Berkman Sahiner

Summary

Affiliation: University of Michigan
Country: USA

Publications

  1. pmc Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches
    Heang Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 5842, USA
    Med Phys 35:4087-95. 2008
  2. ncbi request reprint Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis
    B Sahiner
    Department of Radiology, University of Michigan, Ann Arbor 48109 0904, USA
    Phys Med Biol 43:2853-71. 1998
  3. pmc Computerized nipple identification for multiple image analysis in computer-aided diagnosis
    Chuan Zhou
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 31:2871-82. 2004
  4. pmc Dual system approach to computer-aided detection of breast masses on mammograms
    Jun Wei
    Department of Radiology, University of Michigan, Ann Arbor Michigan 48109, USA
    Med Phys 33:4157-68. 2006
  5. pmc Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms
    Jun Ge
    Department of Radiology, University of Michigan, CGC B2103, 1500 E Medical Center Drive, Ann Arbor, MI 48109, USA
    Phys Med Biol 52:981-1000. 2007
  6. pmc Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: a 3D approach
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 39:28-39. 2012
  7. pmc Classifier performance prediction for computer-aided diagnosis using a limited dataset
    Berkman Sahiner
    Department of Radiology University of Michigan, Ann Arbor Michigan 48109, USA
    Med Phys 35:1559-70. 2008
  8. pmc Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy
    Berkman Sahiner
    Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109 0904, USA
    Radiology 242:716-24. 2007
  9. pmc Joint two-view information for computerized detection of microcalcifications on mammograms
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor 48109 0904, USA
    Med Phys 33:2574-85. 2006
  10. pmc Classifier performance estimation under the constraint of a finite sample size: resampling schemes applied to neural network classifiers
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109 0904, United States
    Neural Netw 21:476-83. 2008

