4 Computer-Aided Detection and Diagnosis in Mammography

Mehul P. Sampat, Mia K. Markey, and Alan C. Bovik The University of Texas at Austin

[1]  Dev P Chakraborty,et al.  Observer studies involving detection and localization: modeling, analysis, and validation. , 2004, Medical physics.

[2]  N. Karssemeijer,et al.  A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. , 2004, Medical physics.

[3]  Dar-Ren Chen,et al.  Watershed segmentation for breast tumor in 2-D sonography. , 2004, Ultrasound in medicine & biology.

[4]  R. F. Wagner,et al.  Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium. , 2004, Academic radiology.

[5]  Lubomir M. Hadjiiski,et al.  Computerized characterization of breast masses on three-dimensional ultrasound volumes. , 2004, Medical physics.

[6]  Peter Aspelin,et al.  Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast , 2004, European Radiology.

[7]  M. Kallergi.  Computer-aided diagnosis of mammographic microcalcification clusters. , 2004, Medical physics.

[8]  Sheng-Fang Huang,et al.  Characterization of spiculation on ultrasound lesions , 2004, IEEE Trans. Medical Imaging.

[9]  Darrin C. Edwards,et al.  Estimating three-class ideal observer decision variables for computerized detection and classification of mammographic mass lesions. , 2003, Medical physics.

[10]  J. Baker,et al.  Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. , 2003, AJR. American journal of roentgenology.

[11]  J.M. Reid,et al.  Computer-aided classification of breast masses in ultrasonic B-scans using a multiparameter approach , 2003, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[12]  M Thelen,et al.  Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography. , 2003, Medical physics.

[13]  Rene Vargas-Voracek,et al.  Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information. , 2003, Medical physics.

[14]  B. Goldberg,et al.  Classification of breast masses in ultrasonic B scans using Nakagami and K distributions , 2003, Physics in medicine and biology.

[15]  David Gur,et al.  Prevalence effect in a laboratory environment. , 2003, Radiology.

[16]  Ruey-Feng Chang,et al.  Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis. , 2003, Ultrasound in medicine & biology.

[17]  L. Turnbull,et al.  Textural analysis of contrast‐enhanced MR images of the breast , 2003, Magnetic resonance in medicine.

[18]  Craig K Abbey,et al.  Computer aided detection of masses in mammography using subregion Hotelling observers. , 2003, Medical physics.

[19]  M. Giger,et al.  Computerized analysis of shadowing on breast ultrasound for improved lesion detection. , 2003, Medical physics.

[20]  R. Chang,et al.  Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. , 2003, Ultrasound in medicine & biology.

[21]  K. Han,et al.  Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. , 2003, Radiology.

[22]  R. Chang,et al.  Support vector machines for diagnosis of breast tumors on US images. , 2003, Academic radiology.

[23]  R. F. Wagner,et al.  Assessment of medical imaging and computer-assist systems: lessons from recent experience. , 2002, Academic radiology.

[24]  T. M. Kolb,et al.  Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. , 2002, Radiology.

[25]  P M Shankar,et al.  Computer aided classification of masses in ultrasonic mammography. , 2002, Medical physics.

[26]  Carol H Lee.  Screening mammography: proven benefit, continued controversy. , 2002, Radiologic clinics of North America.

[27]  Berkman Sahiner,et al.  Improvement of computerized mass detection on mammograms: fusion of two-view information. , 2002, Medical physics.

[28]  B. Zheng,et al.  Soft-copy mammographic readings with different computer-assisted detection cuing environments: preliminary findings. , 2001, Radiology.

[29]  Berkman Sahiner,et al.  Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization , 2001, IEEE Transactions on Medical Imaging.

[30]  C J D'Orsi,et al.  Computer-aided detection: there is no free lunch. , 2001, Radiology.

[31]  Fred Godtliebsen,et al.  Feature extraction and classification of dynamic contrast-enhanced T2*-weighted breast image data , 2001, IEEE Transactions on Medical Imaging.

[32]  Lubomir M. Hadjiiski,et al.  Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses. , 2001, Medical physics.

[33]  T. Freer,et al.  Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. , 2001, Radiology.

