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: Ramachandran, Lakshmanana | Mohan, Veerasamyb
Affiliations: [a] Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India | [b] Department of Electrical and Electronics Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
Correspondence: [*] Corresponding author. Lakshmanan Ramachandran, Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India. E-mail: fourstar.lr@gmail.com.
Abstract: Image segmentation is an essential part of almost any image processing methodology and it play a critical role in protecting the region of interest on any substrate image before its actual analysis is prescribed. In fact, the accuracy of any processing done by image segmentation will largely depends on the efficiency of the segmentation algorithm employed. A typical segmentation method employing a important features of Canny–GLCM (Gray Level Co-occurrence Matrix) incorporated with a simple Artificial Neural Network (ANN) model is proposed in this research work for segmentation of shrimp variability. Performance metrics related to accuracy have been compared with benchmark of this method, and the sensitivity of efficiency level has been described. The segmentation in the proposed research work is targeted towards Penaeus Monodon (PM), and Litopenaeus Vannamei (LV) diversities for main threats detection of White Spot Syndrome (WSS). The proposed model has better performance metrics, such as (94.67%), sensitivity (94.79%), specificity (94.51%) and positive predictive (94.79%) while compared to other existing methods.
Keywords: Image segmentation, white spot syndrome, gray level cooccurence matrix, neural network, detection accuracy
DOI: 10.3233/JIFS-220172
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1453-1466, 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