Classification of masses in digitized mammograms with features in the wavelet transform domain

John B. Weaver; Dennis M. Healy Jr.; Helene Nagy M.D.; Steven P. Poplack; Jian Lu; Tracy Sauerland; David Langdon

doi:10.1117/12.170069

15 March 1994 Classification of masses in digitized mammograms with features in the wavelet transform domain

John B. Weaver, Dennis M. Healy Jr., Helene Nagy M.D., Steven P. Poplack, Jian Lu, Tracy Sauerland, David Langdon

Proceedings Volume 2242, Wavelet Applications; (1994) https://doi.org/10.1117/12.170069
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

One of the primary visual properties used by radiologists in classifying masses is the sharpness of the edge of the mass. Wavelet transforms can be thought of as multiscale edge detectors. We report using the edge detection and classification properties of wavelet transforms to help classify masses on mammograms. We digitized six masses from mammograms: three benign and three malignant. Our preliminary results indicate that edge properties of masses in mammograms can be obtained from features in the wavelet transform domain. These edge properties can be used to help classify masses prior to biopsy. In particular, the change in the direction of the edge gradient at intermediate scales is indicative of malignancy. This work must be extended to a much larger sample size. The larger sample size will allow other measures to be used. More importantly the interaction between measures can then be observed. Undoubtedly a combination of measures will be required to classify masses accurately.

Citation Download Citation

John B. Weaver, Dennis M. Healy Jr., Helene Nagy M.D., Steven P. Poplack, Jian Lu, Tracy Sauerland, and David Langdon "Classification of masses in digitized mammograms with features in the wavelet transform domain", Proc. SPIE 2242, Wavelet Applications, (15 March 1994); https://doi.org/10.1117/12.170069

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