A Sparse-modeled ROI for GLAM Construction in Image Classification Problems—A Case Study of Breast Cancer

  • K. Karteeka PavanEmail author
  • Ch. Srinivasa Rao
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Image segmentation is a process to determine regions of interest (ROI) in mammograms. Mammograms can be classified by extracting textural features of ROI using Gray Level Aura Matrices (GLAM). Scientists are selecting a fixed window size for all ROIs to find respective GLAM, though the masses will not occur in regular two-dimensional geometries. This paper makes an attempt to replicate the problem but by choosing arbitrary shape of masses as they occur. It is found that this kind of natural selection of the arbitrary shape yielded drastic reduction in time complexity by adopting the method suggested by us.


Classification Mammogram Texture GLAM Segmentation ROI 



This work is funded by Department of Science and Technology, New Delhi, India.


  1. 1.
    Winsberg F, Elkin M, Macy J, Bordaz V, Weymouth W (1967) Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology 89:211–215CrossRefGoogle Scholar
  2. 2.
    Bozek J, Mustra M, Delac K, Grgic M (2009) A survey of image processing algorithms in digital mammography. In: Grgic et al (eds) Recent advances in multimedia signal processing and communications, SCI 231, pp 631–657Google Scholar
  3. 3.
    Pisano ED, Cole EB, Hemminger BM, Yaffe MJ, Aylward SR, Maidment ADA, Eugene Johnston R, Williams MB, Niklason LT, Conant EF, Fajardo LL, Kopans DB, Brown ME, Pizer SM (2000) Image processing algorithms for digital mammography: a pictorial essay. J Radiogr 20(5):400–420Google Scholar
  4. 4.
    Mohanty AK, Senapati MR, Lenka SK (2013) A novel image mining technique for classification of mammograms using hybrid feature selection. Neural Comput Appl 22:1151–1161Google Scholar
  5. 5.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621Google Scholar
  6. 6.
    Wiesmuller S, Chandy DA (2010) Content based mammogram retrieval using Gray Level Aura Matrix. Int J Comput Commun Inf Syst (IJCCIS) 2(1):217–223Google Scholar
  7. 7.
    Qin X, Yang Y-H (2004) Similarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition (CVPR’04), vol 4. IEEEGoogle Scholar
  8. 8.
    Chang HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN (2006) Approaches for automated detection and classification of masses in mammograms. Pattern Recogn 39:646–668Google Scholar
  9. 9.
    Ke L, Mu N, Kang Y (2010) Mass computer-aided diagnosis method in mammogram based on texture features. In: Biomedical engineering and informatics (BMEI), 3rd international conference. IEEE explore, pp 146–149Google Scholar
  10. 10.
    Jalja K, Bhagvati C, Deekshatulu BL, Pujari AK (2005) Texture element feature characterizations for CBIR. In: Proceedings of geoscience and remote sensing symposium (IGARSS ’05), vol 2Google Scholar
  11. 11.
    Choraś RS (2008) Feature extraction for classification and retrieval mammogram in databases. Int J Med Eng Inf 1(1):50–61Google Scholar
  12. 12.
    Khuzi AM, Besar R, Wan Zaki WMD (2008) Texture features selection for masses detection in digital mammogram. In: 4th Kuala Lumpur international conference on biomedical engineering, IFMBE proceedings, vol 21. part 3, part 8, pp 629–632Google Scholar
  13. 13.
    Haliche Zohra, Hammouche Kamal (2011) The gray level aura matrices for textured image segmentation. Analog Integr Circ Sig Process 69:29–38CrossRefGoogle Scholar
  14. 14.
    Chandy DA, Johnson JS, Selvan SE (2014) Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval. Multimed Tools Appl 72(2):2011–2024Google Scholar
  15. 15.
    Mohanty AK, Senapati MR, Beberta S, Lenka SK (2013) Texture-based features for classification of mammograms using decision tree. Neural Comput Appl 23:1011–1017Google Scholar
  16. 16.
    Mohanty AK, Senapati MR, Lenka SR (2013) An improved data mining technique for classification and detection of breast cancer from mammograms. Neural Comput Appl 22(1):303–310Google Scholar
  17. 17.
    Hussain M (2014) False-positive reduction in mammography using multiscale spatial Weber law descriptor and support vector machines. Neural Comput Appl 25(1):83–93Google Scholar
  18. 18.
    Mohanty AK, Senapati MR, Beberta S, Lenka SK (2013) Mass classification method in mammograms using correlated association rule mining. Neural Comput Appl 23:273–281Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  1. 1.R.V.R. and J.C. College of EngineeringGunturIndia

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