{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:49:12Z","timestamp":1790200152076,"version":"4.1.0"},"reference-count":47,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"National Centre for Research and Development","doi-asserted-by":"crossref","award":["PBS3\/B9\/34\/2015"],"award-info":[{"award-number":["PBS3\/B9\/34\/2015"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>The study introduces a novel method for automatic segmentation of vertebral column tissue from MRI images. The paper describes a method that combines multiple stages of Machine Learning techniques to recognize and separate different tissues of the human spine. For the needs of this paper, 50 MRI examinations presenting lumbosacral spine of patients with low back pain were selected. After the initial filtration, automatic vertebrae recognition using Cascade Classifier takes place. Afterwards the main segmentation process using the patch based Active Appearance Model is performed. Obtained results are interpolated using centripetal Catmull\u2013Rom splines. The method was tested on previously unseen vertebrae images segmented manually by 5 physicians. A test validating algorithm convergence per iteration was performed and the Intraclass Correlation Coefficient was calculated. Additionally, the 10-fold cross-validation analysis has been done. Presented method proved to be comparable to the physicians (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi mathvariant=\"normal\">F<\/mml:mi><mml:mi mathvariant=\"normal\">F<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn mathvariant=\"normal\">90.19<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn mathvariant=\"normal\">1.01<\/mml:mn><mml:mi mathvariant=\"normal\">%<\/mml:mi><\/mml:math>). Moreover results confirmed a proper algorithm convergence. Automatically segmented area correlated well with manual segmentation for single measurements (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mover accent=\"false\"><mml:mrow><mml:mi>r<\/mml:mi><\/mml:mrow><mml:mo>\u00af<\/mml:mo><\/mml:mover><mml:mo>=<\/mml:mo><mml:mn mathvariant=\"normal\">0.8336<\/mml:mn><\/mml:math>) and for average measurements (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mover accent=\"false\"><mml:mrow><mml:mi>r<\/mml:mi><\/mml:mrow><mml:mo>\u00af<\/mml:mo><\/mml:mover><mml:mo>=<\/mml:mo><mml:mn mathvariant=\"normal\">0.9068<\/mml:mn><\/mml:math>) with <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mi>p<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn mathvariant=\"normal\">0.05<\/mml:mn><\/mml:math>. The 10-fold cross-validation analysis (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mi mathvariant=\"normal\">F<\/mml:mi><mml:mi mathvariant=\"normal\">F<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn mathvariant=\"normal\">91.37<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn mathvariant=\"normal\">1.13<\/mml:mn><mml:mi mathvariant=\"normal\">%<\/mml:mi><\/mml:math>) confirmed a good model generalization resulting in practical performance.<\/jats:p>","DOI":"10.1155\/2018\/7952946","type":"journal-article","created":{"date-parts":[[2018,4,29]],"date-time":"2018-04-29T19:31:45Z","timestamp":1525030305000},"page":"1-13","source":"Crossref","is-referenced-by-count":11,"title":["Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance 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