Knowledge-based hierarchical method for detecting regions of...

Image analysis – Pattern recognition – Feature extraction

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Reexamination Certificate

active

10142175

ABSTRACT:
A knowledge-based hierarchical method for detecting regions of interests (ROIs) uses prior knowledge of the targets and the image resolution in detecting ROIs. The result produces ROIs that contain only one target that is completely enclosed within the ROI. The detected ROI can conform to the shape of the target even if the target is of irregular shape. Furthermore, the method works well with images that contain connected targets or targets broken into pieces. The method is not sensitive to contrast levels and is robust to noise. Thus, this method effectively detects ROIs in common real world imagery that has a low resolution without costly processing while providing fast and robust results.

REFERENCES:
patent: 5887081 (1999-03-01), Bantum
patent: 6614928 (2003-09-01), Chung et al.
patent: 6678404 (2004-01-01), Lee et al.
patent: 6801665 (2004-10-01), Atsumi et al.
patent: 6804403 (2004-10-01), Wang et al.
patent: 6816627 (2004-11-01), Ockman
patent: 2003/0161523 (2003-08-01), Moon et al.
Marshall, David, “Edge Detection,” http://www.cs.cf.ac.uk/Dave/Vision—lecture
ode24.html, 1997.
H. Lin, J. Si, and G. Abousleman, “Knowledge-Based Hierarchical Region-of-Interest Detection,” Proc. IEEE Int'l Conf. Accoustics, Speech and Signal Processing, vol. 4, pp. 3628-3631, Orlando, Florida, May 13-17, 2002.
T.M. Stough and C.E. Brodley, “Focusing Attention on Objects of Interest Using Multiple Matched Filters,” IEEE Trans. Image Processing, vol. 10, No. 3, pp. 419-426, Mar. 2001.
A. Mohan, C. Papageorgiou, and T. Poggio, “Example-Based Object Detection in Images by Components,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, No. 4, pp. 349-361, Apr. 2001.
C.M. Privitera and L.W. Stark, “Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, No. 9, pp. 970-982, Sep. 2000.
T. Zhang, J. Peng, and Z. Li, “An Adpative Image Segmentation Method with Visual Nonlinearity Characteristics,” IEEE Trans. on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 26, No. 4, pp. 619-627, Aug. 1996.
N.R. pal, T.C. Cahoon, J.C. Bezdek, and K. Pal, “A New Approach to Target Recognition for LADAR Data,” IEEE Trans. Fuzzy Systems, vol. 9, No. 1, pp. 44-52, Feb. 2001.
E. Pietka, A. Gertych, S. Pospiech, F. Cao, and H.K. Huang, “Computer-Assisted Bone Age Assessment: Image Preprocessing and Epiphyseal/Metaphyseal ROI Extraction,” IEEE Trans. Image Processing, vol. 20, No. 8, pp. 715-729, Aug. 2001.
E. Pietka, L. Kaabi, M.L. Kuo, and H.K. Huang, “Feature Extraction in Carpal-Bone Analysis,” IEEE Trans. Medical Imaging, vol. 12, No. 1, pp. 44-49, Mar. 1993.
B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M.A. Helvie, D.D. Adler, and M.M. Goodsitt, “Classification of Mass and Normal Breast Tissue: A Convolution Neural Network Classifier with Spatial Domain and Texture Images,” IEEE Trans. Medical Imaging, vol. 15, No. 5, pp. 598-610, Oct. 1996.
E. Klotz, W.A. Kalender, and T. Sandor, “Automated Definition and Evaluation of Anatomical ROI's for Bone Mineral Determination by QCT,” IEEE Trans. Medical Imaging, vol. 8, No. 4, pp. 371-376, Dec. 1989.
J.K. Kim and H.W. Park, “Statistical Textural Features for Detection of Microcalcifications in Digitized Mammograms,” IEEE Trans. Medical Imaging, vol. 18, No. 3, pp. 231-238, Mar. 1999.
E.V.R.D. Bella, G.T. Gullberg, A.B. Barclay, and R.L. Eisner, “Automated Region Selection for Analysis of Dynamic Cardiac SPECT Data,” IEEE Trans. Nuclear Science, vol. 44, No. 3, pp. 1355-1361, Jun. 1997.
J.L. Solka, D.J. Marchette, B.C. Wallet, V.L. Irwin, and G.W. Rogers, “Identification of Man-Made Regions in Unmanned Aerial Vehicle Imagery and Videos,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, No. 8, pp. 852-857, Aug. 1998.
J. Li, J.Z. Wang, R.M. Gray, and G. Wiederhold, “Multiresolution Object-of-Interest Detection for Images with Low Depth of Field,” Proc. Int'l Conf. Image Analysis and Processing, pp. 32-37, Venice, Italy, 1999.
M.N. Gurcan, Y. Yardimci, A.E. Cetin, and R. Ansari, “Detection of Microcalcifications in Mammograms Using Higher Order Statistics,” IEEE Signal Processing Letters, vol. 4, No. 8, pp. 213-216, Aug. 1997.
Christoyianni, E. Dermatas, and G. Kokkinakis, “Automatic Detection of Abnormal Tissue in Mammography,” Proc. 2001 IEEE Intl. Conf. on Image Processing, vol. 2, pp. 877-880, Thessaloniki, Greece, 2001.
W.E. Polakowski, D.A. Cournoyer, S.K. Rogers, M.P. DeSimio, D.W. Ruck, J.W. Hoffmeister, and R.A. Raines, “Computer-Aided Breast Cancer Detection and Diagnosis of Masses Using Difference of Gaussians and Derivative-Based Feature Saliency,” IEEE Trans. Medical Imaging, vol. 16, No. 6, pp. 811-819, Dec. 1997.
Jouan, J. Verdenet, J.C. Cardot, M. Baud, and J. Duvernoy, “Automated Detection of the Left Ventricular Region of Interest by Means of the Extraction of Typical Behaviors in Cardiac Radionuclide Angiographies,” IEEE Trans. Medical Imaging, vol. 9, No. 1, pp. 5-10, Mar. 1990.
M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image Coding Using Wavelet Transform,” IEEE Trans. Image Processing, vol. 1, No. 2, pp. 205-220, Apr. 1992.

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

Knowledge-based hierarchical method for detecting regions of... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Knowledge-based hierarchical method for detecting regions of..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Knowledge-based hierarchical method for detecting regions of... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3840512

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