Automated method and system for the detection of lung...

Image analysis – Applications – Biomedical applications

Reexamination Certificate

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Reexamination Certificate

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10767342

ABSTRACT:
A method, system, and computer program product for detecting at least one nodule in a medical image of a subject, including identifying, in the medical image, an anatomical region corresponding to at least a portion of an organ of interest; filtering the medical image to obtain a difference image; detecting, in the difference image, a first plurality of nodule candidates within the anatomical region; calculating respective nodule feature values of the first plurality of nodule candidates based on pixel values of at least one of the medical image and the difference image; removing false positive nodule candidates from the first plurality of nodule candidates based on the respective nodule feature values to obtain a second plurality of nodule candidates; and determining the at least one nodule by classifying each of the second plurality of nodule candidates as a nodule or a non-nodule based on at least one of the pixel values and the respective nodule feature values. True-positive nodules are identified using linear discriminant analysis and/or a Multi-MTANN.

REFERENCES:
patent: 6141437 (2000-10-01), Xu et al.
patent: 6754380 (2004-06-01), Suzuki et al.
patent: 2002/0141627 (2002-10-01), Romsdahl et al.
patent: 2002/0172403 (2002-11-01), Doi et al.
patent: 2003/0028401 (2003-02-01), Kaufman et al.
patent: 2003/0095696 (2003-05-01), Reeves et al.
patent: 2004/0101181 (2004-05-01), Giger et al.
Masahiro Kaneko, et al., “Peripheral Lung Cancer: Screening and Detection with Low-Dose Spiral CT Versus Radiography,” Radiology 201, 798-802 (1996).
Shusuke Sone, et al., “Mass Screening for Lung Cancer with Mobile Spiral Computed Tomography Scanner,” Lancet 351, 1242-1245 (1998).
Stefan Diederich, et al., “Pulmonary Nodules: Experimental and Clinical Studies at Low-Dose CT,” Radiology 213, 289-298 (1999).
Claudia I. Henschke, et al., “Early Lung Cancer Action Project: Overall Design and Findings form Baseline Screening,” Lancet 354, 99-105 (1999).
Takeshi Nawa, et al., “Lung Cancer Screening Using Low-Dose Spiral CT: Results of Baseline and 1 Year Follow-up Studies,” Chest 122, 15-20 (2002).
Shinji Yamamoto, et al., “Image Processing for Computer-Aided Diagnosis of Lung Cancer by CT (LSCT),” Systems and Computers in Japan 25, 67-79 (1994).
Y. Ukai, et al., “Computer Aided Diagnosis System for Lung Cancer Based on Retrospective Helical CT Image,” Proc. SPIE 3979, 1028-1039 (2000).
Samuel G. Armato III., et al., “Computerized Detection of Pulmonary Nodules on CT Scans,” RadioGraphics 19, 1303-1311 (1999).
Samuel G. Armato III., et al., “Automated Detection of Lung Nodules in CT Scans: Preliminary Results,” Med. Phys. 28, 1552-1561 (2001).
Samuel G. Armoto III., et al., “Lung Cancer: Performance of Automated Lung Nodule Detection Applied to Cancers Missed in a CT Screening Program,” Radiology 225, 685-692 (2002).
Dag Wormanns, et al., “Automatic Detection of Pulmonary Nodules at Spiral CT: Clinical Application of a Computer-Aided Diagnosis System,” Eur. Radiol. 12, 1052-1057 (2002).
Metin N. Gurcan, et al., “Lung Nodule Detection on Thoracic Computed Tomography Images: Preliminary Evaluation of a Computer-Aided Diagnosis System,” Med. Phys. 29, 2552-2558 (2002).
Matthew S. Brown., et al., “Lung Micronodules: Automated Method for Detection at Thin-Section CT—Initial Experience,” Radiology 226, 256-262 (2003).
Maryellen Lissak Giger, et al., “Image Feature Analysis and Computer-Aided Diagnosis in Digital Radiography: Automated Detection of Nodules in Peripheral Lung Fields,” Med Phys. 15, 158-166 (1988).
Xin-Wei Xu, et al., “Development of an Improved CAD Scheme for Automated Detection of Lung Nodules in Digital Chest Images,” Med. Phys. 24, 1395-1403 (1997).
Feng Li, et al., “Lung Cancers Missed at Low-Dose Helical CT Screening in a General Population: Comparison of Clinical, Histopathologic, and Imaging Findings,” Radiology 225, 673-683 (2002).
Kenji Suzuki, et al., “Massive Training Artificial Neural Network (MTANN) for Reduction of False Positives in Computerized Detection of Lung Nodules in Low-Dose Computed Tomography,” Med, Phys., 1602-1617 (2003).
Kenji Suzuki, et al., “Effect of a Small Number of Training Cases on the Performance of Massive Training Artificial Neural Network (MTANN) for Reduction of False Positives in Computerized Detection of Lung Nodules in Low-Dose CT,” SPIE Proc. 5032, 1355-1366 (2003).
Masahito Aoyama, et al., “Automated Computerized Scheme for Distinction Between Benign and Malignant Solitary Pulmonary Nodules on Chest Images,” Med Phys. 29, 701-708 (2002).
Berkman Sahiner, et al., “Computerized Characterization of Masses on Mammograms: The Rubber Band Straightening Transform and Texture Analysis,” Med. Phys. 24, 516-526 (1998).

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