[34]  M. Giger,et al.  Automatic segmentation of breast lesions on ultrasound. , 2001, Medical physics.

[35]  R. F. Wagner,et al.  Components-of-variance models for random-effects ROC analysis: the case of unequal variance structures across modalities. , 2001, Academic radiology.

[36]  R F Wagner,et al.  Analysis of uncertainties in estimates of components of variance in multivariate ROC analysis. , 2001, Academic radiology.

[37]  Sheng Liu,et al.  Multiresolution detection of spiculated lesions in digital mammograms , 2001, IEEE Trans. Image Process..

[38]  Alan C. Evans,et al.  False-negative breast screening assessment: what lessons can we learn? , 2001, Clinical radiology.

[39]  K. J. Ray Liu,et al.  Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation , 2001, IEEE Transactions on Medical Imaging.

[40]  K. Kerlikowske,et al.  Performance of Screening Mammography among Women with and without a First-Degree Relative with Breast Cancer , 2000, Annals of Internal Medicine.

[41]  N Karssemeijer,et al.  Automated classification of clustered microcalcifications into malignant and benign types. , 2000, Medical physics.

[42]  N. Karssemeijer,et al.  An automatic method to discriminate malignant masses from normal tissue in digital mammograms1 , 2000, Physics in medicine and biology.

[43]  C. Vyborny,et al.  Breast cancer: importance of spiculation in computer-aided detection. , 2000, Radiology.

[44]  L. Bruce,et al.  Classifying mammographic mass shapes using the wavelet transform modulus-maxima method , 1999, IEEE Transactions on Medical Imaging.

[45]  Berkman Sahiner,et al.  Classification of malignant and benign masses based on hybrid ART2LDA approach , 1999, IEEE Transactions on Medical Imaging.

[46]  Mario Vento,et al.  Automatic classification of clustered microcalcifications by a multiple expert system , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[47]  Heinz-Otto Peitgen,et al.  Scale-space signatures for the detection of clustered microcalcifications in digital mammograms , 1999, IEEE Transactions on Medical Imaging.

[48]  K. Doi,et al.  Computer-aided diagnosis in radiology: potential and pitfalls. , 1999, European journal of radiology.

[49]  H P Chan,et al.  Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. , 1999, Medical physics.

[50]  S. Orel,et al.  BI-RADS categorization as a predictor of malignancy. , 1999, Radiology.

[51]  Matthew T. Freedman,et al.  Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network , 1999, Pattern Recognit..

[52]  Masayuki Murakami,et al.  Computerized detection of malignant tumors on digital mammograms , 1999, IEEE Transactions on Medical Imaging.

[53]  N. Petrick,et al.  Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. , 1998, Medical physics.

[54]  M L Giger,et al.  Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. , 1998, Medical physics.

[55]  K Doi,et al.  Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms. , 1998, Medical physics.

[56]  L. Liberman,et al.  The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. , 1998, AJR. American journal of roentgenology.

[57]  H Yoshida,et al.  Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. , 1998, Medical physics.

[58]  N. Petrick,et al.  Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. , 1998, Medical physics.

[59]  Rangaraj M. Rangayyan,et al.  Measures of acutance and shape for classification of breast tumors , 1997, IEEE Transactions on Medical Imaging.

[60]  K. J. Ray Liu,et al.  Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms , 1997, IEEE Transactions on Medical Imaging.

[61]  Martin P. DeSimio,et al.  Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency , 1997, IEEE Transactions on Medical Imaging.

[62]  Matthew A. Kupinski,et al.  Investigation of regularized neural networks for the computerized detection of mass lesions in digital mammograms , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[63]  R. Ansari,et al.  Detection of microcalcifications in mammograms using higher order statistics , 1997, IEEE Signal Processing Letters.

[64]  B Sahiner,et al.  False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis. , 1997, Medical physics.

[65]  R. Swensson.  Unified measurement of observer performance in detecting and localizing target objects on images. , 1996, Medical physics.

[66]  Berkman Sahiner,et al.  Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images , 1996, IEEE Trans. Medical Imaging.

