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Automatic Segmentation of Digital Mammograms to Detect Masses

Mohsen A. M. El-Bendary, H. Abdellatif, M. El-Tokgy, T. E. Taha, Sayed M. EL-Rabaie, O. F. Zahran, W. Al-Nauimy, Saleh Ahmad, F. E. Abd El-Samie

Abstract


Mammography is well known method for detection of breast tumors. Early detection and removal of the primary tumor is an essential and effective method to enhance survival rate and reduce mortality. Breast tumor segmentation is needed for monitoring and quantifying breast cancer. However, automated tumor segmentation in mammograms poses many challenges with regard to characteristics of an image. In this paper we propose a fully automatic algorithm for segmentation of a breast masses, using two types of image segmentation, Normalized graph cuts to delineate pectoral muscle and optimal threshold based on the two-dimensional entropy for masses detection.

Keywords


Mammography, Image Segmentation, Thresholding, Entropy, Normalized Graph Cuts

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References


E. E. Sterns, “Relation between clinical and mammographic diagnosis of breast problems and the cancer/ biopsy rate,” Can. J. Surg., vol. 39, no. 2, p 128-132, 1996 .

R. Highnam , M. Brady, “Mammographic Image Analysis”, Kluwer Academic Publishers, 1999. ISBN: 0-7923- 5620-9.

H. B. Kekre, K. SarodeTanuja and M. GhargeSaylee, "Tumor Detection in Mammography Images using Vector Quantization Technique", International Journal of Intelligent Information Technology Application, 2009, 2(5):237-242.

Nikhil. R. Pal, “On Minimum cross entropy thresholding”, J. Pattern Recognition, vol.29, pp.575-580, 1996.

Paul. L. Rosin, “Unimodal Thresholding”, J. Pattern Recognition,vol.34, pp.2083-2096, 2001.

r. gupta, and p.e. udrill : “The Use of Texture Analysis to Delineate Suspicious Masses in Mammography”, Phys. Med.Biol., vol.40, pp.835-855 (1995) .

s.m. kwok, r. chandrasekhar, y. attikiouzel, and m.t.rickard : “Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms”, ieee trans. on Medical Imaging, vol.23, pp.1129-1140, 8 September(2004).

d. raba, a. oliver, j. martí, m. peracaula, j. espunya :"Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms", Lect. Notes Comp sci 3523:471–478, 2005

Shannon, C.E., “A mathematical Theory of Communication”, Int. J.Bell. Syst. Technical, vol.27, pp. 379-423, 1948.

P. K. Sahoo and G. Arora, "Image thresholding using two-dimensional TsalllisHavrda-Charvat entropy". Pattern Recogn. Lett 26: 520-528,2006

j. shi and j. malik:" Normalized Cuts and Image Segmentation", in cvpr, jun. 1997. 2057, 2058-

s. maji, n.k vishnoi; j. malik:"Biased Normalized Cuts", Patern Analysis and Machine Inteligence, ieee Ttranaction on 20-25 june 2011.

j. suckling,j. parker, d.r dance,s. astley, i. hutt, r.m. boggisc, i. ricketts, e. stamatakis, n.cerneaz,s.l. kok , p.taylor ,d.betal , j.savage: "The Mammographic Image Analysis Society Digital Mammogram Database", Excerpta Medica International Congress Series 1069:375–378, 1994.

t. cour, s. yu, and j. shi, Normalized Cuts Matlab Code [Online]. Available: http://www.cis.upenn.edu/~jshi/software.


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