APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO CLASSIFY NORMAL BRAIN TISSUE AND BRAIN LESIONS LIKE LOW AND HIGH GRADE GLIOMA, METASTASES AND MULTIPLE SCLEROSIS

  • ANINDYA GANGULY College of Health and Human Sciences, Charles Darwin University, Australia
  • TAPAN KRISHNA BISWAS
  • RAJIB BANDOPADHYAY
  • AJOY KUMAR DUTTA
Keywords: Principal Component Analysis (PCA); Magnetic Resonance Imaging (MRI); Metabolites of MR Spectroscopy; Refractive Index (RI); Ground Truth Image: Independent Numeric and Dependent Variable ; Prediction.

Abstract

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.

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Author Biographies

ANINDYA GANGULY, College of Health and Human Sciences, Charles Darwin University, Australia

RESEARCHER

TAPAN KRISHNA BISWAS

Department of Instrumentation and Electronics Engineering, Jadavpur university, India.

RAJIB BANDOPADHYAY

Department of Instrumentation and Electronics Engineering, Jadavpur university, India

AJOY KUMAR DUTTA

Department of Production Engineering, Jadavpur University, India

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Published
2018-07-31