PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses. MATERIALS AND METHODS: Digitized mammograms were processed with an adaptive enhancement filter followed by a local border refinement stage. Features were then extracted from each detected structure and used to identify potential masses. The performance of the algorithm was evaluated in independent cases obtained from 263 patients from two institutions. Each case contained one or more pathologically proved breast masses. Contralateral mammograms obtained in the same patients that did not contain a visible lesion were used to estimate the CAD marker rate for the algorithm. The tradeoff between detection sensitivity and the number of CAD marks was analyzed in this study. RESULTS: Malignant masses were detected with the computer in 87% (135 of 156), 83% (130 of 156), and 77% (120 of 156) of the malignant cases at CAD marker rates of 1.5, 1.0, and 0.5 marks per mammogram, respectively. The difference between malignant mass-detection performance in subsets of cases collected at each institution was found to be less than 1%. The detection accuracy for benign masses was lower than that for malignant masses. CONCLUSION: This mass-detection algorithm had a high sensitivity for detection of malignant masses. It may be useful as a second opinion in mammographic interpretation.
PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses. MATERIALS AND METHODS: Digitized mammograms were processed with an adaptive enhancement filter followed by a local border refinement stage. Features were then extracted from each detected structure and used to identify potential masses. The performance of the algorithm was evaluated in independent cases obtained from 263 patients from two institutions. Each case contained one or more pathologically proved breast masses. Contralateral mammograms obtained in the same patients that did not contain a visible lesion were used to estimate the CAD marker rate for the algorithm. The tradeoff between detection sensitivity and the number of CAD marks was analyzed in this study. RESULTS: Malignant masses were detected with the computer in 87% (135 of 156), 83% (130 of 156), and 77% (120 of 156) of the malignant cases at CAD marker rates of 1.5, 1.0, and 0.5 marks per mammogram, respectively. The difference between malignant mass-detection performance in subsets of cases collected at each institution was found to be less than 1%. The detection accuracy for benign masses was lower than that for malignant masses. CONCLUSION: This mass-detection algorithm had a high sensitivity for detection of malignant masses. It may be useful as a second opinion in mammographic interpretation.
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: 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: Jun Wei; Lubomir M Hadjiiski; Berkman Sahiner; Heang-Ping Chan; Jun Ge; Marilyn A Roubidoux; Mark A Helvie; Chuan Zhou; Yi-Ta Wu; Chintana Paramagul; Yiheng Zhang Journal: Acad Radiol Date: 2007-06 Impact factor: 3.173