Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Sreejith, S.a; * | Subramanian, R.b | Karthik, S.c
Affiliations: [a] Department of Electronics & Communication Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India | [b] Department of Electrical & Electronics Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India | [c] Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
Correspondence: [*] Corresponding author. S. Sreejith, SNS College of Technology, Department of Electronics & Communication Engineering, Coimbatore-641035, Tamilnadu, India. E-mail: sreejithpapers@gmail.com.
Abstract: Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions.
Keywords: Segmentation of ischemic stroke lesion, preprocessing, patching asymmetric region, Hough line symmetry axis, Random forest classifier
DOI: 10.3233/JIFS-212457
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 791-800, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl