Information fusion with Bayes networks in computer-aided...

Image analysis – Applications – Biomedical applications

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S155000, C382S173000, C382S181000, C378S001000, C378S004000

Reexamination Certificate

active

10288766

ABSTRACT:
This invention provides an information fusion method for multiple indicators of cancers detected with multiple channels. Each channel consists of specifically tuned detectors, features, classifiers and Bayes networks. The outputs of the Bayes networks are probabilities of malignancy for the detections passing the corresponding classifier.

REFERENCES:
patent: 5260871 (1993-11-01), Goldberg
patent: 5627907 (1997-05-01), Gur et al.
patent: 5661820 (1997-08-01), Kegelmeyer, Jr.
patent: 5696884 (1997-12-01), Heckerman et al.
patent: 5704018 (1997-12-01), Heckerman et al.
patent: 5799100 (1998-08-01), Clarke et al.
patent: 5802256 (1998-09-01), Heckerman et al.
patent: 5815591 (1998-09-01), Roehrig et al.
patent: 5999639 (1999-12-01), Rogers et al.
patent: 6056690 (2000-05-01), Roberts
patent: 6075879 (2000-06-01), Roehrig et al.
patent: 6115488 (2000-09-01), Rogers et al.
patent: 6138045 (2000-10-01), Kupinski et al.
patent: 6198838 (2001-03-01), Roehrig et al.
patent: 6205236 (2001-03-01), Rogers et al.
patent: 6404908 (2002-06-01), Schneider et al.
patent: 6529888 (2003-03-01), Heckerman et al.
patent: 6601055 (2003-07-01), Roberts
patent: 6738499 (2004-05-01), Doi et al.
patent: 6801645 (2004-10-01), Collins et al.
patent: 6810391 (2004-10-01), Birkhoelzer et al.
patent: 7024399 (2006-04-01), Sumner et al.
patent: 2003/0104499 (2003-06-01), Pressman et al.
patent: 2003/0190602 (2003-10-01), Pressman et al.
patent: 2003/0199685 (2003-10-01), Pressman et al.
patent: 2006/0171573 (2006-08-01), Rogers
Heang-Ping Chan, et al., Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space; Phys. Med. Biol., 1995, pp. 857-876; vol. 40; IOP Publishing Ltd.; UK.
Yuan-Hsiang Chang et al., Robustness of Computerized Identification of Masses in Digitized Mammograms; Investigative Radiology; Sep. 1996; pp. 563-568; vol. 31. No. 9: Lippincott-Raven Publishers: Philadelphia.
Yuan-Hsiang Chang et al., Computerized Identification of Suspicious Regions for Masses in Digitized Mammograms; Investigative Radiology;Mar. 1996; pp. 146-153; vol. 31, No. 3; Lippincott-Raven Publishers; Philadelphia.
Ioanna Christoyianni et al., Fast Detection of Masses in Computer-Aided Mammography; IEEE Signal Processing Magazine; Jan. 2000; pp. 54-64; vol. 17, No. 1; IEEE Signal Processing Society; Piscataway NJ.
David B. Fogel et al., Linear and Neural Models for Classifying Breast Masses; IEEE Transactions on Medical Imaging; Jun. 1998; pp. 485-488; vol. 17, No. 3; Engineering in Medicine and Biology Society; Piscataway NJ.
Maryellen L. Giger et al., Computerized characterization of mammographic masses: analysis of spiculation; Cancer Letters; 1994; pp. 201-211; vol. 77; Elsevier Scientific Publishers Ireland Ltd.; Ireland.
R. Gupta et al.; The use of tecture analysis to delineate suspicious masses in mammography; Phys. Med. Biol. 1995; pp. 835-855; vol. 40; IOP Publishing Ltd.; UK.
Lubomir Hadjiiski et al.; Classification of Malignant and Benign Masses Based on Hybrid ART2LDA Approach; IEEE Transactions on Medical Imaging; Dec. 1999; pp. 1178-1187; vol. 18, No. 12; Engineering in Medicine and Biology Society; Piscataway NJ.
Lubomir Hadjiiski et al.; Hybrid unsupervised approach for computerized classification of malignant and benign masses on mammograms; Part of the SPIE Conference on Image Processing, San Diego; Feb. 1999; pp. 464-473; vol. 3661; International Society for Optical Engineering; Bellingham WA.
Trevor Hastie et al.; Statistical Measures for the Computer-Aided Diagnosis of Mammographic Masses; Journal of Computational and Graphical Statistics; 1999; pp. 531-543; vol. 8, No. 3; American Statistical Association et al., Alexandria VA.
Zhimin Huo et al; Analysis of spiculation in the computerized classification of mammographic masses; Medical Physics; Oct. 1995; pp. 1569-1579; vol. 22, No. 10; American Institute of Physics; Melville NY.
Zhimin Huo et al.; Robustness of Computerized Scheme for the Classification of Malignant and Benign Masses on Digitized Mammograms; Computer-Aided Diagnosis in Medical Imaging; 1999; pp. 277-280; Elsevier Science B. V. New York.
