APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO CLASSIFY NORMAL BRAIN TISSUE AND BRAIN LESIONS LIKE LOW AND HIGH GRADE GLIOMA, METASTASES AND MULTIPLE SCLEROSIS
- Principal Component Analysis (PCA); Magnetic Resonance Imaging (MRI); Metabolites of MR Spectroscopy; Refractive Index (RI); Ground Truth Image: Independent Numeric and Dependent Variable ; Prediction.
Copyright (c) 2018 IJRDO - Journal of Electrical And Electronics Engineering (ISSN: 2456-6055)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Principal Component Analysis ( PCA) an extremely useful method of Statistical techniques is applied when working with a lot of parameters or independent numerical variables to predict the different pathological lesions in the brain like Multiple Sclerosis (MS), Glioma , Glioblastoma of different grades and Metastasis. Statistical techniques such as factor analysis or Principal Component Analysis(PCA) help to overcome such difficulties.
In different brain diseases structural alterations in the normal tissue may be noticed in MR images. It is not so simple to detect the brain lesions correctly even from the MR spectroscopic graph. Enormous data collected from various patients such as – Refractive Index, T2 relaxation values, Apparent Diffusion Coefficient (ADC), Creatine (CR), Choline (CHO), NAA (N-Acetyl Aspartate), ratio of CR/NAA, LIP/LAC (Lipid/lactate), MI ( Myoinositol), CHO/CR and T2 value in the periphery of lesion may be confusing. The relationship between each variable may not be clear and that there is a chance of over fitting the data. By reducing the dimension of the feature space by “feature elimination” and “feature extraction”, there may be less chance of over fitting the data. PCA helps identifying the disease condition in doubtful cases by generating a map depicting and classifying the diseases.
 Hagen T, Nieder C, Moringlane JR. Feiden W,Konig J, Correlation of preoperative neuroradiologic with postoperative histological diagnosis in pathological intracranial process. Der Radiologe, Nov 1995;
 Horská Alena and Barker Peter B., Imaging of Brain Tumors: MR Spectroscopy and Metabolic Imaging, Neuroimaging Clin N Am. 2010 ; 20(3): 293–310.
 Jansen JF, Backes WH, Nicolay K, Kooi ME. 1H MR spectroscopy of the brain: absolute quantification of metabolites.Radiology2006; 240 (2): 318–32.
 H. Mazien, Proposed system for the diagnosis of skin diseases using Multiwavelet Transform and Decision Tree, M.Sc. Thesis, University of Technology, Department of Computer Science, 2015.
 Jameela Ali, et al., "Red Blood Cell Recognition using Geometrical Features," International Journal of Computer Science Issues2013 vol. 10, no. 1.
 S. Chandrasiri, et al., Morphology Based Automatic Disease Analysis Through Evaluation of Red Blood Cells, in Department of Information Technology/ Sri Lanka Institute of Information Technology, Fifth International Conference on Intelligent Systems, Modelling and Simulation 2014.
 Mohammed Hussein Miry, Akel A. Alzaiez, Abbas Hussein Miry, Image Authentication Using PCA And BP Neural Network, Eng.& Tech. Journal, 2010; 28,( 22):6536-6545.
 K. Hosny, Exact and fast computation of geometric moments for gray level images, Applied Mathematics and Computation Journal, 2007 vol. 189 :. 1214 1222.
 D. N. George, Tumor Type Recognition Using Artificial Neural Networks, M.Sc. thesis ,2013, Iraqi Commission for Computers And Informatics Institute for Postgraduate Studies.
 Tarun Jhaldiyal, Pawan Kumar Mishra : Analysis and Prediction of Diabetes Mellitus Using PCA, REP and SVM, International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-8, August 2014.
 Vanishri Arun, Arunkumar B.V, Padma S.K. and Shyam V, Disease Classification and Prediction using Principal Component Analysis and Ensemble Classification Framework,2017;10(14):107-116.
 Ian T. Jolliffe, Jorge Cadima: Principal component analysis: A review and recent developments, Philosophical Transactions of the Royal Society, A Mathematical, Physical and Engineering Sciences, 2016; 374: 206514.
 Lindsay.J.Smith A tutorial on Principal Components Analysis 2002, www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
 Jackson JE. A User’s Guide to Principal Components. New York: John Wiley & Sons; 1991.
 Peres-Neto PR, Jackson DA, Somers KM. How many principal components? stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal 2005, 49:974–997.
 T K Biswas, R Bandopadhyay, A Dutta, Validating The Discriminating Efficacy Of MR T2 Relaxation Value Of Different Brain Lesions And Comparison With Other Differentiating Factors: Use Of Artificial Neural Network And Principal Component Analysis. The Internet Journal of Radiology. 2017 Volume 20 Number 1. ISPUB DOI: 10.5580/IJRA.52614
 Biswas TK, Gupta A. Retrieval of true color of the internal organ of CT images and attempt to tissue characterization by refractive index : Initial experience. Indian Journal of Radiology and Imaging 2002;12:169-178
 Biswas TK, Luu T In vivo MR Measurement of Refractive Index, Relative Water Content and T2 Relaxation time of Various Brain lesions With Clinical Application to Discriminate Brain Lesions. The Internet Journal of Radiology 2009;13 (1).
 T K Biswas, S R Choudhury, A Ganguly, R Bandopadhyay, A Dutta, Refractive Index As Surrogate Biological Marker Of Tumefactive And Other Form Of Multiple Sclerosis And Its Superiority Over Other Methods, Internet Journal of Radiology, https://print.ispub.com/api/0/ispub-article/46167.