Abstract

A system, method, and computer program product for computer-aided detection of suspicious lesions in digital mammograms is described, wherein single-view feature vectors from a first digital mammogram are processed in a classification algorithm along with information computed from a plurality of related digital mammograms to assign an overall probability of suspiciousness to potentially suspicious lesions in the first digital mammogram. In one preferred embodiment, a greater probability of suspiciousness is determined where there are similar corresponding lesions in the first digital mammogram and in an alternate digital mammogram view of the same breast. In another preferred embodiment, a lesser probability of suspiciousness is found where there are symmetric lesions or structures located in the first digital mammogram and a digital mammogram of the opposite breast. In another preferred embodiment, a lesser probability of suspiciousness is found where there are similar lesions or structures located in the first digital mammogram and a digital mammogram of the same breast taken months or years earlier in time. In another preferred embodiment, the nipple location, which serves as a reference location point across different digital mammogram, is located using an algorithm that takes into account the chest wall, the skin line of the breast, and the general orientation of the fibrous breast tissue in the digital mammogram relative to the chest wall.

Other References

• Baker et al., 1996, "Artificial Neural Network: Improving the Quality of Breast Biopsy Recommendations," Radiology 198:131-135
• Bick et al., 1995, "A New Single-Image Method for Computer-Aided Detection of Small Mammographic Masses,"In: Computer Assisted Radiology: Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy, Lemke et al., eds. CAR'95 Berlin, Jun. 21-24, 1995
• Doi et al., 1995, "Potential Usefulness of Digital Imaging in Clinical Diagnostic Radiology: Computer Aided Diagnosis," Journal of Digital Imaging 8(1):2-7
• Feig and Yaffe, 1995, "Digital Mammography, Computer-Aided Diagnosis, and Telemammography," The Radiologic Clinics of North America: Breast Imaging 33(6):1205-1230
• Floyd et al., 1994, "Prediction of Breast Cancer Malignancy Using an Artificial Neural Network," Cancer 74(11):2944-2948
• Frankel et al., 1995 "Initial Versus Subsequent Screening Mammography: Comparison of Findings and Their Prognostic Significance," AJR:164: 1107-1109
• Giger et al., 1993, "An `Intelligent` Workstation for Computer-aided Diagnosis," Radiographics 13(3):647-656
• Gurney, 1994, "Neural Networks at the Crossroads: Caution Ahead," Radiology 193:27-30
• Ikeda and Sickles, 1988, "Second-screening Mammography: One versus Two Views per Breast," Radiology 168: 651-656
• Karssemeijer, 1994, "Recognition of stellate lesions in digital mammograms,"In :Digital Mammography, Gale et al., eds., pp. 211-219
• Karssemeijer, 1995, "Detection of stellat distortions in mammograms using scale space operators," In : Information Processing in Medical Imaging, Biszais et al., Kluwer Academic Publishers, Netherlands, pp. 335-346
• Katsuragawa, 1990, "Image feature analysis and computer-aided diagnosis in digital radiography: Effect of digital parameters on the accuracy of computerized analysis of interstitial disease in digital chest radiographs," Med. Phys. 17(1):72-78
• Kegelmeyer et al., 1994, "Computer-aided Mammographic Screening for Spriculated Lesions," Radiology 1991:331-337
• Lin et al., "Application of Neural Networks for Improvement of Lung Nodule Detection in Digital Chest Radiographs," pp. IV-20-IV-23
• Matsubara et al., "Development of a New Algorithm For Detection of Mammographic Masses"
• Miller and Astley, 1993, "Automated Detection of Mammographic Asymmetry Using Anatomical Features," International Journal of Pattern Recognition and Artificial Intelligence 7 (6): 1461-1476
• Miller and Astley, 1993, "Detection of breast asymmetry using anatomical features," SPIE 1905:433-442
• Nishikawa et al., "Computer-aided Detection and Diagnosis of Masses and Clustered Microcalcifications from Digital Mammograms," In: State of the Art in Digital Mammographic Image Analysis, Bowyer and Astley, eds. World Scientific Publishing Co., 1993
• Schmidt et al., "Computer-aided Diagnosis in Mammography," RSNA Categorical Course in Breast Imaging 1995; pp. 199-208
• Specht, 1990, "Probabilistic Neural Networks," Neural Networks 3:109-118
• Sprecht, "Enhancements to Probabilistic Neural Networks," Proceedings of the IEEE International Joint Conference on Neural Networks, Baltimore, MD. Jun. 7-11, 1992
• Sprecht and Romsdahl, "Experience with Adaptive Probabilistic Neural networks and Adaptive General Regression Neural Networks," IEEE International Conference on Neural Networks, Orlando, Florida. Jun. 28 to Jul. 1994
• Tahoces et al., 1995, "Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns," Phys. Med. Biol. 40:103-117
• te Brake and Karssemeijer, 1996, "Detection of Stellate Breast Abnormalities," In: Digital Mammography '96, Doi et al., eds. Elsevier Science B. V. pp. 341-346
• Thurfjell et al., 1988, "Sensitivity and Specificity of Computer-Assisted Breast Cancer Detection in Mammongraphy Screening," Acta Radiologica 39:384-388
• Wei et al., 1995, "Classification of mass and normal breast tissue on digital mammograms: Multiresolution texture analysis," Med. Phys. 22:(9):1501-1513
• Wu et al., 1993, "Artificial Neural Networks in Mammography: Application to Decision Making in the Diagnosis of Breast Cancer," Radiology 187:81-87
• Yin et al., 1993, "Comparison of Bilateral-Subtraction and Single-Image Processing Techniques in the Computerized Detection of Mammographic Masses," Investigative Radiology 28:473-481
• Yin et al., 1991, "Computerized detection of masses in digital mammograms: Analysis of bilateral subtraction images," Med. Phys. 18(5):955-963
• Yoshimura et al., 1992, "Computerized Scheme for the Detection of Pulmonary Nodules: A Nonlinear Filtering Technique," Invest. Radiol. 27:124-129
• Zhang and Giger, 1995, "Automated detection of spiculated lesions and architectural distortions of digitized mammograms," SPIE 2434:846-85