Next Article in Journal
Life Aspirations, Generativity and Compulsive Buying in University Students
Next Article in Special Issue
Dynamical Analysis of Universal Masking on the Pandemic
Previous Article in Journal
Influence of Physical Activity and Socio-Economic Status on Depression and Anxiety Symptoms in Patients after Stroke
Previous Article in Special Issue
Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images
Article

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

1
Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
2
School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3
School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha 751024, India
4
Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA
5
Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK
6
Sheffield Teaching Hospitals NIHR Biomedical Research Centre, Sheffield S10 2JF, UK
7
Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L69 7TX, UK
8
Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, 9000 Aalborg, Denmark
9
School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore
10
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
11
International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Tomonori Okamura
Int. J. Environ. Res. Public Health 2021, 18(15), 8059; https://doi.org/10.3390/ijerph18158059
Received: 18 May 2021 / Revised: 5 July 2021 / Accepted: 23 July 2021 / Published: 29 July 2021
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. View Full-Text
Keywords: stroke type classification; Magnetic Resonance Imaging; Support Vector Machine; adaptive symmetric sampling; Higher Order Spectra stroke type classification; Magnetic Resonance Imaging; Support Vector Machine; adaptive symmetric sampling; Higher Order Spectra
Show Figures

Figure 1

MDPI and ACS Style

Faust, O.; En Wei Koh, J.; Jahmunah, V.; Sabut, S.; Ciaccio, E.J.; Majid, A.; Ali, A.; Lip, G.Y.H.; Acharya, U.R. Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images. Int. J. Environ. Res. Public Health 2021, 18, 8059. https://doi.org/10.3390/ijerph18158059

AMA Style

Faust O, En Wei Koh J, Jahmunah V, Sabut S, Ciaccio EJ, Majid A, Ali A, Lip GYH, Acharya UR. 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. 2021; 18(15):8059. https://doi.org/10.3390/ijerph18158059

Chicago/Turabian Style

Faust, Oliver, Joel En Wei Koh, Vicnesh Jahmunah, Sukant Sabut, Edward J. Ciaccio, Arshad Majid, Ali Ali, Gregory Y.H. Lip, and U. R. Acharya 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, no. 15: 8059. https://doi.org/10.3390/ijerph18158059

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop