Near-automatic quantification of breast tissue glandularity via digitized mammograms

Punam K. Saha; Jayaram K. Udupa; Emily F. Conant; Dev Prasad Chakraborty

doi:10.1117/12.348581

21 May 1999 Near-automatic quantification of breast tissue glandularity via digitized mammograms

Punam K. Saha; Jayaram K. Udupa; Emily F. Conant; Dev Prasad Chakraborty

Author Affiliations +

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348581
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

Studies reported in the literature indicate that breast cancer risk is associated with mammographic densities. Although, an objective, repeatable quantitative measure of risk derived from mammographic densities will be of great use in recommending alternative screening paradigms and/or preventive measures, image processing efforts toward this goal seem to very sparse in the literature, and automatic and efficient methods do not seem to exist. In this paper, we describe and validate an automatic and reproducible method to segment glandular tissue regions from fat within breasts from digitized mammograms using scale-based fuzzy connectivity methods. Different measures for characterizing density are computed from the segmented regions and their accuracies in terms of their linear correlation across two different projections (CC and MLO) are studied. It is shown that quantization of glandularity taking into account the original intensities is more accurate than just considering the segmented areas. This makes the quantification less dependent on the shape of the glandular regions and the angle of projection. A simple phantom experiment is done that supports this observation.

Citation Download Citation

Punam K. Saha, Punam K. Saha, Jayaram K. Udupa, Jayaram K. Udupa, Emily F. Conant, Emily F. Conant, Dev Prasad Chakraborty, Dev Prasad Chakraborty, } "Near-automatic quantification of breast tissue glandularity via digitized mammograms", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348581; https://doi.org/10.1117/12.348581

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