Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

FAUST, Oliver, EN WEI KOH, Joel, JAHMUNAH, Vicnesh, SABUT, Sukant, CIACCIO, Edward J., MAJID, Arshad, ALI, Ali, LIP, Gregory Y. H. and ACHARYA, U. Rajendra (2021). Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images. International Journal of Environmental Research and Public Health, 18 (15).

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Official URL: https://www.mdpi.com/1660-4601/18/15/8059
Open Access URL: https://www.mdpi.com/1660-4601/18/15/8059/pdf (Published version)
Link to published version:: https://doi.org/10.3390/ijerph18158059


This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources.

Item Type: Article
Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1660-4601 **History: published 29-07-2021; accepted 23-07-2021
Uncontrolled Keywords: stroke type classification, Magnetic Resonance Imaging, Support Vector Machine, adaptive symmetric sampling, Higher Order Spectra
Identification Number: https://doi.org/10.3390/ijerph18158059
SWORD Depositor: Colin Knott
Depositing User: Colin Knott
Date Deposited: 02 Aug 2021 11:29
Last Modified: 02 Aug 2021 11:30
URI: https://shura.shu.ac.uk/id/eprint/28897

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