Segmentation of Lungs from CT Scan Images for Early Diagnosis of Lung Cancer
Segmentation is an important step in medical image
analysis and classification for radiological evaluation or computer
aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT
generally first segment the area of interest (lung) and then analyze
the separately obtained area for nodule detection in order to
diagnosis the disease. For normal lung, segmentation can be
performed by making use of excellent contrast between air and
surrounding tissues. However this approach fails when lung is
affected by high density pathology. Dense pathologies are present in
approximately a fifth of clinical scans, and for computer analysis
such as detection and quantification of abnormal areas it is vital that
the entire and perfectly lung part of the image is provided and no
part, as present in the original image be eradicated. In this paper we
have proposed a lung segmentation technique which accurately
segment the lung parenchyma from lung CT Scan images. The
algorithm was tested against the 25 datasets of different patients
received from Ackron Univeristy, USA and AGA Khan Medical
University, Karachi, Pakistan.
[1] M. A. Khawaja, Muhammed Zaheer Aziz, Nadeem Iqbal, "Effectual
Lung Segmentation for CAD Systems Using CT Scan Images",
Proceedings of IEEE, INMIC Conference, FAST Lahore, 2004.
[2] Robin N. Strickland, "Image Processing Techniques for Tumor
Detection", Marcel Dekker Inc. New York, 2002.
[3] R Wicmker PhD, P. Rogalla MD, T Blaffert PhD, "Aspects of
Computer-aided detection (CAD) and volumetry of pulmonary nodules
using multislice CT", The British Journal of Radiology (BJR), vol. 78,
pp. 46-56, 2005.
[4] Malin Dollinger, "Every one-s Guide to Cancer therapy; How Cancer
is Diagnosed, Treated and Manged", 4th Edition, Andrews McMeel
Publishing Kansas, USA, 2002.
[5] Atam P. Dhawan, "Medical Image Analysis", IEEE press series in
Biomedical Engineering, John Wiley & Sons. Inc. Publications, 2003.
[6] Jadwiga Kogowska, "Overview and Fundamental of Medical Image
Segmentation", Hand Book of Medical Imaging, Academic Press, San
Diego, pp. 69-85, 2000.
[7] Samuel G. Armato III, Maryellen L. Giger and Catherine J. Moran,
"Computerized Detection of Pulmonary Nodules on CT Scans",
RadioGraphics, vol. 19, pp. 1303-1311, 1999.
[8] Julian Kerr, "The TRACE method for Segmentation of Lungs from
Chest CT images by Deterministic Edge Linking", University of New
South Wales, Department of Artificial Intelligence, Australia, May
2000.
[9] Shiying Hu, Eric A.Huffman, and Joseph M. Reinhardt, "Automatic
Lung Segementation for Accurate Quantitiation of Volumetric X-Ray
CT images", IEEE Transactions on Medical Imaging, vol. 20, No. 6,
June 2001.
[10] Riccardo Boscolo, Mathew S. Brown, Michael F. McNitt-Gray,
"Medical Image Segmentation with Knowledge-guided Robust Active
Contours", Radiographics, vol. 22, pp. 437-448, 2002.
[11] Ayman El-Baz, Aly A. Farag,
Ph.D., Robert Falk, M.D.
and Renato La
Rocc," Detection, Visualization, and Identification of Lung
Abnormalities in Chest Spiral CT Scans: Phase 1", International
Conference on Biomedical Engineering, Cairo, Egypt, 12-1-2002.
[12] Binsheng Zhao, Gordon Gamsu, Michelle S. Ginsberg, "Automatic
detection of small lung nodules on CT utilizing a local density
maximum algorithm", Journal of Applied Clinical Medical Physics, vol.
4, No. 3, summer 2003.
[13] Ayman El-Baz, Aly A. Farag,
Ph.D., Robert Falk, M.D.
and Renato La
Rocc," A unified approach for detection, visualization, and
identification of lung abnormalities in chest spiral CT scans", proc.
Computer Assisted Radiology and Surgery, London, 2003.
[14] Shu-Yen Wan, William E. Higgins, "Symmetric Region Growing",
IEEE Transactions on Image Processing, Vol. 12, No. 8 August 2003.
[1] M. A. Khawaja, Muhammed Zaheer Aziz, Nadeem Iqbal, "Effectual
Lung Segmentation for CAD Systems Using CT Scan Images",
Proceedings of IEEE, INMIC Conference, FAST Lahore, 2004.
