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Masses Detection in Digital Mammogram by Gray Level Reduction using Texture coding Method

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 4 - Article 2
Year of Publication: 2011
Al Mutaz .M. Abdalla
Safaai Dress
Nazar Zaki

Al Mutaz M Abdalla, Safaai Dress and Nazar Zaki. Article: Masses Detection in Digital Mammogram by Gray Level Reduction using Texture coding Method. International Journal of Computer Applications 29(4):19-23, September 2011. Full text available. BibTeX

	author = {Al Mutaz .M. Abdalla and Safaai Dress and Nazar Zaki},
	title = {Article: Masses Detection in Digital Mammogram by Gray Level Reduction using Texture coding Method},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {29},
	number = {4},
	pages = {19-23},
	month = {September},
	note = {Full text available}


Breast cancer is the most common cancer in women around the world. Various countries including the UAE offer asymptomatic screening for the disease. The interpretation of mammograms is a very challenges task and is subject to human error. Computer-aided detection and diagnosis have been proposed as a second reader for helping radiologists perform this difficult task. Texture features have been widely used as classification of masses in digital mammogram. In this paper we proposed a method for automatic detection of masses in digital mammogram. The proposed method uses the coding technique achieved good accuracy with Linear Discriminant Analysis (LDA) classification. The classification accuracy by using the coded images is improved much compared to one that obtained from the original image.


  • Smith,R.A., “Screening women aged 40-49: where are we today?” J Natl Cancer Inst, 1995, pp. 1198-1199.
  • Tabar,L., Fagerber,G., and Chen,R.H., “Efficacy of breast screening by age: new results from the swedish two country trial”, Cancer, 1995, pp. 1412-1419.
  • Arodź,T., Kurdziel,M., Sevre,E.O.D., and Yuen,D.A., “Pattern Recognition Techniques for Automatic Detection of Suspicious-looking Anomalies in Mammograms”, Computer Methods and Programs in Biomedicine, Elsevier, 2005, pp. 135-149.
  • H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, H.N. Du (2006) “Approaches for automated detection and classification of masses in mammograms”, Pattern Recognition, Vol. 39, pp. 646-668.
  • Mavroforakis,M.E.,Georgiou,H.V.,Dimitropoulos,N., Cavouras,D., and Theodoridis,S., “Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers”, Artificial Intelligence in Medicine, 2006, pp. 145—162.
  • Szekely, N, Toth, N. Pataki, B. (2004). A hybrid system for detecting masses in mammographic images. Instrumentation and measurement technology conference, 2004. IMTC 04. Proceeding of the 21st IEEE Vol3, 18-20 May 2004 pp2065-2070.
  • K. Bovis and S. Singh. Detection of masses in mammograms using texture features. 15th International Conference on Pattern Recognition (ICPR'00), 2:2267, 2000.
  • Mudigonda, N.R, Rangayyan, R. Desautels, J.E.L (2000); Gradients and texture analysis for the classification of mammographic masses Medical Image, IEEE Transaction on Vol 19, Issue 10, Oct. 2000 pp 1032-1043.
  • N. Youssry, F.E.Z. Abou-Chadi, and A.M. El-Sayad. Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model. 4th Annual IEEE Conf on Information Technology Applications in Biomedicine, 2003.
  • Al Mutaz, M. A., Deris, S., Zaki, N. M. 2011. Detection of Masses in Digital Mammogram Using Second Order Statistics and Artificial Neural Network. International Journal of Computer Science & Information Technology (IJCSIT). Vol.3 No.3
  • Lisboa,P.G., “A review of evidence of health benefit from artificial neural networks”, Neural Networks, 2002, pp. 11-39.
  • Arivazhagan,S., and Ganesan,L., “Textural classification using wavelet transform”, Pat Rec Lett , 2003, pp. 1513-21.
  • D. K. M. Heath, K.W. Bowyer, Current status of the digital database for screening mammography, in: Proceeding of the Fourth International Workshop on Digital Mammography, Kluwer Academic Publishers, 1998,pp.457-460.
  • Haralick,R.M., Shanmugam,K., Dinstein,I., “Textural features for image classification”, IEEE Trans Sys Man Cyb, 1973, pp. 610—21.
  • Fatima Eddaoudi, Fakhita Regragui, Microcalcifications Detection in Mammographic Image Using Texture Coding. Applied Mathematical Sciences, Vol.5, 2011, no. 8, 381-393
  • M.Hanifi, F.Sedes, D.Aboutajdine, A.Lasfar. A new approach for coding Satellite. Images : Rank coding. International Journal of Computational Science. , 2009, Vol. 3, No. 1
  • G.Lohmann. Analysis and Synthesis of Textures: a Co-occurrence-Based Approach. Computer and Graphics, 1995, Vol. 19, No. 1, 29:36 J.Parkkinen, T.J. Selkainaho, Detecting texture periodicity from the Co-occurrence matrix. Pattern Recognition Letters, 1990, Vol. 11, 43:50.