6th International Workshop on Breast Densitometry and Breast ...

6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry
and Breast Cancer Risk Assessment
P32 APPLICATION OF SHAPE AND APPEARANCE MODEL APPROACH TO
MAMMOGRAMS
Serghei Malkov 1 , Karla Kerlikowske 2 John A. Shepherd 1
1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 1 Irving
Street, Suite AC108, San Francisco, CA 94143-0628 ; 2 Departments of Medicine and
Epidemiology/Biostatistics, University of California, San Francisco, 1635 Divisadero Street, Suite 600,
San Francisco, CA 94115
Abstract: The extraction of new information from clinical breast mammograms could help to improve
breast cancer risk assessment techniques. The image shape and texture estimation (appearance) designed
for face recognition seems to be promising approach for application in breast imaging.
Purpose: The purpose of this study is to apply a shape and appearance model approach to digital
mammograms. Potentially, the breast shape and appearance model parameters could give new information
valuable for breast density estimation, breast cancer risk assessment and diagnostics.
ABSTRACTS
Materials and Methods: We built a shape and appearance model using 200 full field digital
mammograms with calculated percent fibroglandular volumes (%FGV) from San Francisco
Mammography Registry. The model approach was implemented according to the method of Cootes et al
[1]. To build the shape model, we used the edge and grid point coordinates inside of the breast area. A
Procrustes transformation was used to remove rotation, translation and scale. A Principal Component
Analysis (PCA) was applied to extract significant and uncorrelated components. In order to build an
appearance model, first, we transformed the breast images into the mean texture image by piecewise
linear image transformation. That step aligned all texture information inside the reference mean image.
Then, the image pixels grey-scale values are converted into a set of significant principal component
vectors. Finally, the shape and texture feature sets of each image are created using principal component
vectors. In addition, to remove shape and texture correlations another PCA transform was applied to get a
combined shape-appearance model. The stepwise regression with forward selection and backward
elimination was used to estimate the outcome %FGV with shape and appearance features and other
parameters including age, bmi, thickness, mAs and force.
Results: We obtained the maps and pixel distributions of different shape and appearance PCA
components. The shape and appearance scores are found to correlate moderately to breast %FGV, dense
tissue and actual volumes, bmi and age. The highest Pearson correlation coefficient is equal 0.76 for the
first shape PCA component and actual breast volume. The stepwise regression of the outcome %FGV
from shape and appearance variables, thickness, age, mAs and force, met the 0.01 significance level for
entry into the model during forward selection, demonstrated R-square around 0.8. Eleven significant
variables were selected by regression among 185 parameters.
Conclusion: We applied the shape and appearance model approach to mammograms and demonstrated its
feasibility to extract a new set of variables. Further exploring and testing of this approach is necessary.
References:
T.F. Cootes, G.J Edwards, and C,J. Taylor "Active Appearance Models", IEEE Transactions on Pattern
Analysis and Machine Intelligence 2001
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