Detail Information

Publications66

  1. pmc Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches
    Heang Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 5842, USA
    Med Phys 35:4087-95. 2008
    ..02 and 0.01, respectively) as estimated by alternative FROC analysis. The combined system is a promising approach to improving automated mass detection on DBTs...
  2. ncbi request reprint Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis
    B Sahiner
    Department of Radiology, University of Michigan, Ann Arbor 48109 0904, USA
    Phys Med Biol 43:2853-71. 1998
    ..Our results show that the choice of the feature selection technique is important in computer-aided diagnosis, and that the GA may be a useful tool for designing classifiers for lesion characterization...
  3. pmc Computerized nipple identification for multiple image analysis in computer-aided diagnosis
    Chuan Zhou
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 31:2871-82. 2004
    ..Automated nipple detection will be an important step towards multiple image analysis for CAD...
  4. pmc Dual system approach to computer-aided detection of breast masses on mammograms
    Jun Wei
    Department of Radiology, University of Michigan, Ann Arbor Michigan 48109, USA
    Med Phys 33:4157-68. 2006
    ..05) improvements on the free response receiver operating characteristic curves were observed when the dual system and the single system were compared using the test sets with either average masses or subtle masses...
  5. pmc Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms
    Jun Ge
    Department of Radiology, University of Michigan, CGC B2103, 1500 E Medical Center Drive, Ann Arbor, MI 48109, USA
    Phys Med Biol 52:981-1000. 2007
    ..08, 0.14 and 0.50 per image for the SFM CAD system. When evaluated for malignant cases only, the difference of the performance of the two CAD systems was not statistically significant with AFROC analysis...
  6. pmc Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: a 3D approach
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 39:28-39. 2012
    ..To design a computer-aided detection (CADe) system for clustered microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system...
  7. pmc Classifier performance prediction for computer-aided diagnosis using a limited dataset
    Berkman Sahiner
    Department of Radiology University of Michigan, Ann Arbor Michigan 48109, USA
    Med Phys 35:1559-70. 2008
    ..632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset...
  8. pmc Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy
    Berkman Sahiner
    Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109 0904, USA
    Radiology 242:716-24. 2007
    ....
  9. pmc Joint two-view information for computerized detection of microcalcifications on mammograms
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor 48109 0904, USA
    Med Phys 33:2574-85. 2006
    ..Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing FPs from true microcalcification clusters...
  10. pmc Classifier performance estimation under the constraint of a finite sample size: resampling schemes applied to neural network classifiers
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109 0904, United States
    Neural Netw 21:476-83. 2008
    ..Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited data set...
  11. pmc Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size
    Berkman Sahiner
    Department of Radiology, The University of Michigan, MIB C480A, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA
    Acad Radiol 16:1518-30. 2009
    ....
  12. ncbi request reprint Computerized characterization of breast masses on three-dimensional ultrasound volumes
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 31:744-54. 2004
    ..The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists...
  13. ncbi request reprint Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size
    B Sahiner
    Department of Radiology, University of Michigan, Ann Arbor 48109 0904, USA
    Med Phys 27:1509-22. 2000
    ..For our simulation conditions, these estimates were always pessimistically (conservatively) biased if the ratio of the total number of available samples per class to the number of available features was greater than five...
  14. ncbi request reprint Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization
    B Sahiner
    Department of Radiology, University of Michigan, Ann Arbor 48109 0904, USA
    IEEE Trans Med Imaging 20:1275-84. 2001
    ..89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant...
  15. ncbi request reprint Improvement of mammographic mass characterization using spiculation meausures and morphological features
    B Sahiner
    Department of Radiology, University of Michigan, Ann Arbor 48109, USA
    Med Phys 28:1455-65. 2001
    ..91 +/- 0.02. Our results indicate that combining texture features with morphological features extracted from automatically segmented mass boundaries will be an effective approach for computer-aided characterization of mammographic masses...
  16. ncbi request reprint Computer aided detection of clusters of microcalcifications on full field digital mammograms
    Jun Ge
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 33:2975-88. 2006
    ..The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates...
  17. pmc Bilateral analysis based false positive reduction for computer-aided mass detection
    Yi Ta Wu
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 34:3334-44. 2007
    ..63, respectively, at the corresponding sensitivities, the FP rates were reduced by 40%, 44%, and 42% with the bilateral symmetry information. The improvement was statistically significance (p < 0.05) as estimated by JAFROC analysis...
  18. ncbi request reprint Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study
    Lubomir Hadjiiski
    Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109 0904, USA
    Radiology 233:255-65. 2004
