Image analysis – Applications – Biomedical applications
Reexamination Certificate
2007-07-02
2011-12-13
Wu, Jingge (Department: 2624)
Image analysis
Applications
Biomedical applications
C382S100000, C382S128000, C382S134000
Reexamination Certificate
active
08077958
ABSTRACT:
The present invention is a computer-aided pathological diagnosis method for the classification of cancer cells in a tissue specimen based on a digital cellular image of the tissue specimen. The method of the present invention includes the steps of, extracting the histological characteristic features of the cellular image using preprocessing algorithms having adaptive strategies to enhance the cellular image, declustering the extracted histological characteristic features of the cellular image to isolate the individual cells and the nuclei inside the cells, segmenting the declustered cellular image, labeling the segmented cellular image and classifying the cells in the labeled cellular image as cancer cells or non-cancer cells.
REFERENCES:
patent: 5299269 (1994-03-01), Gaborski et al.
patent: 6728405 (2004-04-01), Kazato et al.
patent: 7761240 (2010-07-01), Saidi et al.
patent: 2003/0184730 (2003-10-01), Price
patent: 2003/0194115 (2003-10-01), Kaufhold et al.
patent: 2005/0266395 (2005-12-01), Gholap et al.
patent: 2006/0036372 (2006-02-01), Yener et al.
patent: 2006/0045323 (2006-03-01), Ateya
patent: 2006/0280364 (2006-12-01), Ma et al.
patent: 2007/0099219 (2007-05-01), Teverovskiy et al.
patent: 2007/0250129 (2007-10-01), Van Oort
Sammouda et al, “Cancerous nuclei detection on digitized pathological lung color image”, Journal of Biomedical Informatics 35, 92-98, 2002.
Qian et al (Image feature extraction for mass detection in digital mammography: influence of wavelet analysis, Med Phys Mar. 1999).
Qian W et al, “Fragmentary Window Filtering for Multiscale Lung Nodule Detection: Preliminary Study,” Academic Radiology, Apr. 1998.
Ward,I.M. & Chen, J. “Histone H2AX is phosphorylated in an ATR-dependent manner in response to replicational stress.” J. Biol. Chem. 276, 47759-47762, 2001.
Mouroutis, T, Roberts, SJ, Bharath, A. A. “Robust cell nuclei segmentation using statistical modeling.” Bio. Imaging 1998; 6:79-91.
Simon, I., Pound, C. R., Partin, A. W., Clemens, J. Q., and Christenbarry, W. A. “Automated image analysis system for detecting boundaries of live prostate cancer cells.” Cytometry 1998; 31:287-94.
Wu, H. S., Barba, J., and Gil, J. “Iterative thresholding for segmentation of cells from noisy images.” J Microsc 2000; 197:296-304.
Theocharis, S., Kanelli, H., Politi, E., et al. “Expression of peroxisome proliferator activated receptor-gamma in non-small cell lung carcinoma: correlation with histological type and grade.” Lung Cancer (Lung cancer (Amsterdam, Netherlands)) 2002; 36(3): 249-55.
Zhou, Z. H., Jiang, Y., Yang, Y. B., et al. “Lung cancer cell identification based on artificial neural network ensembles.” Artif Intell Med (Artificial intelligence in medicine.) 2002; 24(1): 25-36.
Bergqvist M; Brattström D; Larsson A, and others. “The role of circulating anti-p53 antibodies in patients with advanced non-small cell lung cancer and their correlation to clinical parameters and survival.” BMC Cancer (BMC cancer [electronic resource].) 2004; 4(1): 66.
W. Qian, L. P. Clarke, M. Kallergi, and R. A. Clark. “Tree-structured nonlinear filters in digital mammography,” IEEE Trans. Med. Imag., vol. 13(1), 25-36, 1994.
W. Qian, M. Kallergi and L. P. Clarke. “Order Statistic-Neural Network Hybrid Filters for Gamma Camera Image Restoration.” IEEE Trans. in Medical Imaging, pp. 56-65, Mar. 1993.
W. Qian, and L. P. Clarke. “Wavelet-based neural network with fuzzy-logic adaptivity for nuclear image restoration,” Proceedings of the IEEE, Special Issue on Applications of Neural Networks, Invited paper, vol. 84, No. 10, 1996.
B. Y. Zheng, W. Qian, and L. P. Clarke. “Digital mammography: MF-based NN for automatic detection of microcalcifications,” IEEE Trans. on Medical Imaging, pp. 589-597, Oct. 1996.
Qian, W., Li, L., and Clarke, L. P. 1998. “Image Feature Extraction for Mass Detection in Digital Mammography: Influence of Wavelet Analysis.” Am. Assoc. Phys. Med. vol. 26 pp. 402-408.
