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More specifically, this scheme is based on: (i) a hybrid adaptive filtering (HAF) process, that utilises genetic algorithms to the curvelet\u2010based representation of images for efficient extraction of the lesion\u2010related morphological characteristics, (ii) differential lacunarity (DL) analysis for texture feature extraction from the HAF\u2010filtered images and (iii) support vector machines for robust classification performance. For the training of the proposed scheme, namely HAF\u2010DL, an 800\u2010image database was used and the evaluation was based on ten 30\u2010second long endoscopic videos. Experimental results, along with comparison with other related efforts, have shown that the HAF\u2010DL approach evidently outperforms the latter in the field of CE image analysis for automated lesion detection, providing higher classification results. The promising performance of HAF\u2010DL paves the way for a complete computer\u2010aided diagnosis system that could support the physicians\u2019 clinical practice.<\/jats:p>","DOI":"10.1049\/htl.2015.0055","type":"journal-article","created":{"date-parts":[[2016,2,17]],"date-time":"2016-02-17T11:17:09Z","timestamp":1455707829000},"page":"27-33","source":"Crossref","is-referenced-by-count":8,"title":["Use of adaptive hybrid filtering process in Crohn's disease lesion detection from real capsule endoscopy videos"],"prefix":"10.1049","volume":"3","author":[{"given":"Vasileios S.","family":"Charisis","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering Aristotle University of Thessaloniki  Thessaloniki 54636 Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leontios J.","family":"Hadjileontiadis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Aristotle University of Thessaloniki  Thessaloniki 54636 Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2016,3,21]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/35013140"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2011.2175445"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2014.06.026"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2008.12.003"},{"key":"e_1_2_8_6_1","unstructured":"Yu L. 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