Journal of Digital Imaging

, Volume 23, Issue 5, pp 547–553 | Cite as

Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms

  • Rangaraj M. Rangayyan
  • Thanh M. Nguyen
  • Fábio J. Ayres
  • Asoke K. Nandi


The effect of pixel resolution on texture features computed using the gray-level co-occurrence matrix (GLCM) was analyzed in the task of discriminating mammographic breast lesions as benign masses or malignant tumors. Regions in mammograms related to 111 breast masses, including 65 benign masses and 46 malignant tumors, were analyzed at pixel sizes of 50, 100, 200, 400, 600, 800, and 1,000 μm. Classification experiments using each texture feature individually provided accuracy, in terms of the area under the receiver operating characteristics curve (AUC), of up to 0.72. Using the Bayesian classifier and the leave-one-out method, the AUC obtained was in the range 0.73 to 0.75 for the pixel resolutions of 200 to 800 μm, with 14 GLCM-based texture features using adaptive ribbons of pixels around the boundaries of the masses. Texture features computed using the ribbons resulted in higher classification accuracy than the same features computed using the corresponding regions within the mass boundaries. The t test was applied to AUC values obtained using 100 repetitions of random splitting of the texture features from the ribbons of masses into the training and testing sets. The texture features computed with the pixel size of 200 μm provided the highest average AUC with statistically highly significant differences as compared to all of the other pixel sizes tested, except 100 μm.

Key words

Breast cancer breast masses Haralick's texture features mammography margins of masses pixel size pixel resolution ribbon around a mass texture analysis texture features tumor classification digital image processing image analysis mammography 



This project was funded by grants from the Canadian Breast Cancer Foundation: Prairies/NWT Chapter, the Alberta Heritage Foundation for Medical Research (AHFMR), the Natural Sciences and Engineering Research Council (NSERC) of Canada, and Research Services Office of the University of Calgary. This project was also supported by the Distinguished International Research Fellowship of the Schulich School of Engineering, University of Calgary, awarded to A. K. Nandi.


  1. 1.
    Haralick RM, Shanmugam K, Dinstein I: Textural features for image classification. IEEE T Syst Man Cyb 3(6):610–621, 1973CrossRefGoogle Scholar
  2. 2.
    Sivaramakrishna R, Powel KA, Lieber ML, Chilcote WA, Shekhar R: Texture analysis of lesions in breast ultrasound images. Comput Med Imag Grap 26:303–307, 2002CrossRefGoogle Scholar
  3. 3.
    Bovis K, Singh S: Detection of masses in mammograms using texture features. Proceedings of the 15th International Conference on Pattern Recognition; Sept 3–7, 2:267–270, 2000Google Scholar
  4. 4.
    Gupta S, Markey MK: Correspondence in texture features between two mammographic views. Med Phys 36(6):1598–1606, 2005CrossRefGoogle Scholar
  5. 5.
    Lee GN, Hara T, Fujita H: Classifying masses as benign or malignant based on co-occurrence matrix textures: a comparison study of different gray level quantizations. In: Astley SM, et al Eds. International Workshop on Digital Mammography. Manchester, UK, LNCS 4046, 2006, pp 332–339Google Scholar
  6. 6.
    Alto H, Rangayyan RM, Desautels JEL: Content-based retrieval and analysis of mammographic masses. J Electron Imaging 14(2):023016, 2005, 1–17CrossRefGoogle Scholar
  7. 7.
    Mudigonda NR, Rangayyan RM, Desautels JEL: Gradient and texture analysis for the classification of mammographic masses. IEEE T Med Imaging 19:1032–1043, 2000CrossRefGoogle Scholar
  8. 8.
    Mudigonda NR, Rangayyan RM, Desautels JEL: Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE T Med Imaging 20:1215–1227, 2001CrossRefGoogle Scholar
  9. 9.
    Duda RO, Hart PE, Stork DG: Pattern Classification, 2nd edition. New York: Wiley, 2001Google Scholar
  10. 10.
    Metz CE: Basic principles of ROC analysis. Semin Nucl Med 8:283–298, 1978CrossRefPubMedGoogle Scholar
  11. 11.
    Sahiner BS, Chan HP, Petrick N, Helvie MA, Goodsitt MM: Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. Med Phys 25:516–526, 1998CrossRefPubMedGoogle Scholar
  12. 12.
    Sahiner BS, Chan HP, Petrick N, Helvie MA, Hadjiiski LM: Improvement of mammographic mass characterization using spiculation measures and morphological features. Med Phys 28(7):1455–1465, 2001CrossRefPubMedGoogle Scholar
  13. 13.
    Rangayyan RM, Nguyen TM, Ayres FJ, Nandi AK: Analysis of the effect of spatial resolution on texture features in the classification of breast masses in mammograms. Proc. Computer-assisted Radiology and Surgery, Berlin, Germany, June 2007. Springer, pp 334–336Google Scholar
  14. 14.
    Mu T, Nandi AK, Rangayyan RM: Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers. J Digital Imaging 21(2):153–169, 2008CrossRefGoogle Scholar
  15. 15.
    Mu T, Nandi AK, Rangayyan RM: Classification of breast masses via nonlinear transformation of features based on a kernel matrix. Med Biol Eng Comput 45(8):769–780, 2007CrossRefPubMedGoogle Scholar
  16. 16.
    Nandi RJ, Nandi AK, Rangayyan RM, Scutt D: Classification of breast masses in mammograms using genetic programming and feature selection. Med Biol Eng Comput 44(8):683–694, 2006CrossRefPubMedGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2009

Authors and Affiliations

  • Rangaraj M. Rangayyan
    • 1
  • Thanh M. Nguyen
    • 1
  • Fábio J. Ayres
    • 1
  • Asoke K. Nandi
    • 2
  1. 1.Department of Electrical and Computer Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Department of Electrical Engineering and ElectronicsThe University of LiverpoolLiverpoolUK

Personalised recommendations