International Journal of Biomedical Engineering and Clinical Science

| Peer-Reviewed |

Lung Detection and Segmentation Using Marker Watershed and Laplacian Filtering

Received: 04 August 2015    Accepted: 20 October 2015    Published: 20 October 2015
Views:       Downloads:

Share This Article

Abstract

This paper proposes a new speed approach for the segmentation of the lung images in order to detect and extract the tumor region. The approach consists of two main stages, which are the preprocessing stage, marker watershed stage and the tumor detection stage. The preprocessing consists of laplacian filtering to enhance edges and make the next stages more efficient. The marker watershed step applies the Sobel gradient function on the foreground and background markers to get the possible tumor region. The post processing stage consists of tumor detection and segmentation in which the area of the tumor is calculated. The results are done on a medical lung database obtained from Tishreen hospital (in Lattakia, Syria) which consists of 59 images from 10 persons. The result shows robustness of the system in detecting and segmenting tumor region in different depths. The designed GUI supplies user with tumor region and area, and time of each stage.

DOI 10.11648/j.ijbecs.20150102.12
Published in International Journal of Biomedical Engineering and Clinical Science (Volume 1, Issue 2, November 2015)
Page(s) 29-42
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Image Processing, Medical Image Processing, Lung Images, Segmentation, Tumor Detection

References
[1] Chiou YSP, Lure YMF, Ligomenides PA. Neural network image analysis and classification in hybrid lung nodule detection (HLND) system. In: Proceedings of the IEEE-SP Workshop on Neural Networks for Signal Processing, 1993. p.517-526.
[2] Hayashibe R, Asano N, Hirohata H, Okumura K, Kondo S, Handa S, Takizawa M, Sone S, Oshita S. An automatic lung cancer detection from X-ray images obtained through yearly serial mass survey. In: Proceedings of the International Conference on Image Processing, 1996. vol.1, p.343-346.
[3] Mori K, Hasegawa J, Toriwaki J, Anno H, Katada K. Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system. In: Proceedings of the 13th International onference on Pattern Recognition, 1996. vol.3, p.528-532.
[4] Zhou ZH, Chen SF, Chen ZQ. FANNC: A fast adaptive neural network classifier. Knowledge and Information Systems 2000; 2(1): 115-129.
[5] Disha Sharma, Gagandeep Jindal, “Identifying Lung Cancer Using Image Processing Techniques”, International Conference on Computational Techniques and Artificial Intelligence, 2011, pp: 115-120.
[6] Mokhled S. AL-TARAWNEH, “Lung Cancer Detection Using Image Processing Techniques”, Leonardo Electronic Journal of Practices and Technologies, Issue 20, January-June 2012, p. 147-158.
[7] Fatma Ayari1, Mekki Ksouri, Ali Alouani, “A computer based model for lung cancer analysis”, International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, September 2012, pp:438-447.
[8] Vivanti R1, Joskowicz L, Karaaslan OA, Sosna J., “Automatic lung tumor segmentation with leaks removal in follow-up CT studies“, Int J Comput Assist Radiol Surg. 2015 Jan 22,
[9] Masaood Hussain, Tabassum Ansari, Prarthana S.Gawas, Nabanita Nath Chowdhury, “Lung Cancer Detection Using Artificial Neural Network & Fuzzy Clustering, international journal of advanced research in computer and communication Engineering Vol 4, Issue 3, March 2015, pp.360-363.
[10] I. Ibraheem, Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma, International Journal of Biomedical Engineering and Clinical Science, Vol. 1, Issue 2, November 2015, s(1-9)
[11] Vishukumar S. Patel K. Shrivastava P. 2012 Implementation of Medical Image Enhancement Technique using Gabor Filter, International Journal of Current Engineering and Technology, V.2, 2.
[12] R. C. GONZALEZ and R. E. WOODS, “Digital Image Processing”, 2nd Edition, Prentice Hall, January 2002.
Author Information
  • Computer Scinces, Teshreen University, Lattakia, Syria

  • Mechanical and Electrical Engineering, Tishreen University, Lattakia, Syria

Cite This Article
  • APA Style

    Mariam Saii, Ali Mia. (2015). Lung Detection and Segmentation Using Marker Watershed and Laplacian Filtering. International Journal of Biomedical Engineering and Clinical Science, 1(2), 29-42. https://doi.org/10.11648/j.ijbecs.20150102.12

    Copy | Download

    ACS Style

    Mariam Saii; Ali Mia. Lung Detection and Segmentation Using Marker Watershed and Laplacian Filtering. Int. J. Biomed. Eng. Clin. Sci. 2015, 1(2), 29-42. doi: 10.11648/j.ijbecs.20150102.12

    Copy | Download

    AMA Style

    Mariam Saii, Ali Mia. Lung Detection and Segmentation Using Marker Watershed and Laplacian Filtering. Int J Biomed Eng Clin Sci. 2015;1(2):29-42. doi: 10.11648/j.ijbecs.20150102.12

    Copy | Download

  • @article{10.11648/j.ijbecs.20150102.12,
      author = {Mariam Saii and Ali Mia},
      title = {Lung Detection and Segmentation Using Marker Watershed and Laplacian Filtering},
      journal = {International Journal of Biomedical Engineering and Clinical Science},
      volume = {1},
      number = {2},
      pages = {29-42},
      doi = {10.11648/j.ijbecs.20150102.12},
      url = {https://doi.org/10.11648/j.ijbecs.20150102.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijbecs.20150102.12},
      abstract = {This paper proposes a new speed approach for the segmentation of the lung images in order to detect and extract the tumor region. The approach consists of two main stages, which are the preprocessing stage, marker watershed stage and the tumor detection stage. The preprocessing consists of laplacian filtering to enhance edges and make the next stages more efficient. The marker watershed step applies the Sobel gradient function on the foreground and background markers to get the possible tumor region. The post processing stage consists of tumor detection and segmentation in which the area of the tumor is calculated. The results are done on a medical lung database obtained from Tishreen hospital (in Lattakia, Syria) which consists of 59 images from 10 persons. The result shows robustness of the system in detecting and segmenting tumor region in different depths. The designed GUI supplies user with tumor region and area, and time of each stage.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Lung Detection and Segmentation Using Marker Watershed and Laplacian Filtering
    AU  - Mariam Saii
    AU  - Ali Mia
    Y1  - 2015/10/20
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ijbecs.20150102.12
    DO  - 10.11648/j.ijbecs.20150102.12
    T2  - International Journal of Biomedical Engineering and Clinical Science
    JF  - International Journal of Biomedical Engineering and Clinical Science
    JO  - International Journal of Biomedical Engineering and Clinical Science
    SP  - 29
    EP  - 42
    PB  - Science Publishing Group
    SN  - 2472-1301
    UR  - https://doi.org/10.11648/j.ijbecs.20150102.12
    AB  - This paper proposes a new speed approach for the segmentation of the lung images in order to detect and extract the tumor region. The approach consists of two main stages, which are the preprocessing stage, marker watershed stage and the tumor detection stage. The preprocessing consists of laplacian filtering to enhance edges and make the next stages more efficient. The marker watershed step applies the Sobel gradient function on the foreground and background markers to get the possible tumor region. The post processing stage consists of tumor detection and segmentation in which the area of the tumor is calculated. The results are done on a medical lung database obtained from Tishreen hospital (in Lattakia, Syria) which consists of 59 images from 10 persons. The result shows robustness of the system in detecting and segmenting tumor region in different depths. The designed GUI supplies user with tumor region and area, and time of each stage.
    VL  - 1
    IS  - 2
    ER  - 

    Copy | Download

  • Sections