PURPOSE: The purpose of this study is to develop a computer-aided detection (CAD) system that combined a dual system approach with a two-view fusion method to improve the accuracy of mass detection on mammograms. METHODS: The authors previously developed a dual CAD system that merged the decision from two mass detection systems in parallel, one trained with average masses and another trained with subtle masses, to improve sensitivity without excessively increasing false positives (FPs). In this study, they further designed a two-view fusion method to combine the information from different mammographic views. Mass candidates detected independently by the dual system on the two-view mammograms were first identified as potential pairs based on a regional registration technique. A similarity measure was designed to differentiate TP-TP pairs from other pairs (TP-FP and FP-FP pairs) using paired morphological features, Hessian feature, and texture features. A two-view fusion score for each object was generated by weighting the similarity measure with the cross correlation measure of the object pair. Finally, a linear discriminant analysis classifier was trained to combine the mass likelihood score of the object from the single-view dual system and the two-view fusion score for classification of masses and FPs. A total of 2332 mammograms from 735 subjects including 800 normal mammograms from 200 normal subjects was collected with Institutional Review Board (IRB) approval. RESULTS: When the single-view CAD system that was trained with average masses only were applied to the test sets, the average case-based sensitivities were 50.6% and 63.6% for average masses on current mammograms and 22.6% and 36.2% for subtle masses on prior mammograms at 0.5 and 1 FPs/image, respectively. With the new two-view dual system approach, the average case-based sensitivities were improved to 67.4% and 83.7% for average masses and 44.8% and 57.0% for subtle masses at the same FP rates. CONCLUSIONS: The improvement with the proposed method was found to be statistically significant (p<0.0001) by JAFROC analysis.
PURPOSE: The purpose of this study is to develop a computer-aided detection (CAD) system that combined a dual system approach with a two-view fusion method to improve the accuracy of mass detection on mammograms. METHODS: The authors previously developed a dual CAD system that merged the decision from two mass detection systems in parallel, one trained with average masses and another trained with subtle masses, to improve sensitivity without excessively increasing false positives (FPs). In this study, they further designed a two-view fusion method to combine the information from different mammographic views. Mass candidates detected independently by the dual system on the two-view mammograms were first identified as potential pairs based on a regional registration technique. A similarity measure was designed to differentiate TP-TP pairs from other pairs (TP-FP and FP-FP pairs) using paired morphological features, Hessian feature, and texture features. A two-view fusion score for each object was generated by weighting the similarity measure with the cross correlation measure of the object pair. Finally, a linear discriminant analysis classifier was trained to combine the mass likelihood score of the object from the single-view dual system and the two-view fusion score for classification of masses and FPs. A total of 2332 mammograms from 735 subjects including 800 normal mammograms from 200 normal subjects was collected with Institutional Review Board (IRB) approval. RESULTS: When the single-view CAD system that was trained with average masses only were applied to the test sets, the average case-based sensitivities were 50.6% and 63.6% for average masses on current mammograms and 22.6% and 36.2% for subtle masses on prior mammograms at 0.5 and 1 FPs/image, respectively. With the new two-view dual system approach, the average case-based sensitivities were improved to 67.4% and 83.7% for average masses and 44.8% and 57.0% for subtle masses at the same FP rates. CONCLUSIONS: The improvement with the proposed method was found to be statistically significant (p<0.0001) by JAFROC analysis.
Authors: Chuan Zhou; Heang-Ping Chan; Chintana Paramagul; Marilyn A Roubidoux; Berkman Sahiner; Labomir M Hadjiiski; Nicholas Petrick Journal: Med Phys Date: 2004-10 Impact factor: 4.071
Authors: Jun Wei; Berkman Sahiner; Lubomir M Hadjiiski; Heang-Ping Chan; Nicholas Petrick; Mark A Helvie; Marilyn A Roubidoux; Jun Ge; Chuan Zhou Journal: Med Phys Date: 2005-09 Impact factor: 4.071
Authors: Berkman Sahiner; Heang-Ping Chan; Lubomir M Hadjiiski; Mark A Helvie; Chinatana Paramagul; Jun Ge; Jun Wei; Chuan Zhou Journal: Med Phys Date: 2006-07 Impact factor: 4.071
Authors: Jun Wei; Heang-Ping Chan; Berkman Sahiner; Lubomir M Hadjiiski; Mark A Helvie; Marilyn A Roubidoux; Chuan Zhou; Jun Ge Journal: Med Phys Date: 2006-11 Impact factor: 4.071
Authors: Bin Zheng; Joseph K Leader; Gordon S Abrams; Amy H Lu; Luisa P Wallace; Glenn S Maitz; David Gur Journal: Med Phys Date: 2006-09 Impact factor: 4.071
Authors: Jun Wei; Heang-Ping Chan; Chuan Zhou; Yi-Ta Wu; Berkman Sahiner; Lubomir M Hadjiiski; Marilyn A Roubidoux; Mark A Helvie Journal: Med Phys Date: 2011-04 Impact factor: 4.071