Automatic Segmentation of Mammographic Density

Sivaramakrishna, Radhika; Obuchowski, Nancy A.; Chilcote, William A.; Powell, Kimerly A.

doi:10.1016/S1076-6332(03)80534-2

« Previous Next »Academic Radiology
Volume 8, Issue 3 , Pages 250-256, March 2001

Automatic Segmentation of Mammographic Density

Radhika Sivaramakrishna, PhD
- Agnes Christine Roberts Breast Imaging Laboratory, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Agnes Christine Roberts Breast Imaging Laboratory, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195
Nancy A. Obuchowski, PhD
- Department of Biostatistics and Epidemiology, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195, USA
William A. Chilcote, MD
- Division of Radiology, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195, USA
Kimerly A. Powell, PhD
- Agnes Christine Roberts Breast Imaging Laboratory, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195, USA

Received 6 September 2000; received in revised form 9 November 2000; accepted 9 November 2000.

Abstract

Rationale and Objectives

The purpose of this study was to evaluate a completely automatic method, based on Kittler's optimal threshold, to estimate breast density by using the mammographers' definition.

Materials and Methods

Thirty-two normal, right-craniocaudal-view mammograms of women aged 37–86 years were digitized. The whole breast area was segmented by using Kittler's optimal threshold procedure, and the dense portions were then segmented by using a modified version of Kittler's method. Segmentation results were validated by three independent mammographers who provided a signed percentage (in steps of 5%) to indicate the difference between their own visual estimation of the dense portions and the results obtained with the algorithm. The difference between the algorithm measurements and the mammographers' measurements was compared to the interobserver differences.

Results

A high correlation was found between the algorithm measured density and the mammographers' measurements. Spearman correlations ranged from 0.92 to 0.95 (P < .001). Algorithm-measured density differed from the mammographers' measurements by an average of 6.9% (ie, average of the absolute differences). In contrast, mammographers' measurements differed between themselves by an average of 5.4%.

Conclusion

The difference between density as measured with the algorithm and as measured by the mammographers is similar to the differences observed between mammographers. This algorithm could be useful in providing clinically accurate estimates of breast density.

Keywords: Breast cancer, breast density, Kittler's optimal threshold