Zhimin Huo et al; Automated Computerized Classification of Malignant and Benign Masses on Digitized Mammograms; Academic Radiology; Mar. 1998; pp. 155-168; vol. 5, No. 3; Association of University Radiologists; Oak Brook IL.
S. K. Kinoshita et al.; Characterization of breast masses using texture and shape features; Computer-Aided Diagnosis in Medical Imaging; 1999; pp. 265-270; Elsevier Science B. V. New York.
Shuk-Mei Lai et al.; On Techniques for Detecting Circumscribed Masses in Mammograms; IEEE Transactions on Medical Imaging; Dec. 1989; pp. 377-386; vol. 8, No. 4; Engineering in Medicine and Biology Society; Piscataway NJ.
Huai Li et al; Mammographic Mass Detection by Stochastic Modeling and a Multi-Modular Neural Network; SPIE Conference on Image Processing, Newport Beach; Feb. 25-28, 1997; pp. 480-490; vol. 3034; International Society for Optical Engineering; Bellingham WA.
Shih-Chung B. Lo et al; A Multiple Circular Path Convolution Neural Network System for Detection of Mammographic Masses; IEEE Transactions on Medical Imaging; Feb. 2002; pp. 150-158; vol. 21, No. 2; Engineering in Medicine and Biology Society; Piscatawy NJ.
Naga R. Mudigonda et al; Detection of Breast Masses in Mammograms by Density Slicing and Texture Flow-Field Analysis; IEEE Transactions on Medical Imaging; Dec. 2001; pp. 1215-1227; vol. 20, No. 12; Engineering in Medicine and Biology Society; Piscataway NJ.
Naga R. Mudigonda et al; Gradient and Texture Analysis for the Classification of Mammographic Masses; IEEE Transactions on Medical Imaging; Oct. 2000; pp. 1032-1043; vol. 19, No. 10; Engineering in Medicine and Biology Society; Piscataway NJ.
Robert M. Nishikawa et al; Computer-Aided Detection and Diagnosis of Masses and Clustered Microcalcifications from Digital Mammograms; State of the Art in Digital Mammographic Image Analysis; 1994; pp. 82-102; World Scientific Publishing Co., New Jersey.
Nicholas Petrick et al; An Adaptive Density-Weighted Contrast Enhancement Filter for Mammographic Breast Mass Detection; IEEE Transactions on Medical Imaging; Feb. 1996; pp. 59-67; vol. 15, No. 1; Engineering in Medicine and Biology Society; Piscataway NJ.
Nicholas Petrick et al; Unitary Ranking in the Automated Detection of Mammographic Masses; Proceedings of SPIE Conference on Image Processing, Newport Beach; Feb. 25-28, 1997; pp. 522-530; vol. 3034; International Society for Optical Engineering; Bellingham WA.
Nicholas Petrick et al; Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification; Medical Physics; Oct. 1996; pp. 1685-1696; vol. 23, No. 10; American Institute of Physics; Melville NY.
Arthur Petrosian et al; Computer-aided diagnosis in mamography: classification of mass and normal tissue by texture analysis; Physics in Medicine and Biology; 1994; pp. 2273-2288; vol. 39; IOP Publishing Ltd.; UK.
Wei Qian et al; Hybrid Adaptive Wavelet-based CAD Method for Mass Detection; proceedings of SPIE Conference on Image Processing, Newport Beach; Feb. 25-28, 1997; pp. 790-801; vol. 3034; International Society for Optical Engineering; Bellingham WA.
Wei Qian et al. Adaptive Directional Wavelet-based CAD Method for Mass Detection; Computer-Aided Diagnosis in Medical Imaging; 1999; pp. 253-259; Elsevier Science B.V. ; New York.
Berkman Sahiner et al; Image feature selection by a genetic algorithm: Application to classification of mass and normal breast tissue; Medical Physics; Oct. 1996; pp. 1671-1684; vol. 23, No. 10; American Institute of Physics; Melville NY.
Berkman Sahiner et al; Classification of Mass and Normal Breast tissue: Feature Selection Using a Genetic Algorithm; Colloquium on Digital Mammography; Feb. 1996: pp. 379-384: Elsevier Science B. V.. New York.
Berkman Sahiner et al; Classification of Mass and Normal Breast Tissue: A Convolution Neural Network Classifier with Spatial Domain and Texture Images; IEEE Transactions on Medical Imaging; Oct. 1

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Information fusion with Bayes networks in computer-aided... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Information fusion with Bayes networks in computer-aided..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Information fusion with Bayes networks in computer-aided... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3866111

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.