZOU Zao-jian1,3, XU Feng1. An improved extreme learning machine based on Akaike criterion [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(6): 7-11.
[6]	LIU Cheng-Yun, CHEN Zhen-Hua, CHANG Fa-Liang. Self-adaptive threshold method of MR image denoisingbased on stationary wavelet transformation [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 58-61.