Affiliations: Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India
Correspondence:
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Corresponding author. Tanvi Gupta, Department of Electrical Engineering, Indian Institute of Technology Delhi,
Delhi 110016, India. Tel.: +91 9899666057; Fax: +91 11 2659 6102; E-mail: [email protected].
Abstract: Magnetic Resonance Imaging (MRI) is a diagnostic tool of remarkable potential in the area of neuroscience and clinical neuroimaging. The diagnostic accuracy can be limited by incompetence of the operating personnel, which can be supplemented by machine learning algorithms for classification of physiology and pathology. This paper uses effective information feature extraction, principal component analysis (PCA) for feature reduction and support vector machine (SVM) for classification of multi-sequence MR images of 7 patients. All axial slices of the brain are classified into normal and abnormal images. Various methods for feature extraction were tested among which effective information yielded the highest accuracy of 80.8% in a set of 677 images used for training and testing. The sensitivity and specificity were 80% and 81.06%, respectively. Different grid sizes were tested, and the highest accuracy was reported for 2 × 2 which indicates that the feature extraction must be taken over a small grid to ensure detection of minor variation from normal. The image sequences tested considered in the study are T1 weighted, T2 weighted, Fluid-attenuated inversion recovery (FLAIR), and post contrast T1 weighted. T2 weighted images were best classified with the maximum accuracy of 95.97%. This method proved to be effective to classify the images of all four sequences with accuracy ranging from 92–96%. The method was also tested with out of sample data and the accuracy obtained was 72.4%. The novelty of this work lies in the classification of multi-sequential images using all the different slices of the patient which includes the top of the skull as well as the mandible. The slices differ significantly as the spread of the tumor varies with each slice. The slices are taken at 5mm gap and the tumor can have a thickness less or more than the slice gap considered for the scan.
