Deteksi Tumor Hati dengan Graph Cut dan Taksiran Volume Tumornya

Nurjannah Syakrani, Yudi Widhiyasana, Abid Arinu Efendi

Abstract


Liver is one of the most important organs in the human body. One of the dangerous diseases of the liver is tumor. In the CT scan image, the tumor has different texture, color, shape, and position, according to patient's condition. In this study, a tumor detection was carried out by tree stages: firstly some steps of preprocessing, such as filtering, edge detection, and erotion; secondly, finding the liver among organs in abdomen using segmentation and checking the liver position in the right abdomen; and thirdly performing the tumor detection in the liver using graph cut and push relabel algorithm. Usually, segmentation using graph cut needs two interactive inputs, namely sample of object area and sample of background area. In this paper, the interactive inputs on graph cut were replaced by deviation standard calculation. Testing using three sets of CT image and the ground truth produces average of the dice similarity coefficient (DSC), volumetric overlap error (VOE), and absolute volume difference (AVD) parameters of 78.15%, 25.72%, 19.30%, respectively. Furthermore, volume of liver tumor is approximated by utilizing area of tumor in each slice of CT image, then displayed in 3D view.

Keywords


deteksi, segmentasi, tumor hati, graph cut, push relabel

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References


“Cancer”, World Health Organization, [Online]. Tersedia: http://www.who.int/mediacentre/factsheets/fs297/en/. [Diakses 31 Juli 2017].

“Liver Cancer in Indonesia,” World Life Expectancy, 2014. [Online]. Tersedia: http://www.worldlifeexpectancy.com/indonesia-liver-cancer. [Diakses 24 Februari 2017].

“Test and Procedures CTscan”, Mayo Clinic, [Online]. Tersedia: http://www.mayoclinic.org/tests-procedures/ctscan/basics/definition/prc-20014610. [Diakses 18 January 2017].

A.N. Kurniawan., T.S. Widodo, I. Soesanti, JNTETI, Vol. 2, No. 4, Februari 2013.

B.N. Li, C.K. Chui, S. Chang, dan H.S. Ong, “A new unified level set method for semi-automatic liver tumor segmentation,” Expert Systems with Applications, Vol. 39, No. 10, 2012.

Y. Häme dan M. Pollari, “Semi-automatic liver tumor segmentation with hidden Markov measure field and non-parametric distribution estimation,” Medical Image Analysis, Vol. 16, No. 1, pp. 140-149, 2012.

R.S. Moni, S.S. Kumar dan J. Rajeesh, “Automatic Segmentation of Liver and Tumor for CAD of Liver,” Journal Of Advances In Information Technology, Vol. 2, No. 1, 2011.

Y. Qi, W. Xiong, W.K. Leow, Q. Tian, J. Zhou, J. Liu, T. Han, S.K. Venkatesh, dan S.-c. Wang, “Semi-automatic Segmentation of Liver Tumors from CT Scans Using Bayesian Rule-based 3D Region Growing,” The Midas Journal, 2008.

L. Massoptier dan S. Casciaro, “Fully Automatic Liver Segmentation through Graph-Cut Technique,” 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, Aug. 23-26, 2007.

Miccai Competition 2017, [Online]. Tersedia:

https://competitions.codalab.org/competitions/17094, [Diakses 19 Januari 2017].

G.M. Cooper, Elements of Human Cancer, Boston: Jones and Bartlett, 1992.

“Liver cancer,” [Online]. Tersedia: http://medicaldictionary.thefreedictionary.com/liver+cancer. [Diakses 04 Juli 2017].

“Background Information”, [Online]. Tersedia:

https://nifti.nimh.nih.gov/background, [Diakses 15 Juni 2017].

“NIfTI-1 Data Format”, [Online]. Tersedia: https://nifti.nimh.nih.gov/nifti-1, [Diakses 15 Juni 2017].

“Coordinate systems and affines”, NIPY, [Online]. Tersedia: http://nipy.org/nibabel/coordinate_systems.html, [Diakses 3 Agustus 2017].

K.H. Rosen, Discrete Mathematics and Its Application, 7th Edition, New York: McGraw-Hill Education, 2011.

S.S. Skiena, The Algorithm Design Manual, Second Edition, New York: Springer, 2008.

C. Ananth, D.R. Bai, K. Renuka, A. Vidhya, dan C. Savithra, “Liver And Hepatic Tumors Segmentation in 3-D CT Images”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. III, No. 2, 2014.

Y. Boykov, O. Veksler, dan R. Za ih, “Fast Approximate Energy Minimization via Graph Cuts”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 11, 2001.

S.N. Sinha, “Graph Cut Algorithms in Vision, Graphics and Machine Learning”, Integrative Paper, 2004.

T.H. Cormen, C.E. Leiserson, R.L. Rivest, dan C. Stein, Introduction to Algorithms, third edition, London: MIT Press, 2009.

Al-Shayeh dan M.S. Al-Ani, “Efficient 3D O ject Visualization via 2D Images”, International Journal of Computer Science and Network Security, Vol. 9, No. 11, Nov. 2009, hal. 234-239.

“mla : Python scripting for 3D for plotting,” Mayavi, [Online]. Tersedia: http://docs.enthought.com/mayavi/mayavi/mlab.html. [Diakses 19 Juli 2017].




DOI: http://dx.doi.org/10.22146/jnteti.v7i1.398

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