Automatic detection of regions of interest in mammographic images

Erkang Cheng; Haibin Ling; Predrag R. Bakic; Andrew D. A. Maidment; Vasileios Megalooikonomou

doi:10.1117/12.877667

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14 March 2011 Automatic detection of regions of interest in mammographic images

Erkang Cheng; Haibin Ling; Predrag R. Bakic; Andrew D. A. Maidment; Vasileios Megalooikonomou

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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79623J (2011) https://doi.org/10.1117/12.877667
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

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, and 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

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