Multilevel Thresholding for Image Segmentation Using Mean Gradient
Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some o...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1254852 |
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| _version_ | 1832550397469786112 |
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| author | Abubakar M. Ashir |
| author_facet | Abubakar M. Ashir |
| author_sort | Abubakar M. Ashir |
| collection | DOAJ |
| description | Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically impossible to implement these algorithms in a low-capacity hardware application where computational power and memory utilization are of great concern. In this study, a simple yet effective and noniterative global and bilevel thresholding technique is proposed. It uses the concept of image gradient vector to binarize or segment the image into three clusters. In addition, a parametric preprocessing approach is also proposed that can be used in image restoration applications. Evidences from the experiments from both visual and standard evaluation metrics show that the proposed methods perform exceptionally well. The proposed global thresholding outperforms the formidable Otsu thresholding technique. |
| format | Article |
| id | doaj-art-0f122d5ce5624a8dbcbd3be3e7a854a5 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-0f122d5ce5624a8dbcbd3be3e7a854a52025-02-03T06:06:48ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/1254852Multilevel Thresholding for Image Segmentation Using Mean GradientAbubakar M. Ashir0Department of Computer EngineeringImage binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically impossible to implement these algorithms in a low-capacity hardware application where computational power and memory utilization are of great concern. In this study, a simple yet effective and noniterative global and bilevel thresholding technique is proposed. It uses the concept of image gradient vector to binarize or segment the image into three clusters. In addition, a parametric preprocessing approach is also proposed that can be used in image restoration applications. Evidences from the experiments from both visual and standard evaluation metrics show that the proposed methods perform exceptionally well. The proposed global thresholding outperforms the formidable Otsu thresholding technique.http://dx.doi.org/10.1155/2022/1254852 |
| spellingShingle | Abubakar M. Ashir Multilevel Thresholding for Image Segmentation Using Mean Gradient Journal of Electrical and Computer Engineering |
| title | Multilevel Thresholding for Image Segmentation Using Mean Gradient |
| title_full | Multilevel Thresholding for Image Segmentation Using Mean Gradient |
| title_fullStr | Multilevel Thresholding for Image Segmentation Using Mean Gradient |
| title_full_unstemmed | Multilevel Thresholding for Image Segmentation Using Mean Gradient |
| title_short | Multilevel Thresholding for Image Segmentation Using Mean Gradient |
| title_sort | multilevel thresholding for image segmentation using mean gradient |
| url | http://dx.doi.org/10.1155/2022/1254852 |
| work_keys_str_mv | AT abubakarmashir multilevelthresholdingforimagesegmentationusingmeangradient |