slugMR Brain Image Segmentation Based on Principle Component Analysis and Self-Organizing Map
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 3
Abstract
In this paper, a fully unsupervised segmentation of Magnetic Resonance (MR) brain image is presented, which is based on a competitive learning algorithm- Self Organizing Map (SOM). We tried to address the problem of segmentation of MR brain images using Principle Co mponent Analysis (PCA) and unsupervised classifier. The proposed technique contains number of steps such as preprocessing using Brain Extraction Tool (BET), feature extraction (first and second order features), feature selection using PCA and segmentation using SOM clustering. Our proposed method is performed over real MR data provided by Internet Brain Repository (IBSR) database. Performance evaluation using Tanimoto performance index shows that the proposed method has good segmentation results. Tanimoto p erformance index gives mean and standard deviation of 0.59 0.06 for white matter (WM) and 0.58 0.05 for gray matter (GM). Algorithm offers 86.74% and 70.2% sensitivity for WM and GM respectively and 96.49% and 96.44% specificity for WM and GM. This fully unsupervised method can be used to identify the brain disorders such as brain tumours, dementia Alzheimer’s disease and other neuro anatomical disorders.
Authors and Affiliations
Jesna M, Kumudha Raimond
Keywords
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- EP ID EP17797
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