Epilepsy detection and classification based on the contour maps of brain MR images

A new method of epilepsy detection and classification of the degree of affliction is presented.The proposed method is based on the information generated by the filled contour maps of the brain MRI.The result is independent of the size and orientation of the target image.Distribution of multiple intensity levels in an MR image is generated by the proposed contour map segmentation technique.Disease afflicted regions are segmented using this technique.The epilepsy diagnosis is carried out by classifying MR images as healthy and diseased.The diseased ones are further classified into three sub-classes of affliction, namely, mild, medium and severe.The classification is based on the area of the top level segment relative to the total area of the non-skull region.In the proposed method, the classification error is found to be between 5% to 10%.

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Publication Details

Journal
International Journal of Medical Engineering and Informatics
Published
2024-12-13
DOI
https://doi.org/10.1504/ijmei.2025.143282
Primary Topic
Brain Tumor Detection and Classification
Type
article
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article

Epilepsy detection and classification based on the contour maps of brain MR images

H.S. VinayKumar
International Journal of Medical Engineering and Informatics
Brain Tumor Detection and Classification
article

Epilepsy detection and classification based on the contour maps of brain MR images

H.S. VinayKumar
article en

Abstract

A new method of epilepsy detection and classification of the degree of affliction is presented.The proposed method is based on the information generated by the filled contour maps of the brain MRI.The result is independent of the size and orientation of the target image.Distribution of multiple intensity levels in an MR image is generated by the proposed contour map segmentation technique.Disease afflicted regions are segmented using this technique.The epilepsy diagnosis is carried out by classifying MR images as healthy and diseased.The diseased ones are further classified into three sub-classes of affliction, namely, mild, medium and severe.The classification is based on the area of the top level segment relative to the total area of the non-skull region.In the proposed method, the classification error is found to be between 5% to 10%.

International Journal of Medical Engineering and InformaticsVol. 17(1)
Good health and well-being
Openalex Percentile: Top 74%
Brain Tumor Detection and Classification
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