Geometric Regularized Hopfield Neural Network for Medical Image Enhancement
One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2021/6664569 |
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| author | Fayadh Alenezi K. C. Santosh |
| author_facet | Fayadh Alenezi K. C. Santosh |
| author_sort | Fayadh Alenezi |
| collection | DOAJ |
| description | One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods. |
| format | Article |
| id | doaj-art-f3a5379f4d47459cb14c1b73ca07fa13 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-f3a5379f4d47459cb14c1b73ca07fa132025-02-03T05:52:56ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962021-01-01202110.1155/2021/66645696664569Geometric Regularized Hopfield Neural Network for Medical Image EnhancementFayadh Alenezi0K. C. Santosh1Department of Electrical Engineering, College of Engineering, Jouf University Sakaka, 72388, Saudi ArabiaDepartment of Computer Science, University of South Dakota, Vermillion, SD 57069, USAOne of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.http://dx.doi.org/10.1155/2021/6664569 |
| spellingShingle | Fayadh Alenezi K. C. Santosh Geometric Regularized Hopfield Neural Network for Medical Image Enhancement International Journal of Biomedical Imaging |
| title | Geometric Regularized Hopfield Neural Network for Medical Image Enhancement |
| title_full | Geometric Regularized Hopfield Neural Network for Medical Image Enhancement |
| title_fullStr | Geometric Regularized Hopfield Neural Network for Medical Image Enhancement |
| title_full_unstemmed | Geometric Regularized Hopfield Neural Network for Medical Image Enhancement |
| title_short | Geometric Regularized Hopfield Neural Network for Medical Image Enhancement |
| title_sort | geometric regularized hopfield neural network for medical image enhancement |
| url | http://dx.doi.org/10.1155/2021/6664569 |
| work_keys_str_mv | AT fayadhalenezi geometricregularizedhopfieldneuralnetworkformedicalimageenhancement AT kcsantosh geometricregularizedhopfieldneuralnetworkformedicalimageenhancement |