{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:27:57Z","timestamp":1781105277805,"version":"3.54.1"},"reference-count":0,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,4,1]]},"abstract":"<p>This paper presents a brain tumor automatic segmentation approach applied to magnetic resonance (MR) images. The authors' approach addresses all types of brain tumors. The proposed method involves therefore: image pre-processing, feature extraction via wavelet transform-spatial gray level dependence matrix (WT-SGLDM), dimensionality reduction using genetic algorithm (GA), parameters optimization by GA-SVM model and classification of the reduced features using support vector machine (SVM). These optimal features and optimized parameters are employed for the segmentation of brain tumor. The resulting method is aimed at early tumor diagnostics support by distinguishing between the brain tissue, benign tumor and malignant tumor tissue. The authors' contribution consists in involving the parameters optimization phase to improve the classification and segmentation results by using GA-SVM model. The segmentation results in different types of brain tissue are evaluated by comparison with the manual segmentation as well as with other existing techniques. The qualitative evaluation shows that their approach outperforms manual segmentation with a Match Percent measure (MP) equal to 97.08% and 98.89% for the malignant and the benign tumors respectively. The quantitative evaluation displays that the authors' attitude overtakes FCM algorithm with an accuracy rate of 99.69% for benign tumor and 99.36% for malignant tumor.<\/p>","DOI":"10.4018\/ijcini.2014040102","type":"journal-article","created":{"date-parts":[[2015,4,3]],"date-time":"2015-04-03T09:06:46Z","timestamp":1428052006000},"page":"15-33","source":"Crossref","is-referenced-by-count":4,"title":["Toward Efficient Segmentation of Brain Tumors Based on Support Vector Machine Classifier Through Optimized RBF Kernel Parameters and Optimal Texture Features"],"prefix":"10.4018","volume":"8","author":[{"given":"Ahmed","family":"Kharrat","sequence":"first","affiliation":[{"name":"Computer Embedded Systems Laboratory (CES), University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Abid","sequence":"additional","affiliation":[{"name":"Computer Embedded Systems Laboratory (CES), University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","container-title":["International Journal of Cognitive Informatics and Natural Intelligence"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=126265","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T10:25:25Z","timestamp":1654079125000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJCINI.2014040102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2014,4,1]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2014,4]]}},"URL":"https:\/\/doi.org\/10.4018\/ijcini.2014040102","relation":{},"ISSN":["1557-3958","1557-3966"],"issn-type":[{"value":"1557-3958","type":"print"},{"value":"1557-3966","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,4,1]]}}}