[67]  N. Obuchowski,et al.  Quantitative classification of breast tumors in digitized mammograms. , 1996, Medical physics.

[68]  K Doi,et al.  An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms. , 1996, Academic radiology.

[69]  Atam P. Dhawan,et al.  Analysis of mammographic microcalcifications using gray-level image structure features , 1996, IEEE Trans. Medical Imaging.

[70]  L J Yeoman,et al.  Screening interval breast cancers: mammographic features and prognosis factors. , 1996, Radiology.

[71]  Lihua Li,et al.  X-ray medical image processing using directional wavelet transform , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[72]  K Doi,et al.  An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. , 1996, Medical physics.

[73]  Michele Nappi,et al.  Computer Aided Diagnosis in Radiology , 1995, ICSC.

[74]  N. Petrick,et al.  Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis. , 1995, Medical physics.

[75]  Vijay K. Jain,et al.  Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.

[76]  Y H Chang,et al.  Computer-aided detection of clustered microcalcifications in digitized mammograms. , 1995, Academic radiology.

[77]  L. Clarke,et al.  Tree structured wavelet transform segmentation of microcalcifications in digital mammography. , 1995, Medical physics.

[78]  Robin N. Strickland,et al.  Wavelet transforms for detecting microcalcifications in mammography , 1994, Proceedings of 1st International Conference on Image Processing.

[79]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

[80]  J. M. Pruneda,et al.  Computer-aided mammographic screening for spiculated lesions. , 1994, Radiology.

[81]  W D Flanders,et al.  The lifetime risk of developing breast cancer. , 1993, Journal of the National Cancer Institute.

[82]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[83]  D. Kopans.  The positive predictive value of mammography. , 1992, AJR. American journal of roentgenology.

[84]  M L Giger,et al.  Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images. , 1991, Medical physics.

[85]  D. Dance,et al.  Automatic computer detection of clustered calcifications in digital mammograms , 1990, Physics in medicine and biology.

[86]  D. Chakraborty,et al.  Free-response methodology: alternate analysis and a new observer-performance experiment. , 1990, Radiology.

[87]  D P Chakraborty,et al.  Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data. , 1989, Medical physics.

[88]  K Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. , 1987, Medical physics.

[89]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[90]  H.M. Wechsler,et al.  Digital image processing, 2nd ed. , 1981, Proceedings of the IEEE.

[91]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[92]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[93]  Summary of Safety and Effectiveness Data , 2002 .

[94]  Nico Karssemeijer,et al.  Computer-Aided Diagnosis in Medical Imaging , 2001, IEEE Trans. Medical Imaging.

[95]  Jong Kook Kim,et al.  Statistical textural features for detection of microcalcifications in digitized mammograms , 1999, IEEE Transactions on Medical Imaging.

[96]  Rangaraj M. Rangayyan,et al.  Detection of Breast Tumor Boundaries Using ISO-Intensity Contours and Dynamic Thresholding , 1998, Digital Mammography / IWDM.

[97]  Ricardo José Ferrari,et al.  Detection and Characterization of Mammographic Masses by Artificial Neural Network , 1998, Digital Mammography / IWDM.

[98]  E. Feuer,et al.  Estimating Lifetime and Age-Conditional Probabilities of Developing Cancer , 1998, Lifetime data analysis.

[99]  Maryellen L. Giger,et al.  Automated seeded lesion segmentation on digital mammograms , 1998, IEEE Transactions on Medical Imaging.

[100]  Nico Karssemeijer,et al.  Detection of stellate distortions in mammograms , 1996, IEEE Trans. Medical Imaging.

[101]  Berkman Sahiner,et al.  An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection , 1996, IEEE Trans. Medical Imaging.

[102]  Martin D. Fox,et al.  Classifying mammographic lesions using computerized image analysis , 1993, IEEE Trans. Medical Imaging.

[103]  D Brzakovic,et al.  An approach to automated detection of tumors in mammograms. , 1990, IEEE transactions on medical imaging.

[104]  S. Lai,et al.  On techniques for detecting circumscribed masses in mammograms. , 1989, IEEE transactions on medical imaging.