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Magnetic Resonance Signal Processing in Medical Applications
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SYSNO ASEP 0386210 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Magnetic Resonance Signal Processing in Medical Applications Author(s) Mikulka, J. (CZ)
Gescheidtová, E. (CZ)
Bartušek, Karel (UPT-D) RID, ORCID, SAINumber of authors 3 Source Title ICONS 2012: The Seventh Interatnional Conference on Systems. - Saint Gilles : IARIA, 2012 - ISBN 978-1-61208-184-7 Pages s. 148-153 Number of pages 6 s. Publication form Print - P Action GlobeNet 2012: ICN 2012, ICONS 2012, VisGra 2012, DBKDA 2012 Event date 29.02.2012-05.03.2012 VEvent location Saint Gilles Country RE - Réunion Event type WRD Language eng - English Country RE - Réunion Keywords magnetic resonance ; biomedical image processing ; image segmentation ; level set ; active countour ; edge analysis ; noise suppression ; volumetry Subject RIV JA - Electronics ; Optoelectronics, Electrical Engineering R&D Projects ED0017/01/01 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GAP102/11/0318 GA ČR - Czech Science Foundation (CSF) Institutional support UPT-D - RVO:68081731 Annotation Image processing in biomedical applications is an important developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. It is necessary to choose a suitable segmentation method to create a correct three-dimensional model. The accuracy of reconstruction depends on precision of regions boundary determining in magnetic resonance slices. A frequent application is detection of soft tissues. To obtain images of the soft tissues mentioned, tomography based on magnetic resonance is usually used. Ideally, several tissue slices in three orthogonal planes (sagittal, coronal, transverse) are acquired. Following reconstruction of shape of examined tissues is the most accurate. In case of acquired slices only in one plane, the high spatial information lost occurs by image acquisition. Then it is necessary to reconstruct the shape of tissue appropriately. At first the images are segmented and with use of particular segments the three dimensional model is composed. This article compares several segmentation approaches of magnetic resonance images and their results. The results of segmentation by active contour, thresholding, edge analysis by Sobel mask, watershed and region-based level set segmentation methods are compared. The results for different values of parameters of segmentation methods are compared. As the test image, slice of human liver tumour was chosen. Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2013
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