Qian, W., Sun, A., Song, D., and Clark, R. A. 2001. “Digital Mammography: Wavelet Transform and Kalman-Filtering Neural Network in Mass Segmentation and Detection.” Academic Radiology, vol. 8, No. 11, pp. 1074-1082.
Zajac, A., Song, D., and Zhukov, T. 2007. Protein Microarrays and Quantum Dot Probes for Early Cancer Detection. Colloids and Surfaces. 58 (2). pp. 309-314.
Qian, W., Clarke, L. P., Zheng, B., Kallergi, M., and Clark, R. 1995. “Computer Assisted Diagnosis for Digital Mammography.” IEEE Engineering in Medicine and Biology. Sep./Oct. 1995 issue. pp. 561-569.
Qian, W., Zhukov, T., Song, D., and Tockman, M. S. 2007. Computerized Analysis of Cellular Features and Biomarkers for Cytologic Diagnosis of Early Lung Cancer. Analytical and Quantitative Cytology and Histology. vol. 29, No. 2/Apr. 2007. pp. 103-111.
Yang, F.; Jiang, T. 2001. Cell Image Segmentation with Kernel-Based Dynamic Clustering and an Ellipsoidal Cell Shape Model. Journal of Biomedical Informatics. 34: 67-73.
Arman, F.; Pearce, J.A. 1990. Unsupervised Classification of Cell Images Using Pyramid Node Linking. IEEE Transactions on Biomedical Engineering. 37: 647-650.
Jiang, T.; Yang, F. 2002. An Evolutionary Tabu Search for Cell Image Segmentation. IEEE Transactions on Systems , Man, and Cybernetics—Part B: Cybernetics. 32: 675-678.
Comaniciu, D.; Meer, P. 2002. Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 24: 603-619.
Thiran, J. 1996. Morphological Feature Extraction for the Classification of Digital Images of Cancerous Tissues. IEEE Transactions on Biomedical Engineering. 43: 1011-1020.
Beil, M.; Irinopoulou, T.; Vassy, J.; Wolf, G. 1995. A dual approach to structural texture analysis in microscopic cell images. Computer Methods and Programs in Biomedicine. 48: 211-219.
Dillon, D.A.; Howe, C. L; Bosari, S.; Costa, J. “The Molecular Biology of Breast Cancer: Accelerating Clinical Applications.” Critical Reviews in Oncogenesis. 1998; 9(2):125-140.
Burma, S.; Chen, B.P.; Murphy, M.; Kurimasa, A. and Chen, D.J. “ATM phosphorylates histone H2AX in response to DNA double-strand breaks”. J. Biol. Chem. Nov. 9, 2001; 276(45):42462-42467.
Sedelnikova, O.A.; Pilch, D.R.; Redon, C.; Bonner, W.M. “Histone H2AX in DNA damage and repair.” Cancer Biol Ther. May/Jun. 2003; 2(3):233-235, 2003.
Kayser, K.; Kayser, G.; Bovin, N.V.; Gabius, H. J. “Quantitative evaluation of ligandohistochemistry with cytoplasmatic markers: program structure and application to lung carcinomas.” Electronic journal of pathology and histology. 1995;1(4).
Garrido, A.; Perez De La Blanca, N. “Applying deformable templates for cell image segmentation.” Pattern Recognit 2000; 33:821-32.
Wu, H.S.; Barba, J.; Gil, J. “A parametric fitting algorithm for segmentation of cell images.” IEEE Trans Biomed Eng Mar. 1998; 45(3):400-7.
Kapur, J.N.; Sahoo, P.K.; Wong, A.K.C. “A new method for gray-level picture thresholding using the entropy of the histogram.” Comput Vis Graph Image Process 1985; 29:273-85.
Kittler, J.; Illingworth, J. “Minimum error thresholding.” Pattern Recognit 1986;19(1):41-47.
Hamasaki, M.; Kamma, H.; Wu, W.; Kaneko, S.; Fujiwara, M.; Satoh, H.; Haraoka, S.; Kikuchi, M.; Shirakusa, T. “Expression of hnRNP B1 in four major histological types of lung cancers.” Anticancer Res. 2001; 21 (2A): 979-84.
Yamaji, H.; Iizasa, T.; Koh, E.; Suzuki, M.; Otsuji, M.; Chang, H.; Motohashi, S.; Yokoi, S.; Hiroshima, K.; Tagawa, M.; Nakayama, T.; Fujisawa, T. “Correlation between interleukin 6 production and tumor proliferation in non-small cell lung cancer.” Cancer Immunol Immunother (Cancer immunology, immunotherapy : CII.) 2004; 53: 786-92.
Traynor, A.M.; Schiller, J.H. “Systemic treatment of advanced non-small cell lun
Qian Wei
Song Dansheng
Zhukov Tatyana A.
Dunn Courtney M.
Sauter Molly L.
Smith & Hopen , P.A.
University of South Florida
Wu Jingge
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