International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 5, No. 8, 28 Jun. 2013
Cover page and Table of Contents: PDF (size: 480KB)
Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis
Full Text (PDF, 480KB), PP.29-36
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Author(s)
Sindhumol S
1,*
Kannan Balakrishnan
1
Anil Kumar
2
Index Terms
Fuzzy C-Means Clustering, Magnetic Resonance Imaging, Multisignal wavelet analysis, Multispectral analysis, Principal Component Analysis
Abstract
In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.
Cite This Paper
Sindhumol S,Kannan Balakrishnan,Anil Kumar,"Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis", IJIGSP, vol.5, no.8, pp.29-36, 2013. DOI: 10.5815/ijigsp.2013.08.04
Reference
[1]Vannier M W, Butterfield R L, Jordan D, Murphy W A, Levitt R G, Gado M, Multispectral analysis of magnetic resonance images, Radiology, 1985, 154(1):221-224.
[2]Kvinnsland Y, Brekke N, Taxt T M, Gruner R, Multispectral analysis of multimodal images, Acta Oncol, 2009, 48( 2): 277-84.
[3]Christopher M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer, 2006.
[4]Aapo Hyvarinen, Juha Karhunen, Erkki Oja, Independent component analysis, John Wiley & Sons Inc, New York, 2001.
[5]Daniel D. Lee, H. Seung, Learning the parts of objects by non-negative matrix factorization, Nature, 1999, 401:788-791.
[6]Stephan Liwicki, Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja Pantic, Euler Principal Component Analysis, Int J Comput Vis, 2013, 101(3):498-518.
[7]Tomoko Tateyama, Zensho Nakao, Yen-Wei Chen, Classification of Brain Matters in MRI by Kernel Independent Component Analysis, Proc of IIH-MSP, IEEE Computer Society, 2008:713-716.
[8]Henry Han, Xiao-Li Li, Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery, BMC Bioinformatics, 2011, 12(1:S7).
[9]Abdullah N, Lee Wee Chuen, Ngah U K, Ahmad K A, Improvement of MRI brain classification using principal component analysis, Proc IEEE Int Conf on Control System, Computing and Engineering (ICCSCE), 2011:557-561.
[10]Eyal E, Bloch B N, Rofsky N M, Furman-Haran E, Genega E M, Lenkinski R E, Degani H., Principal Component Analysis for Dynamic Contrast-Enhanced MRI in Human Prostate Cancer, Invest Radiol, 2010, 45(4):174–181.
[11]Mark-John Bruwer, John F. MacGregor, Michael D. Noseworthy, Dynamic Contrast-Enhanced MRI Diagnostics in Oncology via PCA, Proc 10th Scandinavian Symposium on Chemometrics, SSC10, J. Chemometrics, 2008, (22):708-716.
[12]Tiranee Achalakul, Stephen Taylor, A Distributed Spectral-screening PCT Algorithm, J Parallel Distrib Comput, 2003, 63(3):373-384.
[13]Sindhumol S., M. Wilscy, Hyper-Spectral Image Analysis-A Robust Algorithm using Support Vectors and Principal Components, Proc IEEE Int Conf on Computing: Theory and Applications, 2007:389-395,.
[14]Sinthop Kaewpijit, Jacqueline Le Moigne, Tarek El-Ghazawi, Feature reduction of hyperspectral imagery using hybrid wavelet principal component analysis, Opt Eng, 2004, 43(2):350-362.
[15]Bruce L M, Koger C H, Jiang Li, Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction, IEEE Trans on Geoscience and Remote Sensing, 2002, 40(10):2331–2338.
[16]He R, Datta S, Sajja B R, Narayana P A, Generalized Fuzzy Clustering for Segmentation of Multi-Spectral Magnetic Resonance Images, Comput Med Imaging Graph, 2008, 32(5):353-366.
[17]James C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
[18]Brainweb:Simulated Brain Database, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University. http://www.bic.mni.mcgill.ca/brainweb.
[19]S. Mallat, A Wavelet Tour of Signal Processing, 3rd ed., The Sparse Way Academic Press, 2008.
[20]Sindhumol S, Anil Kumar, Kannan Balakrishnan, Abnormality detection from multispectral brain MRI using multiresolution independent component analysis, International Journal of Signal processing, Image processing, and Pattern recognition, 2013, 6(1):177-190.
[21]Sumitra N, Rakesh Kumar Saxena, Brain Tumor Classification Using Back Propagation Neural Network, International Journal of Image, Graphics and Signal Processing, 2013, 5(2):45-50.
[22]Yang X, Fei B, A multiscale and multiblock fuzzy C-means classification method for brain MR images, Medical Physics, 2011, 38(6):2879–2891.
[23]Boudraa A O, Dehak S M, Zhu Y M, Pachai C, Bao Y G, Grimaud J, Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering, Comput Biol Med, 2000, 30(1):23–40.
[24]Jiawei Han, Micheline Kamber, Data Mining: Concepts and Techniques, 2nd Edn., Morgan Kaufmann Publisher, San Fransisco, USA, 2006.