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2011 | Vol. 7, no. 4 | 95--99
Tytuł artykułu

An application software for anatomical structures segmentation from tomographic data of abdominal cavity

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
New, increasingly sophisticated and accessible imaging methods, ranging from Computed Tomography (CT), Magnetic Resonance Imaging (MRI) to Positron Emission Tomography (PET) allow scientists and medical doctors to obtain non-invasively potentially life-saving information about patient condition and body. However, the purpose of recent medical imaging is not only to obtain simple visualization and insight into human anatomic structures, but also as a powerful tool for computer-aided diagnosis, radiotherapies, tumor tracking and surgical operation planning and simulation. This is exactly the field where very accurate and reliable image segmentation methods can play a key role. In this work, a cross-platform application software equipped in algorithm for segmentation of anatomical structures from abdomen CT data has been developed. As a segmentation technique balancing good accuracy and efficiency, a semi-automated, region growing segmentation algorithm, based on voxel intensities criteria, was used. Additional features of an application are: ability to visualize large medical data, render segmentation results in 3D as well as support of DICOM standard.
Wydawca

Rocznik
Strony
95--99
Opis fizyczny
Bibliogr. 10 poz., rys.
Twórcy
autor
  • AGH University Of Science And Technology, Department Of Measurement And Instrumentation, Al. A. Mickiewicza 30, Kraków, Poland, gtoporek@gmail.com
  • AGH University Of Science And Technology, Multidisciplinary School Of Engineering In Medicine, Al. A. Mickiewicza 30, Kraków, Poland
autor
  • AGH University Of Science And Technology, Department Of Measurement And Instrumentation, Al. A. Mickiewicza 30, Kraków, Poland, socha@agh.edu.pl
autor
  • AGH University Of Science And Technology, Department Of Measurement And Instrumentation, Al. A. Mickiewicza 30, Kraków, Poland, skalski@agh.edu.pl
Bibliografia
  • 1. Wirjadi O.: Survey of 3D image segmentation Methods. Technical Report, ITWM 2007.
  • 2. Acton S. T., Ray N.: Biomedical Image Analysis: Segmentation. Morgan & Claypool Publishers 2009.
  • 3. Tadeusiewicz R., Śmietański J.: Acquisition of medical images and their processing, analysis, automatic recognition and diagnostic interpretation. (in Polish) Pozyskiwanie obrazów medycznych oraz ich przetwarzanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja. Kraków: Wydawnictwo STN, 2011.
  • 4. Tadeusiewicz R., Ogiela M. R.: Medical image understanding technology: artificial intelligence and soft-computing for image understanding. Heidelberg: Springer-Verlag, 2004, pp. 145–149.
  • 5. Sonka M.: Handbook of Medical Imaging. Vol.2, Medical Image Processing and Analysis. SPIE The International Society for Optical Engineering, 2000.
  • 6. McInerney T., Terzopoulos D.: Deformable models in medical image analysis: a survey. Medical image analysis 1996, 1(2): 91–108.
  • 7. Fenster A., Chiu B.: Evaluation of segmentation algorithms for medical imaging. Engineering in Medicine and Biology Society, 7186 – 7189, IEEE-EMBS 2005.
  • 8. http://public.kitware.com/pub/itk/Data/LiverTumor/.
  • 9. www.itk.org.
  • 10. www.vtk.org.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-24528191-5121-49c6-aa93-76ccd7013650
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