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Segmentation of Lungs from CT Scan Images for Early Diagnosis of Lung Cancer

Authors: Nisar Ahmed Memon, Anwar Majid Mirza, S.A.M. Gilani

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,

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056533

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References:


[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.