This work presents the usefulness of texture features in the classification of breast lesions in 5518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.
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Acknowledgments
This work was supported by FAPESP—The State of São Paulo Foundation Research. We thank all researchers of the Kurt Rossmann Laboratories for Radiologic Image Research at the University of Chicago who contributed to this work, Mr. L. F. Oliveira for improving the algorithms, and Mrs. E. Lanzl for improving the manuscript.
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Pereira, R.R., Azevedo Marques, P.M., Honda, M.O. et al. Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms. J Digit Imaging 20, 248–255 (2007). https://doi.org/10.1007/s10278-006-9945-8
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DOI: https://doi.org/10.1007/s10278-006-9945-8