    ..To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' characterization of masses on serial mammograms...
  19. ncbi request reprint ROC study of the effect of stereoscopic imaging on assessment of breast lesions
    Heang Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 32:1001-9. 2005
    ..This study demonstrates the potential of using stereomammography to improve the detection and characterization of mammographic lesions...
  20. pmc Computer-aided detection systems for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms
    Jun Wei
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
    Acad Radiol 14:659-69. 2007
    ..To compare the performance of computer aided detection (CAD) systems on pairs of full-field digital mammogram (FFDM) and screen-film mammogram (SFM) obtained from the same patients...
  21. pmc Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching
    Jiazheng Shi
    Department of Radiology, The University of Michigan, Ann Arbor Michigan 48109, USA
    Med Phys 34:1336-47. 2007
    ..7 +/- 3.3 mm. Only two pairs had an error larger than 10 mm. The average volume overlap measure was 0.71 +/- 0.24. Eighty-three of the 101 pairs had ratios larger than 0.5, and only two pairs had no overlap. The final hit rate was 93/101...
  22. pmc Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis
    Peter Filev
    Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 35:5340-50. 2008
    ..0014). Our interval change analysis system can detect the corresponding cluster on the prior mammogram with high sensitivity, and classify them with a satisfactory accuracy...
  23. ncbi request reprint Breast masses: computer-aided diagnosis with serial mammograms
    Lubomir Hadjiiski
    Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109 0904, USA
    Radiology 240:343-56. 2006
    ....
  24. pmc A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis
    Yiheng Zhang
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 33:3781-95. 2006
    ....
  25. pmc Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours
    Ted W Way
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 33:2323-37. 2006
    ..The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries...
  26. pmc Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices
    Heang Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 37:3576-86. 2010
    ....
  27. pmc Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features
    Ted W Way
    Department of Radiology, University of Michigan, Ann Arbor 48109 5842, USA
    Med Phys 36:3086-98. 2009
    ..This study demonstrated that the authors' proposed segmentation and feature extraction techniques are promising for classifying lung nodules on CT images...
  28. pmc Computer-aided detection of breast masses on mammograms: dual system approach with two-view analysis
    Jun Wei
    Department of Radiology, University of Michigan, 1500 E Medical Center Drive, Med Inn Building C478, Ann Arbor, Michigan 48109 5842, USA
    Med Phys 36:4451-60. 2009
    ..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...
  29. pmc Computer-aided diagnosis of lung nodules on CT scans: ROC study of its effect on radiologists' performance
    Ted Way
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109 5842, USA
    Acad Radiol 17:323-32. 2010
    ..The aim of this study was to evaluate the effect of computer-aided diagnosis (CAD) on radiologists' estimates of the likelihood of malignancy of lung nodules on computed tomographic (CT) imaging...
  30. ncbi request reprint Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images
    Jun Wei
    Department of Radiology, University of Michigan, Ann Arbor, Ann Arbor, Michigan 49109, USA
    Med Phys 31:933-42. 2004
    ..4% to 6.3%. The mean bias ranged from 3% to 6%. The high correlation indicates that changes in mammographic density may be a useful indicator of changes in fibroglandular tissue volume in the breast...
  31. pmc Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): performance evaluation with independent data sets
    Chuan Zhou
    Department of Radiology, University of Michigan, Med Inn Building C479, 1500 E Medical Center Drive, Ann Arbor, Michigan 48109, USA
    Med Phys 36:3385-96. 2009
    ....
  32. pmc Characterization of mammographic masses based on level set segmentation with new image features and patient information
    Jiazheng Shi
    Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 35:280-90. 2008
    ..Finally, an independent test on the publicly available digital database for screening mammography with 132 benign and 197 malignant ROIs containing masses achieved a view-based Az value of 0.84 +/- 0.02...
  33. pmc Multi-modality CADx: ROC study of the effect on radiologists' accuracy in characterizing breast masses on mammograms and 3D ultrasound images
    Berkman Sahiner
    Department of Radiology, The University of Michigan, MIB C480A, 1500 East Medical Center Drive, Ann Arbor, MI 48109 5842, USA
    Acad Radiol 16:810-8. 2009
    ..To investigate the effect of a computer-aided diagnosis (CADx) system on radiologists' performance in discriminating malignant and benign masses on mammograms and three-dimensional (3D) ultrasound (US) images...
  34. pmc Computer-aided detection of breast masses on full field digital mammograms
    Jun Wei
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 32:2827-38. 2005
    ..85, 1.31, and 2.14 FP marks/image, respectively, at the corresponding sensitivities. This study demonstrated the usefulness of our CAD techniques for automated detection of masses on FFDM images...
  35. pmc Dynamic multiple thresholding breast boundary detection algorithm for mammograms
    Yi Ta Wu
    Department of Radiology, University of Michigan, Ann Arbor Michigan 48109, USA
    Med Phys 37:391-401. 2010
    ..In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms...
  36. pmc Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting
    Zhanyu Ge
    Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 32:2443-54. 2005
