{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:16:13Z","timestamp":1772064973515,"version":"3.50.1"},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2018,4,29]],"date-time":"2018-04-29T00:00:00Z","timestamp":1524960000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, an efficient computer\u2010aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m\u2010band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with <jats:italic>k<\/jats:italic>\u2010fold approach.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4<jats:sup>th<\/jats:sup> level statistical features of DTMBWT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.27210","type":"journal-article","created":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T05:05:28Z","timestamp":1525064728000},"page":"2393-2401","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Analysis of dual tree M\u2010band wavelet transform based features for brain image classification"],"prefix":"10.1002","volume":"80","author":[{"given":"Ratna Raju","family":"Ayalapogu","sequence":"first","affiliation":[{"name":"Mahatma Gandhi Institute of Technology  Hyderabad Telangana India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suresh","family":"Pabboju","sequence":"additional","affiliation":[{"name":"Department of Information Technology Chaitanya Bharathi Institute of Technology  Hyderabad Telangana India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajeswara Rao","family":"Ramisetty","sequence":"additional","affiliation":[{"name":"Jawaharal Nehru Technological University University College of Engineering Vizianagaram  Andhra Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,4,29]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2009.07.002"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.02.012"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.12.066"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2013.09.001"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2006.12.001"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2013.08.017"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22147"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.2528\/PIER16070801"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2016.07.008"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.03.046"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1049\/htl.2013.0022"},{"key":"e_1_2_6_13_1","doi-asserted-by":"crossref","unstructured":"LahmiriS BoukadoumM DiIevaA.Fractal\u2010based arteriovenous malformations detection in brain magnetic resonance images. 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