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Effect of pixel resolution on texture features of breast masses in mammograms.

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...

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Published in:Journal of Digital Imaging Vol. 23; no. 5; pp. 547 - 554
Main Authors: Rangayyan RM, Nguyen TM, Ayres FJ, Nandi AK
Format: diagnostic images research tables/charts Journal Article
Published: Springer Nature Oct2010
Online Access:View this record in EBSCOhost
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      jtl: Journal of Digital Imaging
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      dt: Oct2010
      vid: 23
      iid: 5
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-009-9238-0
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      tig:
        atl: Effect of pixel resolution on texture features of breast masses in mammograms.
      aug:
        au:
          Rangayyan RM
          Nguyen TM
          Ayres FJ
          Nandi AK
        affil: Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW Calgary Canada T2N 1N4; ranga@ucalgary.ca
      sug:
        subj:
          Mammography
          Breast Neoplasms Diagnosis
          Breast Diseases Diagnosis
          Radiographic Image Enhancement
          Breast Pathology
          Breast Neoplasms Classification
          Human
          T-Tests
          ROC Curve
          Evaluation Research
          Funding Source
      ab: 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.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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