    ..37, 1.61, and 0.34, respectively. The improvement in the test Az with the 25 new features was statistically significant (p<0.0001) compared to that with the previous 19 features alone...
  37. pmc Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications
    Chuan Zhou
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA
    Med Phys 34:4567-77. 2007
    ..The results demonstrate that vessel segmentation using our method can extract the pulmonary vessels accurately and is not degraded by PE occlusion to the vessels in these test cases...
  38. pmc Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation
    Jiazheng Shi
    Department of Radiology, The University of Michigan, Ann Arbor Michigan 48109 5842, USA
    Med Phys 36:5052-63. 2009
    ..641). The automated mass segmentation method may have the potential to assist physicians in monitoring volume change in breast masses in response to treatment...
  39. pmc A new automated method for the segmentation and characterization of breast masses on ultrasound images
    Jing Cui
    Department of Radiology, The University of Michigan, Ann Arbor Michigan 48109 0904, USA
    Med Phys 36:1553-65. 2009
    ..88 +/- 0.03 to 0.92 +/- 0.02, indicating a comparable performance to those extracted from manual segmentation by radiologists (A(z) value range: 0.87 +/- 0.03 to 0.90 +/- 0.03)...
  40. pmc Quasi-continuous and discrete confidence rating scales for observer performance studies: Effects on ROC analysis
    Lubomir Hadjiiski
    Department of Radiology, The University of Michigan, CGC B2102, 1500 East Medical Center Drive, Ann Arbor, MI 48109 0904, USA
    Acad Radiol 14:38-48. 2007
    ..To examine the effects of the number of categories in the rating scale used in an observer experiment on the results of ROC analysis by a simulation study...
  41. pmc Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation
    Ethan Street
    Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 34:4399-408. 2007
    ..In addition, the quality rating data showed that, despite the very lax assumptions made on the lesion characteristics in designing the system, the automatic contours approximated many of the lesions very well...
  42. ncbi request reprint Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system
    Metin N Gurcan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 29:2552-8. 2002
    ..These preliminary results demonstrate the feasibility of our approach to lung nodule detection and FP reduction on CT images...
  43. ncbi request reprint Comparison of similarity measures for the task of template matching of masses on serial mammograms
    Peter Filev
    Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 32:515-29. 2005
    ..05) in the task of matching the corresponding masses on serial mammograms than the other nine similarity measures...
  44. pmc Artifact reduction methods for truncated projections in iterative breast tomosynthesis reconstruction
    Yiheng Zhang
    Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
    J Comput Assist Tomogr 33:426-35. 2009
    ....
  45. pmc Effect of finite sample size on feature selection and classification: a simulation study
    Ted W Way
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 5842, USA
    Med Phys 37:907-20. 2010
    ..The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples...
  46. pmc Computerized image analysis: texture-field orientation method for pectoral muscle identification on MLO-view mammograms
    Chuan Zhou
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 5842, USA
    Med Phys 37:2289-99. 2010
    ..To develop a new texture-field orientation (TFO) method that combines a priori knowledge, local and global information for the automated identification of pectoral muscle on mammograms...
  47. pmc Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience
    Heang Ping Chan
    Department of Radiology, University of Michigan, 1500 E Medical Center Dr, UHB1F510B, Ann Arbor, MI 48109 0030, USA
    Radiology 237:1075-80. 2005
    ..03 (standard error of mean). The CAD system achieved a sensitivity of 85%, with 2.2 false-positive objects per case. The results demonstrate the feasibility of the authors' approach to the development of a CAD system for DBT mammography...
  48. ncbi request reprint An observer study comparing spot imaging regions selected by radiologists and a computer for an automated stereo spot mammography technique
    Mitchell M Goodsitt
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 0030, USA
    Med Phys 31:1558-67. 2004
    ..8% +/- 10.0% for the radiologists. This study indicates that the CAD determined ROIs could potentially be useful for a screening technique that includes stereo spot mammography imaging...
  49. pmc Application of boundary detection information in breast tomosynthesis reconstruction
    Yiheng Zhang
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Med Phys 34:3603-13. 2007
    ..Our study demonstrates that, by using the 2D and 3D breast boundary information, all breast boundary and most detector boundary artifacts can be effectively removed on all tomosynthesized slices...
  50. pmc Performance analysis of three-class classifiers: properties of a 3-D ROC surface and the normalized volume under the surface for the ideal observer
    Berkman Sahiner
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
    IEEE Trans Med Imaging 27:215-27. 2008
    ..Our results indicate that, under the conditions that lead to our 3-D ROC analysis, the performance of a three-class classifier may be analyzed by considering the ROC surface, and its accuracy characterized by the NVUS...
  51. pmc Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study
    Ted W Way
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
    Phys Med Biol 53:1295-312. 2008
    ..Tracking nodule growth with computerized segmentation methods would reduce inter- and intraobserver variabilities...
  52. pmc Head and neck cancers on CT: preliminary study of treatment response assessment based on computerized volume analysis
