Nosologic imaging of the brain: segmentation and classification using MRI and MRSI (Articolo in rivista)

Type
Label
  • Nosologic imaging of the brain: segmentation and classification using MRI and MRSI (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Alternative label
  • Luts J., Laudadio T., Idema A.J., Simonetti A.W., Heerschap A., Vandermeulen D., Suykens J.A.K., Van Huffel S. (2009)
    Nosologic imaging of the brain: segmentation and classification using MRI and MRSI
    in NMR in biomedicine
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Luts J., Laudadio T., Idema A.J., Simonetti A.W., Heerschap A., Vandermeulen D., Suykens J.A.K., Van Huffel S. (literal)
Pagina inizio
  • 374 (literal)
Pagina fine
  • 390 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 22 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Department of Electrical Engineering (ESAT), Research Division SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; Istituto Applicazioni Calcolo, CNR, sez. Bari, via G. Amendola 122/D, I-70126 Bari, Italy; Department of Neurosurgery, University of Nijmegen, University Medical Center, Geert Grooteplein Z18, PO Box 9101, 6500 HB Nijmegen, Netherlands; Philips Medical Systems, QR-1121, PO Box 10000, 5680 DA Best, Netherlands; Department of Radiology, University of Nijmegen, University Medical Center, Geert Grooteplein Z18, PO Box 9101, 6500 HB Nijmegen, Netherlands; Department of Electrical Engineering (ESAT), Research Division PSI, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; (literal)
Titolo
  • Nosologic imaging of the brain: segmentation and classification using MRI and MRSI (literal)
Abstract
  • A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumours. First, a registered brain atlas and a subject-specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high-resolution nosologic images, representing tumour heterogeneity and class probabilities, which may help clinicians in decision making. (literal)
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