An automatic level set based liver segmentation from MRI data sets


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Goceri E., Unlu M. Z., Guzelis C., DİCLE O.

2012 3rd International Conference on Image Processing Theory, Tools and Applications, IPTA 2012, İstanbul, Turkey, 15 - 18 October 2012, pp.192-197 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/ipta.2012.6469551
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.192-197
  • Keywords: Liver segmentation, MRI, Geometric active contours, Level set method, ACTIVE CONTOURS, T-SNAKES, SHAPE, IMAGES, CT, ENERGY
  • Dokuz Eylül University Affiliated: Yes

Abstract

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results. © 2012 IEEE.