Automated Detection of Anatomical Regions in Magnetic Resonance Images

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dc.contributor.authorTóth, Márton J.en_US
dc.contributor.authorBlaskovics, Tamásen_US
dc.contributor.authorRuskó, Lászlóen_US
dc.contributor.authorDelso, Gasparen_US
dc.contributor.authorCsébfalvi, Balázsen_US
dc.contributor.editorJan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urbanen_US
dc.date.accessioned2014-12-16T07:26:13Z
dc.date.available2014-12-16T07:26:13Z
dc.date.issued2014en_US
dc.description.abstractRecognition of body parts in three-dimensional medical images is an important task in many clinical applications. It can facilitate image segmentation, registration methods and it can be the first step of an automatic image-processing workflow. In this paper, we propose an automated method to classify the axial slices of threedimensional magnetic resonance image series according to the body part they belong to. We apply the Zernike transform to obtain feature vectors representing the structural information of the axial slices. Using machine learning tools statistical correlation is found between the extracted feature vectors and the position of the slices within the human body. The initial classification is filtered by a dynamic programming based error correction method that takes the correct sequence of anatomy regions into consideration to eliminate the false recognitions. Using our approach, different body regions can be recognized at high precision rate.en_US
dc.description.seriesinformationVision, Modeling & Visualizationen_US
dc.identifier.isbn978-3-905674-74-3en_US
dc.identifier.urihttps://doi.org/10.2312/vmv.20141279en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.8 [Image Processing and Computer Vision]en_US
dc.subjectScene Analysisen_US
dc.subjectObject recognitionen_US
dc.titleAutomated Detection of Anatomical Regions in Magnetic Resonance Imagesen_US
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