[2] Robin N. Strickland, "Image Processing Techniques for Tumor
Detection", Marcel Dekker Inc. New York, 2002.
[3] R Wicmker PhD, P. Rogalla MD, T Blaffert PhD, "Aspects of
Computer-aided detection (CAD) and volumetry of pulmonary nodules
using multislice CT", The British Journal of Radiology (BJR), vol. 78,
pp. 46-56, 2005.
[4] Malin Dollinger, "Every one-s Guide to Cancer therapy; How Cancer
is Diagnosed, Treated and Manged", 4th Edition, Andrews McMeel
Publishing Kansas, USA, 2002.
[5] Atam P. Dhawan, "Medical Image Analysis", IEEE press series in
Biomedical Engineering, John Wiley & Sons. Inc. Publications, 2003.
[6] Jadwiga Kogowska, "Overview and Fundamental of Medical Image
Segmentation", Hand Book of Medical Imaging, Academic Press, San
Diego, pp. 69-85, 2000.
[7] Samuel G. Armato III, Maryellen L. Giger and Catherine J. Moran,
"Computerized Detection of Pulmonary Nodules on CT Scans",
RadioGraphics, vol. 19, pp. 1303-1311, 1999.
[8] Julian Kerr, "The TRACE method for Segmentation of Lungs from
Chest CT images by Deterministic Edge Linking", University of New
South Wales, Department of Artificial Intelligence, Australia, May
2000.
[9] Shiying Hu, Eric A.Huffman, and Joseph M. Reinhardt, "Automatic
Lung Segementation for Accurate Quantitiation of Volumetric X-Ray
CT images", IEEE Transactions on Medical Imaging, vol. 20, No. 6,
June 2001.
[10] Riccardo Boscolo, Mathew S. Brown, Michael F. McNitt-Gray,
"Medical Image Segmentation with Knowledge-guided Robust Active
Contours", Radiographics, vol. 22, pp. 437-448, 2002.
[11] Ayman El-Baz, Aly A. Farag,
Ph.D., Robert Falk, M.D.
and Renato La
Rocc," Detection, Visualization, and Identification of Lung
Abnormalities in Chest Spiral CT Scans: Phase 1", International
Conference on Biomedical Engineering, Cairo, Egypt, 12-1-2002.
[12] Binsheng Zhao, Gordon Gamsu, Michelle S. Ginsberg, "Automatic
detection of small lung nodules on CT utilizing a local density
maximum algorithm", Journal of Applied Clinical Medical Physics, vol.
4, No. 3, summer 2003.
[13] Ayman El-Baz, Aly A. Farag,
Ph.D., Robert Falk, M.D.
and Renato La
Rocc," A unified approach for detection, visualization, and
identification of lung abnormalities in chest spiral CT scans", proc.
Computer Assisted Radiology and Surgery, London, 2003.
[14] Shu-Yen Wan, William E. Higgins, "Symmetric Region Growing",
IEEE Transactions on Image Processing, Vol. 12, No. 8 August 2003.
@article{"International Journal of Medical, Medicine and Health Sciences:51272", author = "Nisar Ahmed Memon and Anwar Majid Mirza and S.A.M. Gilani", title = "Segmentation of Lungs from CT Scan Images for Early Diagnosis of Lung Cancer", abstract = "Segmentation is an important step in medical image
analysis and classification for radiological evaluation or computer
aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT
generally first segment the area of interest (lung) and then analyze
the separately obtained area for nodule detection in order to
diagnosis the disease. For normal lung, segmentation can be
performed by making use of excellent contrast between air and
surrounding tissues. However this approach fails when lung is
affected by high density pathology. Dense pathologies are present in
approximately a fifth of clinical scans, and for computer analysis
such as detection and quantification of abnormal areas it is vital that
the entire and perfectly lung part of the image is provided and no
part, as present in the original image be eradicated. In this paper we
have proposed a lung segmentation technique which accurately
segment the lung parenchyma from lung CT Scan images. The
algorithm was tested against the 25 datasets of different patients
received from Ackron Univeristy, USA and AGA Khan Medical
University, Karachi, Pakistan.", keywords = "Computer Aided Diagnosis, Medical ImageProcessing, Region Growing, Segmentation, Thresholding,", volume = "2", number = "8", pages = "249-6", }