    Lubomir Hadjiiski
    Department of Radiology, The University of Michigan, 1500 E Medical Center Dr, MIB C476, Box 5842, Ann Arbor, MI 48109 5842, USA
    AJR Am J Roentgenol 194:1083-9. 2010
    ....
  53. pmc Computer-aided detection of breast masses: four-view strategy for screening mammography
    Jun Wei
    Department of Radiology, University of Michigan, 1500 East Medical Center Drive, C478 Med Inn Building, Ann Arbor, Michigan 48109 5842, USA
    Med Phys 38:1867-76. 2011
    ..To improve the performance of a computer-aided detection (CAD) system for mass detection by using four-view information in screening mammography...
  54. ncbi request reprint Optimal neural network architecture selection: improvement in computerized detection of microcalcifications
    Metin N Gurcan
    Department of Radiology, University of Michigan Hospitals, Ann Arbor 48109 0030, USA
    Acad Radiol 9:420-9. 2002
    ..The authors evaluated the effect of optimal neural network architecture selection on the performance of a computer-aided diagnostic system designed to detect microcalcification clusters on digitized mammograms...
  55. pmc Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study
    Jun Wei
    Department of Radiology, University of Michigan Hospital, 1500 E Medical Center Dr, MIB C478, Ann Arbor, MI 48109 5842, USA
    Radiology 260:42-9. 2011
    ..To develop a computerized mammographic parenchymal pattern (MPP) measure and investigate its association with breast cancer risk...
  56. pmc Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial
    Mark A Helvie
    Department of Radiology, University of Michigan Health Systems, 1500 E Medical Center Dr, TC 2910, Ann Arbor, MI 48109 0326, USA
    Radiology 231:208-14. 2004
    ..To evaluate a noncommercial computer-aided detection (CAD) program for breast cancer detection with screening mammography...
  57. ncbi request reprint Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis -- experience in 263 patients
    Nicholas Petrick
    Department of Radiology, University of Michigan Medical Center, CGC B2102, Box 0904, 1500 E Medical Center Dr, Ann Arbor, MI 48109 0904 From the 2001 RSNA scientific assembly Received June 18, 2001
    Radiology 224:217-24. 2002
    ..To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses...
  58. pmc A similarity study of content-based image retrieval system for breast cancer using decision tree
    Hyun Chong Cho
    Department of Radiology, The University of Michigan, Ann Arbor, MI, USA
    Med Phys 40:012901. 2013
    ..We are developing a decision tree content-based image retrieval (DTCBIR) CADx system to assist radiologists in characterization of breast masses on ultrasound images...
  59. pmc Preliminary investigation of computer-aided detection of pulmonary embolism in three-dimensional computed tomography pulmonary angiography images
    Chuan Zhou
    Department of Radiology, University of Michigan, Ann Arbor, CGC B2103, 1500 E Medical Center Drive, Ann Arbor, MI 48109, USA
    Acad Radiol 12:782-92. 2005
    ..We sought to develop a computer-aided diagnosis (CAD) system for assisting radiologists in the detection of pulmonary embolism (PE) on computed tomography pulmonary angiographic (CTPA) images...
  60. pmc Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review
    Heang Ping Chan
    Department of Radiology, Med Inn Building C477, 1500 East Medical Center Drive, The University of Michigan, Ann Arbor, MI 48109 5842, USA
    Acad Radiol 15:535-55. 2008
    ..In this review, we summarize the studies that have been reported in these areas, discuss some challenges in the development of CAD, and identify areas that deserve particular attention in future research...
  61. ncbi request reprint Effects of magnification and zooming on depth perception in digital stereomammography: an observer performance study
    Heang Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
    Phys Med Biol 48:3721-34. 2003
    ....
  62. pmc Evaluating computer-aided detection algorithms
    Hong Jun Yoon
    Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
    Med Phys 34:2024-38. 2007
    ..Software based on this work is expected to benefit CAD developers working in diverse areas of medical imaging...
  63. pmc Similarity evaluation in a content-based image retrieval (CBIR) CADx system for characterization of breast masses on ultrasound images
    Hyun Chong Cho
    Department of Radiology, The University of Michigan, Ann Arbor Michigan 48109 0904, USA
    Med Phys 38:1820-31. 2011
    ..In this study, the authors compared seven similarity measures to be considered for the CBIR system. The similarity between the query and the retrieved masses was evaluated based on radiologists' visual similarity assessments...
  64. ncbi request reprint Improvement of computerized mass detection on mammograms: fusion of two-view information
    Sophie Paquerault
    Department of Radiology, University of Michigan, Ann Arbor 48109 0030, USA
    Med Phys 29:238-47. 2002
    ..The corresponding cased-based detection sensitivity improved from 77% to 91%...
  65. pmc Advances in computer-aided diagnosis for breast cancer
    Lubomir Hadjiiski
    Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 0904, USA
    Curr Opin Obstet Gynecol 18:64-70. 2006
    ....
  66. ncbi request reprint 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
    Lori E Dodd
    Biometrics Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 6130 Executive Blvd, MSC 7434, Bethesda, MD 20892, USA
    Acad Radiol 11:462-75. 2004
    ..We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database...

Research Grants1

  1. Multimodality CAD system with image references for breast mass characterization
    Berkman Sahiner; Fiscal Year: 2007
    ..Any reduction in this number without a decrease in breast cancer detection sensitivity will decrease health care costs, as well as contribute to the well-being of the patient by reducing anxiety and morbidity. ..