Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis
This paper presents an efficient skull stripping method to improve the decision-making process. Extended Weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu’s and Tsa...
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| Veröffentlicht in: | Neural computing & applications 2023-04, Vol.35 (10), p.7315-7338 |
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| creator | Vijh, Surbhi Pandey, Hari Mohan Gaurav, Prashant |
| description | This paper presents an efficient skull stripping method to improve the decision-making process. Extended Weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu’s and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%). |
| doi_str_mv | 10.1007/s00521-021-06709-w |
| format | Article |
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Extended Weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu’s and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%).</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-021-06709-w</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Accuracy ; Algorithms ; Artificial Intelligence ; Brain ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Decision making ; Discriminant analysis ; Embedding ; emerging fuzzy hybridization systems ; Feature extraction ; fuzzy and their Hybridization ; Image enhancement ; Image Processing and Computer Vision ; Image quality ; Image segmentation ; Medical imaging ; neuro-fuzzy ; Optimization ; Optimization algorithms ; Probability and Statistics in Computer Science ; S.I. : Neuro ; S.I: Fuzzy inference ; Sensitivity ; Statistical analysis</subject><ispartof>Neural computing & applications, 2023-04, Vol.35 (10), p.7315-7338</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-c5ca0ba59933bc3a6a6b8a21a6277fd3a715f91e31640f8a4e615ec5b155ddcc3</citedby><cites>FETCH-LOGICAL-c363t-c5ca0ba59933bc3a6a6b8a21a6277fd3a715f91e31640f8a4e615ec5b155ddcc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-021-06709-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-021-06709-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Vijh, Surbhi</creatorcontrib><creatorcontrib>Pandey, Hari Mohan</creatorcontrib><creatorcontrib>Gaurav, Prashant</creatorcontrib><title>Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>This paper presents an efficient skull stripping method to improve the decision-making process. Extended Weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu’s and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%).</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Brain</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Decision making</subject><subject>Discriminant analysis</subject><subject>Embedding</subject><subject>emerging fuzzy hybridization systems</subject><subject>Feature extraction</subject><subject>fuzzy and their Hybridization</subject><subject>Image enhancement</subject><subject>Image Processing and Computer Vision</subject><subject>Image quality</subject><subject>Image segmentation</subject><subject>Medical imaging</subject><subject>neuro-fuzzy</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Probability and Statistics in Computer Science</subject><subject>S.I. : Neuro</subject><subject>S.I: Fuzzy inference</subject><subject>Sensitivity</subject><subject>Statistical analysis</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kc1u3CAURlGUSJkmeYGskLJ2C8Zge5mO-ieN1E2iLK1ruHaY2ngKWNOZR8nTFsuVusviwoLvHK70EXLP2UfOWPkpMCZznrFlVMnq7HhBNrwQIhNMVpdkw-pieSrENfkQwp4xVqhKbsjbZw_W0TiPk6cB-xFdhGgnR-dgXU_xT0Rn0NAXtA49BWfoDg4DaAuODktwOkQ72vNKwdBP3sbXkR7TSbv5fD7RI9r-NSbJr2zEhOHYojGLfkhS8NTYoH2SOHAxfQHDKdhwS646GALe_btvyPPXL0_b79nu57cf28ddpoUSMdNSA2tB1rUQrRagQLUV5BxUXpadEVBy2dUcBVcF6yooUHGJWrZcSmO0FjfkYfUe_PR7xhCb_TT7tERo8rIq6ypXuUypfE1pP4XgsWsOaWPwp4azZumgWTto2DJLB80xQWKFQgq7Hv1_9TvUX7_Jjyk</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Vijh, Surbhi</creator><creator>Pandey, Hari Mohan</creator><creator>Gaurav, Prashant</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20230401</creationdate><title>Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis</title><author>Vijh, Surbhi ; Pandey, Hari Mohan ; Gaurav, Prashant</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-c5ca0ba59933bc3a6a6b8a21a6277fd3a715f91e31640f8a4e615ec5b155ddcc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Brain</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Decision making</topic><topic>Discriminant analysis</topic><topic>Embedding</topic><topic>emerging fuzzy hybridization systems</topic><topic>Feature extraction</topic><topic>fuzzy and their Hybridization</topic><topic>Image enhancement</topic><topic>Image Processing and Computer Vision</topic><topic>Image quality</topic><topic>Image segmentation</topic><topic>Medical imaging</topic><topic>neuro-fuzzy</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Probability and Statistics in Computer Science</topic><topic>S.I. : Neuro</topic><topic>S.I: Fuzzy inference</topic><topic>Sensitivity</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vijh, Surbhi</creatorcontrib><creatorcontrib>Pandey, Hari Mohan</creatorcontrib><creatorcontrib>Gaurav, Prashant</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vijh, Surbhi</au><au>Pandey, Hari Mohan</au><au>Gaurav, Prashant</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2023-04-01</date><risdate>2023</risdate><volume>35</volume><issue>10</issue><spage>7315</spage><epage>7338</epage><pages>7315-7338</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>This paper presents an efficient skull stripping method to improve the decision-making process. Extended Weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu’s and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%).</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-021-06709-w</doi><tpages>24</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Artificial Intelligence Brain Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Decision making Discriminant analysis Embedding emerging fuzzy hybridization systems Feature extraction fuzzy and their Hybridization Image enhancement Image Processing and Computer Vision Image quality Image segmentation Medical imaging neuro-fuzzy Optimization Optimization algorithms Probability and Statistics in Computer Science S.I. : Neuro S.I: Fuzzy inference Sensitivity Statistical analysis |
| title | Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis |
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