Alzheimer’s disease detection by pseudo zernike moment and linear regression classification

CNS and Neurological Disorders - Drug Targets

Volumn 16, Issue 1, 2017, Pages 11-15

  Wang, Shui Hua ^a,b   Du, Sidan ^c   Zhang, Yin ^d   Phillips, Preetha ^e   Wu, Le Nan ^f   Chen, Xian Qing ^g   Zhang, Yu Dong ^a,b  

^a NANJING NORMAL UNIVERSITY   (China)

^b Hunan Policy Academy   (China)

^c NANJING UNIVERSITY   (China)

^d ZHONGNAN UNIVERSITY OF ECONOMICS AND LAW   (China)

^e SHEPHERD UNIVERSITY   (United States)

^f SOUTHEAST UNIVERSITY   (China)

^g ZHEJIANG NORMAL UNIVERSITY   (China)

Author keywords

Alzheimer s disease; Linear regression classification; Pseudo Zernike moment

Indexed keywords

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; COMPUTER AIDED DESIGN; COMPUTER SIMULATION; FUNCTIONAL NEUROIMAGING; LINEAR REGRESSION CLASSIFICATION; MACHINE LEARNING; MAMMOGRAPHY; MEASUREMENT ACCURACY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRINCIPAL COMPONENT ANALYSIS; PRINCIPAL COORDINATE ANALYSIS; PSEUDO ZERNIKE MOMENT; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VOXEL BASED MORPHOMETRY; AGED; BRAIN; DIAGNOSTIC IMAGING; FEMALE; HUMAN; MALE; MIDDLE AGED; NEUROIMAGING; REPRODUCIBILITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; VERY ELDERLY;

AGED; AGED, 80 AND OVER; ALGORITHMS; ALZHEIMER DISEASE; BRAIN; FEMALE; HUMANS; IMAGING, THREE-DIMENSIONAL; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NEUROIMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 85011068537     PISSN: 18715273     EISSN: 19963181     Source Type: Journal    
DOI: 10.2174/1871527315666161111123024     Document Type: Article

References (38)

