Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms
- 202 Downloads
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 wordsBreast 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.
- 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
- 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
- 9.Duda RO, Hart PE, Stork DG: Pattern Classification, 2nd edition. New York: Wiley, 2001Google Scholar
- 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