Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

Wenda He; Arne Juette; Erica R. E. Denton; Reyer Zwiggelaar

doi:10.1117/12.2081815

20 March 2015 Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

Wenda He, Arne Juette, Erica R. E. Denton, Reyer Zwiggelaar

Author Affiliations +

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94133I (2015) https://doi.org/10.1117/12.2081815
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

Citation Download Citation

Wenda He, Arne Juette, Erica R. E. Denton, and Reyer Zwiggelaar "Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133I (20 March 2015); https://doi.org/10.1117/12.2081815

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