Deformable Model Based Marked Controlled Liver Ct-Scan Image Segmentation
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International Journal of Engineering Trends and Technology (IJETT) | 
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| © 2017 by IJETT Journal | ||
| Volume-46 Number-4 |
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| Year of Publication : 2017 | ||
| Authors : B.Manasa, L.Akhila, G.Shivaji Babu |
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| DOI : 10.14445/22315381/IJETT-V46P239 |
Citation
B.Manasa, L.Akhila, G.Shivaji Babu "Deformable Model Based Marked Controlled Liver Ct-Scan Image Segmentation", International Journal of Engineering Trends and Technology (IJETT), V46(4),226-230 April 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Liver is a gland that plays a major role in metabolism with numerous functions in the human body,
including regulation of glycogen storage, decomposition of red blood cells, plasma protein synthesis, hormone
production, and detoxification. The diagnosis of liver disease is made by liver function tests, groups of blood
tests that can readily show the extent of liver damage. If infection is suspected, then other serological tests will
be carried out. Sometimes, an ultrasound or a CT scan is needed to produce an image of the liver. Physical
examination of the liver can only reveal its size and any tenderness, and some form of imaging will also be
needed. Computerized liver tumor segmentation on contrast-enhanced method is proposed for CT images. It is a
challenging problem due to the great diversity of shape, intensity and texture. Deformable models such a 3D
active surface a previously existing 2D active contour mode. GVF based active contour techniques are used to
segmented the liver in the CT scan image and detects the fatty liver and identify the various problems. Pre-
Processing is done by adaptive bilateral filter which remove noise improves contrast and preserves edges. A
marker controlled active contour method is proposed for liver segmentation. The performance of the proposed
method is evaluated.
References
[1] V.Dey, Y.Zhang, M.Zhong., 2010.A Review on image
segmentation techniques.
[2] L. Rusko, G. Bekes, M. Fidrich, Automatic segmentation of
the liver from multiand single-phase contrast-enhanced CT
images, Med. Image Anal. 13 (2009) 871–882.
[3] D. Smeets, D. Loeckx, B. Stijnen, B. De
Dobbelaer, D. Vandermeulen, P. Suetens, Semi-automatic level
set segmentation of liver tumors combining a spiralscanning
technique with supervised fuzzy pixel classification, Med. Image
Anal. 14 (2010) 13–20.
[4] J. Lee, N. Kim, H. Lee, J.B. Seo, H.J. Won, Y.M. Shin, et al.,
Efficient liver segmentation using a level-set method with optimal
detection of the initial liver boundary from level-set speed images,
Comput. Methods Progr. Biomed. 88 (2007) 26–38.
[5] Y. Boykov, G. Funka-Lea, Graph cuts and efficient N-D
image segmentation, Int. J. Comput. Vis. 70 (2006) 109–131.
[6] G. Chen, L. Gu, L. Qian, J. Xu, An improved level set for
liver segmentation and perfusion analysis in MRIs, IEEE
Trans.Inf. Technol. Biomed. 13 (2009) 94–103.
[7] E. Göçeri, M.N. Gürcan, O. Dicle, Fully automated liver
segmentation from SPIR image series, Comput. Biol. Med.
53(2014) 265–278.
[8] Kass M., Witkin A., Terzopoulos. D.: Snakes: Active Contour Models, in Proceedings of 1st International Conference on
Computer Vision, London, 1987, pp. 259-267
[9] Marek J., Demjénová E., Tomori Z., Janá?ek J., Zolotová I.,
Valle F., Favre M., Dietler G.: Interactive Measurement and
Characterization of DNA Molecules by Analysis of AFM images,
Cytometry Part A, Volume 63A, Issue 2, March 2005, pp. 87-93,
Wiley-Liss, Inc.
[10] Demjénová E., Zolotová I., Tomori Z.: Interactive
Segmentation of Fibrelike Objects, in Proceedings of 2nd
Slovakian-Hungarian Joint Symposium on Applied Machine
Intelligence, SAMI 2004, Her?any, Slovakia, January 16-17, 2004,
pp. 259-268, ISBN 963 7154 23 X.
[11].B.Sridhar, K.V.V.S.Reddy, A.Mallikarjuna Prasad
“Clustered micro calcification detection based on the Texture
feature extraction using Adaptive bilateral filter, mathematical
morphology" i-manager`s Journal on Software Engineering.Vol:8
No:4 April – June 2014 pp. 18-25
Keywords
Liver analysis, bilateral filtering, active contour techniques, dice coefficients.