Automated Categorization of Multi-Class Brain Abnormalities Using Decomposition Techniques With MRI Images: A Comparative Study

Medical imaging and analysis are useful to visualize anatomic structure. However, analysis of the pathologic substrate is difficult and inefficient when using simple imaging tools. The manual detection and classification of brain abnormality is particularly tedious. Moreover, the currently used meth...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.28498-28509
Main Authors: Gudigar, Anjan, Raghavendra, U., Ciaccio, Edward J., Arunkumar, N., Abdulhay, Enas, Acharya, U. Rajendra
Format: Article
Language:English
Subjects:
Abnormalities
Automation
Bispectral feature
Brain
brain pathology
Classification
Comparative studies
Decomposition
Diseases
Empirical analysis
Entropy
Feature extraction
Hand tools
Imaging techniques
Magnetic resonance imaging
Medical imaging
neighborhood projection embedding
Principal component analysis
Substrates
support vector machine
Support vector machines
Transforms
variational mode decomposition
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Description
Summary:Medical imaging and analysis are useful to visualize anatomic structure. However, analysis of the pathologic substrate is difficult and inefficient when using simple imaging tools. The manual detection and classification of brain abnormality is particularly tedious. Moreover, the currently used methodology suffers from interobserver variability during image interpretation. Magnetic resonance imaging (MRI) is an efficient imaging technique for revealing complex anatomical architecture, and it is highly efficacious for precise brain imaging. Herein, we describe a novel computer aided diagnosis method for automated processing of brain MRI images. The performances of two decomposition techniques, namely, bidimensional empirical mode decomposition and variational mode decomposition (VMD), are compared. Thereafter, bispectral feature extraction and supervised neighborhood projection embedding are implemented to represent each feature in a new subspace, for the automated classification of various categories of disease. A support vector machine classifier is used to train and test the performance accuracy. The level of classification accuracy of 90.68%, 99.43% sensitivity and 87.95% specificity is obtained using the VMD technique. Hence, the developed system can be used as an adjunct tool by radiologists to confirm their screening.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2901055