Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images
Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, dia...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/6304915 |
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| author | M. Kumar S. K. Mishra S. S. Sahu |
| author_facet | M. Kumar S. K. Mishra S. S. Sahu |
| author_sort | M. Kumar |
| collection | DOAJ |
| description | Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN) to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN) and the Cat Swarm Optimization (CSO) is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR), have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods. |
| format | Article |
| id | doaj-art-61d92f99487f405ab6adbf94d698fef8 |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-61d92f99487f405ab6adbf94d698fef82025-08-20T02:24:00ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/63049156304915Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography ImagesM. Kumar0S. K. Mishra1S. S. Sahu2Department of EEE, BIT Mesra, Ranchi, IndiaDepartment of EEE, BIT Mesra, Ranchi, IndiaDepartment of ECE, BIT Mesra, Ranchi, IndiaGaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN) to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN) and the Cat Swarm Optimization (CSO) is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR), have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.http://dx.doi.org/10.1155/2016/6304915 |
| spellingShingle | M. Kumar S. K. Mishra S. S. Sahu Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images Applied Computational Intelligence and Soft Computing |
| title | Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images |
| title_full | Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images |
| title_fullStr | Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images |
| title_full_unstemmed | Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images |
| title_short | Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images |
| title_sort | cat swarm optimization based functional link artificial neural network filter for gaussian noise removal from computed tomography images |
| url | http://dx.doi.org/10.1155/2016/6304915 |
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