Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation
Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improv...
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| Published in | Computational intelligence and neuroscience Vol. 2020; no. 2020; pp. 1 - 14 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
2020
Hindawi John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5265 1687-5273 1687-5273 |
| DOI | 10.1155/2020/5047976 |
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| Abstract | Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu’s method, MAOTSU_2D, and two latest 1D Otsu’s methods (Cao’s method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%. |
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| AbstractList | Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu’s method, MAOTSU_2D, and two latest 1D Otsu’s methods (Cao’s method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%. Otsu's algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu's method is more robust compared to 1D Otsu's method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu's algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu's method, MAOTSU_2D, and two latest 1D Otsu's methods (Cao's method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%.Otsu's algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu's method is more robust compared to 1D Otsu's method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu's algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu's method, MAOTSU_2D, and two latest 1D Otsu's methods (Cao's method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%. |
| Audience | Academic |
| Author | Qingge, Letu Xing, Jiangwa Yang, Pei |
| AuthorAffiliation | 3 Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA 1 Research Center of Basic Medical Sciences, Medical College, Qinghai University, Xining 810016, China 2 Department of Computer Technology and Application, Qinghai University, Xining 810016, China |
| AuthorAffiliation_xml | – name: 2 Department of Computer Technology and Application, Qinghai University, Xining 810016, China – name: 3 Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA – name: 1 Research Center of Basic Medical Sciences, Medical College, Qinghai University, Xining 810016, China |
| Author_xml | – sequence: 1 fullname: Xing, Jiangwa – sequence: 2 fullname: Qingge, Letu – sequence: 3 fullname: Yang, Pei |
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| CitedBy_id | crossref_primary_10_1109_TIM_2022_3228008 crossref_primary_10_1364_BOE_468685 crossref_primary_10_1007_s11554_023_01272_0 crossref_primary_10_1088_1361_6501_ad9e0e crossref_primary_10_1007_s12195_023_00780_0 crossref_primary_10_1016_j_inpa_2021_12_003 crossref_primary_10_1016_j_prime_2023_100411 crossref_primary_10_1016_j_jrras_2022_100500 crossref_primary_10_1016_j_bspc_2023_104647 crossref_primary_10_1109_ACCESS_2024_3447716 crossref_primary_10_1007_s10278_025_01457_y crossref_primary_10_1515_bmt_2023_0266 crossref_primary_10_1007_s11265_021_01700_z crossref_primary_10_1155_2022_6274903 |
| Cites_doi | 10.1109/tcyb.2015.2489719 10.1016/j.ijleo.2014.05.003 10.1016/j.patrec.2004.09.035 10.1145/1177352.1177355 10.3724/sp.j.1004.2009.01022 10.1007/978-81-322-0491-6_82 10.1117/1.1631315 10.1016/s0031-3203(97)00043-5 10.1007/s00138-013-0551-8 10.1080/02522667.2017.1383662 10.1109/TIP.2013.2297014 10.1016/j.ijleo.2019.02.118 10.1109/tsmc.1979.4310076 10.1016/j.patrec.2011.12.009 10.1007/s00521-016-2645-5 10.4236/jsip.2015.63023 10.1186/s13634-017-0509-5 10.1049/iet-ipr.2019.0176 10.1109/access.2018.2889013 10.1016/j.imavis.2009.03.004 10.1016/j.apsusc.2015.05.033 10.1109/TMI.2013.2256922 10.1016/j.patrec.2006.03.009 10.5120/8954-3140 10.1109/TPAMI.2008.263 10.1016/j.jvcir.2016.10.013 10.1016/j.patcog.2011.12.013 10.1109/34.85672 10.1016/j.cell.2011.11.055 10.1016/j.patrec.2011.01.021 |
| ContentType | Journal Article |
| Copyright | Copyright © 2020 Jiangwa Xing et al. COPYRIGHT 2020 John Wiley & Sons, Inc. Copyright © 2020 Jiangwa Xing et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 Copyright © 2020 Jiangwa Xing et al. 2020 |
| Copyright_xml | – notice: Copyright © 2020 Jiangwa Xing et al. – notice: COPYRIGHT 2020 John Wiley & Sons, Inc. – notice: Copyright © 2020 Jiangwa Xing et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2020 Jiangwa Xing et al. 2020 |
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| Snippet | Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However,... Otsu's algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu's method is more robust compared to 1D Otsu's method. However,... |
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| SubjectTerms | Algorithms Comparative analysis Coronaviruses Fuzzy sets Illumination Image acquisition Image processing Image segmentation Medical imaging equipment Methods Neighborhoods Noise Partitions Robustness |
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| Title | Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation |
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