Bi-modality Image Classification Based on Independent Component Analysis_Journal of Biomedical Engineering

Authors：

GUANJie ,  YANGWenlu

College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China;

Corresponding author：

YANGWenlu, Email: wlyang@shmtu.edu.cn

Keywords：

Alzheimer's disease; mild cognitive impairment; independent component analysis; support vector machine; bi-modality images

DOI：

10.7507/1001-5515.20140193

Video：

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We in the present research proposed a classification method that applied infomax independent component analysis (ICA) to respectively extract single modality features of structural magnetic resonance imaging (sMRI) and positron emission tomography (PET). And then we combined these two features by using a method of weight combination. We found that the present method was able to improve the accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Compared AD to healthy controls (HC): the study achieved a classification accuracy of 93.75%, with a sensitivity of 100% and a specificity of 87.64%. Compared MCI to HC: classification accuracy was 89.35%, with a sensitivity of 81.85% and a specificity of 99.36%. The experimental results showed that the bi-modality method performed better than the individual modality in comparison to classification accuracy.

Citation： GUANJie, YANGWenlu. Bi-modality Image Classification Based on Independent Component Analysis. Journal of Biomedical Engineering, 2014, 31(5): 1031-1036. doi: 10.7507/1001-5515.20140193 Copy

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17.	XU L, GROTH K M, PEARLSON G, et al. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia[J]. Hum Brain Mapp, 2009, 30(3): 711-724.
18.	RAMKUMAR P, PARKKONEN L, HARI R, et al. Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis[J]. Hum Brain Mapp, 2012, 33(7): 1648-1662.
19.	MIZIOLEK W, SAWICKI D. Face recognition: PCA or ICA[J]. Przeglad Elektrotechniczny, 2012, 88(07a): 286-288.
20.	SHAO Y E, LU C J, WANG Y C. A hybrid ICA-SVM approach for determining the quality variables at fault in a multivariate process[J]. Math Prob Eng, 2012, 2012: DOI: 10.1155/2012/284910.
21.	白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008:13-40.
22.	SENGUR A. Support vector machine ensembles for intelligent diagnosis of valvular heart disease[J]. J Med Syst, 2012, 36(4): 2649-2655.
23.	ZHANG Y L. Support vector machine classification algorithm and its application[M]. Information computing and applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Berlin Heidelberg: Springer Berlin Heidelberg, 2012, 308: 179-186.
24.	ZHANG D Q, WANG Y P, ZHOU L P, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment[J]. NeuroImage, 2011, 55(3): 856-867.
25.	HINRICHS C, SINGH V, XU G, et al. MKL for robust multi-modality AD classification[J]. Med Image Comput Comput Assist Interv, 2009, 12(Pt 2): 786-794.

1. 王永炎,田金洲.阿尔茨海默病的诊断与治疗[M].北京:人民卫生出版社,2009:3-20.
2. EWERS M, WALSH C, TROJANOWSKI J Q, et al. Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance[J]. Neurobiol Aging, 2012, 33(7): 1203-1214.
3. 薛小燕,郭小华,李敏,等.阿尔茨海默病发病机理的研究进展[J].神经药理学报,2011,1(6):37-47.
4. WEINER M W, VEITCH D P, AISEN P S, et al. The Alzheimer's disease neuroimaging initiative: a review of papers published since its inception[J]. Alzheimers Dement, 2012, 8(1 Suppl): S1-S68.
5. 雷露雯,王勇.阿尔茨海默病发病机制与炎症研究进展[J].医学综述,2008,14(20):3041-3043.
6. 赵小杰,龙志颖,郭小娟,等.阿尔茨海默氏症研究中的磁共振成像数据分析[J].软件学报,2009,20(5):1123-1138.
7. 魏宪玲,唐桂波.磁共振对阿尔茨海默病的研究[J].医学信息:下旬刊,2013,26(4):698-699.
8. ILLÁN I A, GÓRRIZ J M, RAMÍREZ J, et al. 18F-FDG pet imaging analysis for computer aided Alzheimer's diagnosis[J]. Information Sciences, 2011, 181(4): 903-916.
9. 石庆丽,韩利坤,袁春,等.阿尔茨海默病的影像学研究进展[J].中国康复理论与实践,2013,19(4):354-359.
10. FRISTON K, ASHBURNER J, KIEBEL S, et al. Statistical parametric mapping: The analysis of functional brain images[M]. London: Academic Press, 2006:1-30.
11. CHEN H B, LI B L, CHEN Z C. Automatic extracting event-related potentials within several trials using Infomax ICA algorithm[J]. J Sci Ind Res, 2012, 71(7): 468-473.
12. LANGLOIS D, CHARTIER S, GOSSELIN D. An introduction to Independent Component Analysis: InfoMax and FastICA algorithms[J]. Tutor Quant Methods Psychol, 2012, 6(1): 31-38.
13. ARBIZU J, PRIETO E, MARTÍNEZ-LAGE P, et al; Alzheimer's Disease Neuroimaging Initiative. Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer's disease dementia[J]. Eur J Nucl Med Mol Imaging, 2013, 40(9):1394-1405.
14. LANCASTER J L, TORDESILLAS-GUTIÉRREZ D, MARTINEZ M, et al. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template[J]. Hum Brain Mapp, 2007, 28(11):1194-1205.
15. 杨彭举,封洲燕,孙静.独立分量分析在神经元锋电位分类中的一种新应用[J].中国生物医学工程学报,2011,30(4):500-508.
16. 陈华富,尧德中,周可,等.一种基于独立成分分析的功能磁共振数据处理方法[J].生物医学工程学杂志,2002,19(1):64-66.
17. XU L, GROTH K M, PEARLSON G, et al. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia[J]. Hum Brain Mapp, 2009, 30(3): 711-724.
18. RAMKUMAR P, PARKKONEN L, HARI R, et al. Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis[J]. Hum Brain Mapp, 2012, 33(7): 1648-1662.
19. MIZIOLEK W, SAWICKI D. Face recognition: PCA or ICA[J]. Przeglad Elektrotechniczny, 2012, 88(07a): 286-288.
20. SHAO Y E, LU C J, WANG Y C. A hybrid ICA-SVM approach for determining the quality variables at fault in a multivariate process[J]. Math Prob Eng, 2012, 2012: DOI: 10.1155/2012/284910.
21. 白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008:13-40.
22. SENGUR A. Support vector machine ensembles for intelligent diagnosis of valvular heart disease[J]. J Med Syst, 2012, 36(4): 2649-2655.
23. ZHANG Y L. Support vector machine classification algorithm and its application[M]. Information computing and applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Berlin Heidelberg: Springer Berlin Heidelberg, 2012, 308: 179-186.
24. ZHANG D Q, WANG Y P, ZHOU L P, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment[J]. NeuroImage, 2011, 55(3): 856-867.
25. HINRICHS C, SINGH V, XU G, et al. MKL for robust multi-modality AD classification[J]. Med Image Comput Comput Assist Interv, 2009, 12(Pt 2): 786-794.

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