Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5120
Title: An automatic level set based liver segmentation from MRI data sets
Authors: Göçeri, Evgin
Ünlü, Mehmet Zübeyir
Güzeliş, Cüneyt
Dicle, Oğuz
Keywords: Geometric active contours
Level set method
Liver segmentation
MRI
Magnetic resonance imaging
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Göçeri, E., Ünlü, M. Z., Güzeliş, C., and Dicle, O. (2012, October). An automatic level set based liver segmentation from MRI data sets. Paper presented at the 3rd International Conference on Image Processing Theory, Tools and Applications, IPTA 2012. doi:10.1109/IPTA.2012.6469551
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.
Description: 3rd International Conference on Image Processing Theory, Tools and Applications, IPTA 2012; Istanbul; Turkey; 15 October 2012 through 18 October 2012
URI: http://doi.org/10.1109/IPTA.2012.6469551
http://hdl.handle.net/11147/5120
ISBN: 9781467325837
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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