1. Dong Z. Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog Electromagnet Res 2014; 144: 171-84.
2. Liu J, Wang Z, Huo Y, et al. A Functional Imaging Study of Self-Regulatory Capacities in Persons Who Stutter. Plos One 2014; 9(2): Article ID: e89891.
3. Asman AJ, Huo Y, Plassard AJ, Landman BA. Multi-atlas learner fusion: An efficient segmentation approach for large-scale data. Med Image Anal 2015; 26(1): 82-91.
4. Plant C, Teipel SJ, Oswald A, et al. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. NeuroImage 2010; 50(1): 162-74.
5. Yuan TF. Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Computl Neurosci 2015; 9: Article ID: 66.
6. Savio A, Grana M. Deformation based feature selection for Computer Aided Diagnosis of Alzheimer’s Disease. Expert Sys Appl 2013; 40(5): 1619-28.
7. Zhang Y. Detection of Alzheimer’s disease by displacement field and machine learning. Peer J 2015; 3: Article ID: e1251.
8. Gray KR, Aljabar P, Heckemann RA, et al. Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. Neuroimage 2013; 65: 167-75.
9. Wang S, Zhang Y, Liu G, Phillips P, Yuan TF. Detection of Alzheimer’s Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging. J Alzheimer’s Dis 2016; 50(1): 233-48.
10. Gorji HT, Haddadnia J. A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI. Neuroscience 2015; 305: 361-71.
11. Naseem I, Togneri R, Bennamoun M. Linear Regression for Face Recognition. IEEE Trans Pattern Anal Mach Intell 2010; 32(11): 2106-12.
12. Ardekani BA, Figarsky K, Sidtis JJ. Sexual Dimorphism in the Human Corpus Callosum: An MRI Study Using the OASIS Brain Database. Cereb Cortex 2013; 23(10): 2514-20.
13. Tahmasbi A, Saki F, Shokouhi SB. Classification of benign and malignant masses based on Zernike moments. Comput Biol Med 2011; 41(8): 726-35.
14. Sajad Farokhi, Usman Ullah Sheikh, Jan Flusser, Bo Yang. Near infrared face recognition using Zernike moments and Hermite kernels. Info Sci 2015; 316: 234-45.
15. Sharma S, Khanna P. Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM. J Digit Imag 2015; 28(1): 77-90.
16. Deng A-W, Wei C-H, Gwo C-Y. Stable, fast computation of high- order Zernike moments using a recursive method. Pattern Recognition 2016; 56: 16-25.
17. Xiao JK. Image Forgery Detection Algorithm Based on Non Sampling Wavelet Transform and Zernike Moments. Int J Security Appl 2016; 10(2): 27-38.
18. Aggarwal A, Singh C. Zernike Moments-Based Gurumukhi Character Recognition. Appl Artificial Intell 2016; 30(5): 429-44.
19. Singh C, Aggarwal A. A comparative performance analysis of DCT-based and Zernike moments-based image up-sampling techniques. Optik 2016; 127(4): 2158-64.
20. Yan Y, Zhang Y, Tian S, Gao F. Phase Analysis of Three-Dimensional Zernike Moment for Building Classification and Orientation in Digital Surface Model. IEEE Geosci Remote Sens Lett 2016; 13(1): 58-62.
21. Nugroho HA, Yusufiyah HKN, Adji TB, Nugroho A. Zernike Moment Feature Extraction for Classifying Lesion’s Shape of Breast Ultrasound Images. In: 7th International Conference on Information Technology and Electrical Engineering. King Mongkut’s Institute, Chiang Mai, Thailand: IEEE: 2015. pp 458-463.
22. Deng A W, Wei C H, Gwo C Y. Algorithms for Computing Zernike Moments and Image Reconstruction in Parallel Process. In: 2nd International Conference on Information Science And Control Engineering. Shanghai, China: IEEE: 2015. Pp 105-109.
23. Dai X, Liu T, Shu H, Luo L. Pseudo-Zernike moment invariants to blur degradation and similarity transformation. Int J Comput Math 2014; 91(11): 2403-14.
24. Borzue N, Faez K. Object Contour Detecting Using Pseudo Zernike Moment and Multi-Layer Perceptron. In: 22nd Iranian Conference on Biomedical Engineering. Tehran, Iran: IEEE: 2015. pp. 304-308.
25. Gaglione D, Clemente C, Pallotta L, Proudler I, Maio AD, Soraghan JJ. Krogager Decomposition and Pseudo-Zernike Moments for Polarimetric Distributed ATR. In: Sensor Signal Processing for Defence. Edinburgh, Scotland: IEEE: 2014. pp.47-52.
26. Ahmady M, Ghasemi R, Kanan HR. Local Weighted Pseudo Zernike Moments and Fuzzy classification for facial expression Recognition. In: 13th Iranian Conference on Fuzzy Systems. Qazvin, Iran: IEEE: 2013. Pp 156-161.
27. Hu W, Liu H, Hu C, Wang S, Chen D, Mo J, Liang Q. Vision-based force measurement using pseudo-zernike moment invariants. Measurement 2013; 46(10): 4293-05.
28. Shi Z, Liu G, Du M. Rotary Face Recognition Based on Pseudo-Zernike Moment. In: E. Mao, L.L. Xu, and W. Tian, Eds, Emerging Computation and Information Technologies for Education, Berlin: Springer-Verlag Berlin: 2012. Pp 641-646.
29. Hosny KM. Accurate pseudo Zernike moment invariants for greylevel images. Imag Sci J 2012; 60(4): pp 234-42.
30. Seal A, Bhattacharjee D, Nasipuri M, Basu DK. UGC-JU face database and its benchmarking using linear regression classifier. Multimedia Tool Appl 2015; 74(9): 2913-37.
31. Ruiz-Medina MD, Anh VV, Espejo RM, Angulo JM, Frías MP. Least-Squares Estimation of Multifractional Random Fields in a Hilbert-Valued Context. J Optim Theory Appl 2015; 167(3): 888-11.
32. Carbo-Dorca R. Least squares estimation of unknown molecular properties and quantum QSPR fundamental equation. J Math Chem 2015; 53(8): 1651-56.
33. Feng Q, Zhu Q, Tang L-L, et al. Double linear regression classification for face recognition. J Mod Opt 2015; 62(4): 288-95.
34. Chou YT, Yang JF. Identity recognition based on generalised linear regression classification for multi-component images. IET Comput Vis 2016; 10(1): 18-27.
35. Wang S, Pan H, Zhang C, et al. RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs. J Visual Commun Image Represent 2014; 25(2): 263-72.
36. Zhou X-X, Yang J-F, Sheng H, et al. Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. Simulation 2016; 92(9): 827-37.
37. Wei L, Yang J. Fitness-scaling adaptive genetic algorithm with local search for solving the Multiple Depot Vehicle Routing Problem. Simulation 2016; 92(7): 601-16.
38. Du S. Multi-objective path finding in stochastic networks using a biogeography-based optimization method. Simulation 2016; 92(7): 637-47.