Open Access Subscription Access
An Efficient Britwari Technique to Enhance Canny Edge Detection Algorithm using Deep Learning
Artificial Intelligence edge detection refers to a set of mathematical techniques used to recognize digital image locations. The picture brightness plays a vital role in detecting dissimilarities and making decisions. Edges are the sharp changes in pictures with respect to the brightness and are commonly categorized into a collection of curved line segments. The main focus of this paper is to find sharp corner edges and the false edges present in the MRI images. The canny edge algorithm is a popular method for detecting these types of edges. The traditional canny edge detection technique has various issues that are discussed in this paper. This study analyses the canny edge algorithm and enhances the smoothing filter, pixel identifier, and feature selection. The proposed Britwari technique, Tabu Search Heuristic Pattern Identifier (TSHPI) enhances the edge detection using SUSAN Filter. Feature Selection is performed to improvise the canny edge method. Deep Learning algorithm is used for classification of pre-trained neural networks to find a greater number of edge pixels. The implementation results show that the Britwari proposed technique (SUSAN Filter Tabu Search Heuristic Pattern Identifier Hill Climbing) reached better accuracy than the traditional Canny Edge Detection algorithms. The results produced better feature set selection using edge detection in MRI images.
Britwari Technique, Edge Detection, Deep Learning, Image Processing
- Saad Albawi, Tareq Abed Mohammed and Saad Al-Zawi, “Understanding of a Convolutional Neural Network”, Proceedings of International Conference on Communication and Electronics Telecommunications, pp. 1-8, 2017.
- Ruohui Wang, “Edge Detection using Convolutional Neural Network”, Proceedings of International Conference on Computer Science and Engineering, pp. 12-20, 2019.
- A.S. Pooja and P. Smitha Vas, “Edge Detection using Deep Learning”, International Research Journal of Engineering and Technology, Vol. 5, No. 7, pp. 1-12, 2018.
- Mohamed A. El-Sayed, Yarub A. Estaitia and Mohamed A. Khafagy, “Automated Edge Detection using Convolutional Neural Network”, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 3, pp. 1-13, 2013.
- A. Ahmed, Y.C. Byun and D. Hazra, “Edge Detection for Roof Images using Transfer Learning”, Proceedings of 18th International Conference on Computer and Information Science, pp. 1-7, 2019.
- Chenxing Xue, Jun Zhang, Jiayuan Xing, Yuting Lei and Yan Sun, “Research on Edge Detection Operator of a Convolutional Neural Network”, Proceedings of Joint International Conference on Information Technology and Artificial Intelligence, pp. 1-14, 2020.
- Z. Qu, P. Wang and Z.K. Shen, “Fast SUSAN Edge Detector by Adapting Step-Size”, Optik - International Journal for Light and Electron Optics, Vol. 124, No. 3, pp. 747-750, 2013.
- C. Gao, H. Zhu and Y. Guo, Y. (2012), “Analysis and improvement of SUSAN algorithm Signal Processing”, Vol. 92, No. 10, pp. 2552-2559, 2012.
- Shenghua Xu, Litao Han and Lihua Zhang, “An Algorithm to Edge Detection Based on SUSAN Filter and Embedded Confidence”, Proceedings of 6th International Conference on Intelligent Systems Design and Applications, pp. 1-11, 2006.
- X. Wei, S. Jiang and Y. Li, “Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology”, IEEE Transactions on Intelligent Transportation System, Vol. 21, No. 3, pp. 947-958, 2019.
- Huanli Li, Lihong Guo and Tao Chen, “The Corner Detector of Teeth Image Based on the Improved SUSAN Algorithm”, Proceedings of International Conference on Biomedical Engineering and Informatics, pp. 16-18, 2010.
- E. Rafajlowicz, “SUSAN Edge Detector Reinterpreted, Simplified and Modified”, Proceedings of International Workshop on Multidimensional Systems, pp. 1-14, 2021.
- Xiaofeng Li, Hongshuang Jiao and Yanwei Wang, “Edge Detection Algorithm of Cancer Image based on Deep Learning”, Bioengineered, Vol. 11, No. 1, pp. 693-707, 2020.
- Hafiza Huma Taha, Syed Sufyan Ahmed and Haroon Rasheed, “Tumor Detection through Image Processing using MRI”, International Journal of Scientific and Engineering Research, Vol. 6, No. 2, pp. 1-14, 2015.
- H.N.T.K. Kaldera, S.R. Gunasekara and M.B. Dissanayake, “MRI based Glioma Segmentation using Deep Learning Algorithms”, Smart Computing and Systems Engineering, Vol. 8, pp. 1-16, 2019.
- Shanaka Ramesh Gunasekara, Shanaka Ramesh Gunasekara and Maheshi B. Dissanayake, “A Systematic Approach for MRI Brain Tumor Localization and Segmentation using Deep Learning and Active Contouring”, Journal of Healthcare Engineering, Vol. 2021, pp. 1-13, 2021.
- Github, Available at https://github.com/
- Algorithm to Code Converter, Available at http://codershunt.weebly.com/projects/algorithm-to-code-converter
- Visual Studio, Available at https://visualstudio.microsoft.com/
- BSDS500, “Berkeley Segmentation Dataset 500”, Available at https://paperswithcode.com/dataset/bsds500#:~:text=Berkeley%20Segmentation%20Data%20Set%20500%20(BSDS500)%20is%20a%20standard%20benchmark,interior%20boundaries%20and%20background%20boundaries.
Abstract Views: 100
PDF Views: 20