A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer's disease

摘要

Highlights•Image Fusion based approach for generation of band images for Independent Component Analysis.•Novel combination of different algorithms for automatic tissue classification of Brain MRI into CSF, WM and GM.•Statistical quality analysis in various implementation stages of CAD system for atrophy detection in Alzheimer’s disease.•100% accuracy for automatic tissue classification with implemented methodology for test samples under consideration.•Validation of brain MRI tissue segmentation and classification results by the end user- neurologist/ radiologist.AbstractBrain Magnetic Resonance Image (MRI) plays a vital role in diagnosis of diseases like Brain Tumor, Alzheimer, Multiple Sclerosis, Schizophrenia and other White Matter Lesions. In most of the cases accurate segmentation of Brain MRI into tissue types like Cerebro-Spinal Fluid (CSF), White Matter (WM) and Grey Matter (GM) is of interest. The diagnostic accuracy of expert and non-expert Radiologists can be improved with accurate and automated tissue segmentation and classification system. Such system can also be used for trainees to understand the individual tissue distribution in MRI scans. In this paper, we propose a novel automated tissue segmentation and classification method based on Independent Component Analysis (ICA) with Band Expansion Process (BEP) and Support Vector Machine (SVM) classifier which with input as T1, T2 and Proton Density (PD) scans of patient, provides output as CSF, WM and GM indicating the possible atrophy in brain which can help in diagnosis of Alzheimer’s disease (AD). The objective of this work is to test the effectiveness of ICA with different input images generated using BEP for accurate brain tissue segmentation by validating results with different quality metrics. The novel method for generating input images for ICA has been implemented and segmented tissues are used for atrophy detection. The BEP+ICA+Thresholding+‘SVM trained with Grey Level Co-occurrence Matrix (GLCM) based texture features’ is giving 100% tissue classification accuracy for test samples under consideration.