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14 March 2011Automatic detection of regions of interest in mammographic images
This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the
underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in
development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated
machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic
image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with
Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used
for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were
generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate
ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for
elimination of false positives. The strategies differed in their approach to combining confidence scores by summation,
averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with
annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation
strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of
maximum score showed the best performance with 96% detection rate.
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Erkang Cheng, Haibin Ling, Predrag R. Bakic, Andrew D. A. Maidment, Vasileios Megalooikonomou, "Automatic detection of regions of interest in mammographic images," Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623J (14 March 2011); https://doi.org/10.1117/12.877667