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
Article

Abstract

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 

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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

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