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.
| Published in | International Journal of Biomedical Engineering and Clinical Science (Volume 1, Issue 2) |
| DOI | 10.11648/j.ijbecs.20150102.12 |
| 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), 2015. Published by Science Publishing Group |
Image Processing, Medical Image Processing, Lung Images, Segmentation, Tumor Detection
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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
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
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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://article.sciencepublishinggroup.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}
}
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 -
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