Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm
[Display omitted] •Kapur entropy method measuring the homogeneity of segmented classes.•T2 weighted axial MR brain images are considered for segmentation.•Higher entropy value and low standard deviation are explain the better and consistent performance of the algorithm. Medical image analysis is one...
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Veröffentlicht in: | Measurement : journal of the International Measurement Confederation 2014-01, Vol.47, p.558-568 |
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container_title | Measurement : journal of the International Measurement Confederation |
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creator | Manikandan, S. Ramar, K. Willjuice Iruthayarajan, M. Srinivasagan, K.G. |
description | [Display omitted]
•Kapur entropy method measuring the homogeneity of segmented classes.•T2 weighted axial MR brain images are considered for segmentation.•Higher entropy value and low standard deviation are explain the better and consistent performance of the algorithm.
Medical image analysis is one of the major research areas in the last four decades. Many researchers have contributed quite good algorithms and reported results. In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images. The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation. The optimum multilevel thresholding is found by maximizing the entropy. The results are compared with the results of the existing algorithms like Nelder–Mead simplex, PSO, BF and ABF. The statistical performances of the 100 independent runs are reported. The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods. |
doi_str_mv | 10.1016/j.measurement.2013.09.031 |
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•Kapur entropy method measuring the homogeneity of segmented classes.•T2 weighted axial MR brain images are considered for segmentation.•Higher entropy value and low standard deviation are explain the better and consistent performance of the algorithm.
Medical image analysis is one of the major research areas in the last four decades. Many researchers have contributed quite good algorithms and reported results. In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images. The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation. The optimum multilevel thresholding is found by maximizing the entropy. The results are compared with the results of the existing algorithms like Nelder–Mead simplex, PSO, BF and ABF. The statistical performances of the 100 independent runs are reported. The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods.</description><identifier>ISSN: 0263-2241</identifier><identifier>EISSN: 1873-412X</identifier><identifier>DOI: 10.1016/j.measurement.2013.09.031</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Brain ; Entropy ; Genetic algorithms ; Image segmentation ; Medical ; MRI brain image ; Multilevel ; Optimization ; Real coded genetic algorithm ; Segmentation ; Thresholds</subject><ispartof>Measurement : journal of the International Measurement Confederation, 2014-01, Vol.47, p.558-568</ispartof><rights>2013 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c354t-82d192bc6c2df638ecca3ed8aa37618b042608770519b8ad0d5ac5c716d0c7463</citedby><cites>FETCH-LOGICAL-c354t-82d192bc6c2df638ecca3ed8aa37618b042608770519b8ad0d5ac5c716d0c7463</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.measurement.2013.09.031$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Manikandan, S.</creatorcontrib><creatorcontrib>Ramar, K.</creatorcontrib><creatorcontrib>Willjuice Iruthayarajan, M.</creatorcontrib><creatorcontrib>Srinivasagan, K.G.</creatorcontrib><title>Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm</title><title>Measurement : journal of the International Measurement Confederation</title><description>[Display omitted]
•Kapur entropy method measuring the homogeneity of segmented classes.•T2 weighted axial MR brain images are considered for segmentation.•Higher entropy value and low standard deviation are explain the better and consistent performance of the algorithm.
Medical image analysis is one of the major research areas in the last four decades. Many researchers have contributed quite good algorithms and reported results. In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images. The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation. The optimum multilevel thresholding is found by maximizing the entropy. The results are compared with the results of the existing algorithms like Nelder–Mead simplex, PSO, BF and ABF. The statistical performances of the 100 independent runs are reported. The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods.</description><subject>Brain</subject><subject>Entropy</subject><subject>Genetic algorithms</subject><subject>Image segmentation</subject><subject>Medical</subject><subject>MRI brain image</subject><subject>Multilevel</subject><subject>Optimization</subject><subject>Real coded genetic algorithm</subject><subject>Segmentation</subject><subject>Thresholds</subject><issn>0263-2241</issn><issn>1873-412X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkEFr3DAQhUVpoNs0_0G95WJ3JNmyfSxL0hRSemmhN6Edjb1aZCuR5ED_fb1sDznmNDB878H7GPssoBYg9JdTPZPNa6KZllJLEKqGoQYl3rGd6DtVNUL-ec92ILWqpGzEB_Yx5xMAaDXoHcMfayg-0AsFXo6J8jEG55eJjzHxTNO51hYfFx5HPpPzaAM_JOsX7mc7UeZrPuOJtj9GR45PtFDxyG2YYvLlOH9iV6MNmW7-32v2-_7u1_6hevz57fv-62OFqm1K1UsnBnlAjdKNWvWEaBW53lrVadEfoJEa-q6DVgyH3jpwrcUWO6EdYNdodc1uL71PKT6vlIuZfUYKwS4U12xEq2AYQHXdhg4XFFPMOdFontK2J_01AsxZrDmZV2LNWayBwWxit-z-kqVty4unZDJ6WnCTkwiLcdG_oeUf5H-J9w</recordid><startdate>201401</startdate><enddate>201401</enddate><creator>Manikandan, S.</creator><creator>Ramar, K.</creator><creator>Willjuice Iruthayarajan, M.</creator><creator>Srinivasagan, K.G.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201401</creationdate><title>Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm</title><author>Manikandan, S. ; Ramar, K. ; Willjuice Iruthayarajan, M. ; Srinivasagan, K.G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c354t-82d192bc6c2df638ecca3ed8aa37618b042608770519b8ad0d5ac5c716d0c7463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Brain</topic><topic>Entropy</topic><topic>Genetic algorithms</topic><topic>Image segmentation</topic><topic>Medical</topic><topic>MRI brain image</topic><topic>Multilevel</topic><topic>Optimization</topic><topic>Real coded genetic algorithm</topic><topic>Segmentation</topic><topic>Thresholds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Manikandan, S.</creatorcontrib><creatorcontrib>Ramar, K.</creatorcontrib><creatorcontrib>Willjuice Iruthayarajan, M.</creatorcontrib><creatorcontrib>Srinivasagan, K.G.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Measurement : journal of the International Measurement Confederation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Manikandan, S.</au><au>Ramar, K.</au><au>Willjuice Iruthayarajan, M.</au><au>Srinivasagan, K.G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm</atitle><jtitle>Measurement : journal of the International Measurement Confederation</jtitle><date>2014-01</date><risdate>2014</risdate><volume>47</volume><spage>558</spage><epage>568</epage><pages>558-568</pages><issn>0263-2241</issn><eissn>1873-412X</eissn><abstract>[Display omitted]
•Kapur entropy method measuring the homogeneity of segmented classes.•T2 weighted axial MR brain images are considered for segmentation.•Higher entropy value and low standard deviation are explain the better and consistent performance of the algorithm.
Medical image analysis is one of the major research areas in the last four decades. Many researchers have contributed quite good algorithms and reported results. In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images. The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation. The optimum multilevel thresholding is found by maximizing the entropy. The results are compared with the results of the existing algorithms like Nelder–Mead simplex, PSO, BF and ABF. The statistical performances of the 100 independent runs are reported. The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.measurement.2013.09.031</doi><tpages>11</tpages></addata></record> |
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subjects | Brain Entropy Genetic algorithms Image segmentation Medical MRI brain image Multilevel Optimization Real coded genetic algorithm Segmentation Thresholds |
title | Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm |
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