NDLI: An automatic level set based liver segmentation from MRI data sets

Content Provider	IEEE Xplore Digital Library
Author	Goceri, E. Unlu, M.Z. Guzelis, C. Dicle, O.
Copyright Year	2012
Description	Author affiliation: Computer Engineering Department, Pamukkale University, Kinikli, Denizli, Turkey (Goceri, E.) \|\| Faculty of Medicine, Radiology Department, Dokuz Eylül University, Narlidere, Izmir, Turkey (Dicle, O.) \|\| Electrical and Electronics Engineering Department, Izmir Institute of Technology, Urla, Turkey (Unlu, M.Z.) \|\| Electronics and Telecommunications Engineering Department, Izmir University of Economics, Balçova, Turkey (Guzelis, C.)
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.
Starting Page	192
Ending Page	197
File Size	646061
Page Count	6
File Format	PDF
ISBN	9781467325851
ISSN	2154512X
e-ISBN	9781467325844
DOI	10.1109/IPTA.2012.6469551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-15
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Shape Level set Magnetic resonance imaging Liver segmentation MRI Liver Geometric active contours Level set method Active contours Biomedical imaging
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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