12 May 2004 Multimodality CAD: combination of computerized classification techniques based on mammograms and 3D ultrasound volumes for improved accuracy in breast mass characterization
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Abstract
Mammography and ultrasound (US) are two low-cost modalities that are commonly used by radiologists for evaluating breast masses and making biopsy recommendations. The goal of this study was to investigate computerized methods for combining information from these two modalities for mass characterization. Our data set consisted of 3D US images and mammograms of biopsy-proven solid breast masses from 60 patients. Thirty of the masses were malignant and 30 were benign. The US volume was obtained by scanning with an experimental 3D US image acquisition system. After computerized feature extraction from the 3D US images and mammograms, we investigated three methods (A, B and C) for combining the image features or classifier scores from different mammographic views and the US volumes. The classifier scores were analyzed using the receiver operating characteristic (ROC) methodology. The area Az under the ROC curve of the classifier based on US alone was 0.88±0.04 for testing Two classifiers were designed using the mammograms alone, with test Az values of 0.85±0.05 and 0.87±0.05, respectively. The test accuracy of combination methods A, B, and C were 0.89±0.04, 0.92±0.03, and 0.93±0.03, respectively. Our results indicate that combining the image features or classifier scores from the US and mammographic classification methods can improve the accuracy of computerized mass characterization.
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Berkman Sahiner, Berkman Sahiner, Heang-Ping Chan, Heang-Ping Chan, Lubomir M. Hadjiiski, Lubomir M. Hadjiiski, Marilyn A. Roubidoux, Marilyn A. Roubidoux, Chintana Paramagul, Chintana Paramagul, Mark A. Helvie, Mark A. Helvie, Chuan Zhou, Chuan Zhou, } "Multimodality CAD: combination of computerized classification techniques based on mammograms and 3D ultrasound volumes for improved accuracy in breast mass characterization", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535868; https://doi.org/10.1117/12.535868
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