Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans

Mahmoud Saleh Jawarneh; Mohammed Said Abual-Rub

doi:doi:10.11648/j.ijiis.20160501.12

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Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans

Mahmoud Saleh Jawarneh, Mohammed Said Abual-Rub

Published in International Journal of Intelligent Information Systems (Volume 5, Issue 1)

Received: 1 January 2016 Accepted: 11 January 2016 Published: 4 February 2016

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Abstract

The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate segmentation would allow physicians to analyze and visualize the human structures and re-plan radiation therapy and surgery. This paper introduces a knowledge system based on different sources of medical knowledge to automate medical image segmentation through active contour methods. The way of getting benefit of the knowledge provided by medical atlas, expert’s rules, image features, image multiple views and image Meta data introduced by this knowledge system. We classify the system in different domains in way can be manage properly to guide active contour segmentation methods for abdominal CT scans. The obtained results are very promising showing significant improvements over other methods where the volume measurements error is 7% and the processing time was improved by 68%.

Published in	International Journal of Intelligent Information Systems (Volume 5, Issue 1)
DOI	10.11648/j.ijiis.20160501.12
Page(s)	5-16
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), 2016. Published by Science Publishing Group

Keywords

Knowledge-Based System, Medical Knowledge, Active Contour Image Segmentation, Automated Processes

References

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APA Style

Mahmoud Saleh Jawarneh, Mohammed Said Abual-Rub. (2016). Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans. International Journal of Intelligent Information Systems, 5(1), 5-16. https://doi.org/10.11648/j.ijiis.20160501.12

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ACS Style

Mahmoud Saleh Jawarneh; Mohammed Said Abual-Rub. Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans. Int. J. Intell. Inf. Syst. 2016, 5(1), 5-16. doi: 10.11648/j.ijiis.20160501.12

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AMA Style

Mahmoud Saleh Jawarneh, Mohammed Said Abual-Rub. Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans. Int J Intell Inf Syst. 2016;5(1):5-16. doi: 10.11648/j.ijiis.20160501.12

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@article{10.11648/j.ijiis.20160501.12,
  author = {Mahmoud Saleh Jawarneh and Mohammed Said Abual-Rub},
  title = {Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {1},
  pages = {5-16},
  doi = {10.11648/j.ijiis.20160501.12},
  url = {https://doi.org/10.11648/j.ijiis.20160501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160501.12},
  abstract = {The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate segmentation would allow physicians to analyze and visualize the human structures and re-plan radiation therapy and surgery. This paper introduces a knowledge system based on different sources of medical knowledge to automate medical image segmentation through active contour methods. The way of getting benefit of the knowledge provided by medical atlas, expert’s rules, image features, image multiple views and image Meta data introduced by this knowledge system. We classify the system in different domains in way can be manage properly to guide active contour segmentation methods for abdominal CT scans. The obtained results are very promising showing significant improvements over other methods where the volume measurements error is 7% and the processing time was improved by 68%.},
 year = {2016}
}

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TY  - JOUR
T1  - Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans
AU  - Mahmoud Saleh Jawarneh
AU  - Mohammed Said Abual-Rub
Y1  - 2016/02/04
PY  - 2016
N1  - https://doi.org/10.11648/j.ijiis.20160501.12
DO  - 10.11648/j.ijiis.20160501.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 5
EP  - 16
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20160501.12
AB  - The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate segmentation would allow physicians to analyze and visualize the human structures and re-plan radiation therapy and surgery. This paper introduces a knowledge system based on different sources of medical knowledge to automate medical image segmentation through active contour methods. The way of getting benefit of the knowledge provided by medical atlas, expert’s rules, image features, image multiple views and image Meta data introduced by this knowledge system. We classify the system in different domains in way can be manage properly to guide active contour segmentation methods for abdominal CT scans. The obtained results are very promising showing significant improvements over other methods where the volume measurements error is 7% and the processing time was improved by 68%.
VL  - 5
IS  - 1
ER  -

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Author Information

Mahmoud Saleh Jawarneh

Computer Science Department, Al-Imam Muhammad Ibn Saud Islamic University, Ahsa, Saudi Arabia
Mohammed Said Abual-Rub

Computer Science Department, Al-Imam Muhammad Ibn Saud Islamic University, Ahsa, Saudi Arabia

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Plain Text BibTeX RIS

APA Style

Mahmoud Saleh Jawarneh, Mohammed Said Abual-Rub. (2016). Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans. International Journal of Intelligent Information Systems, 5(1), 5-16. https://doi.org/10.11648/j.ijiis.20160501.12

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ACS Style

Mahmoud Saleh Jawarneh; Mohammed Said Abual-Rub. Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans. Int. J. Intell. Inf. Syst. 2016, 5(1), 5-16. doi: 10.11648/j.ijiis.20160501.12

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AMA Style

Mahmoud Saleh Jawarneh, Mohammed Said Abual-Rub. Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans. Int J Intell Inf Syst. 2016;5(1):5-16. doi: 10.11648/j.ijiis.20160501.12

Copy | Download

@article{10.11648/j.ijiis.20160501.12,
  author = {Mahmoud Saleh Jawarneh and Mohammed Said Abual-Rub},
  title = {Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {1},
  pages = {5-16},
  doi = {10.11648/j.ijiis.20160501.12},
  url = {https://doi.org/10.11648/j.ijiis.20160501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160501.12},
  abstract = {The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate segmentation would allow physicians to analyze and visualize the human structures and re-plan radiation therapy and surgery. This paper introduces a knowledge system based on different sources of medical knowledge to automate medical image segmentation through active contour methods. The way of getting benefit of the knowledge provided by medical atlas, expert’s rules, image features, image multiple views and image Meta data introduced by this knowledge system. We classify the system in different domains in way can be manage properly to guide active contour segmentation methods for abdominal CT scans. The obtained results are very promising showing significant improvements over other methods where the volume measurements error is 7% and the processing time was improved by 68%.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans
AU  - Mahmoud Saleh Jawarneh
AU  - Mohammed Said Abual-Rub
Y1  - 2016/02/04
PY  - 2016
N1  - https://doi.org/10.11648/j.ijiis.20160501.12
DO  - 10.11648/j.ijiis.20160501.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 5
EP  - 16
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20160501.12
AB  - The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate segmentation would allow physicians to analyze and visualize the human structures and re-plan radiation therapy and surgery. This paper introduces a knowledge system based on different sources of medical knowledge to automate medical image segmentation through active contour methods. The way of getting benefit of the knowledge provided by medical atlas, expert’s rules, image features, image multiple views and image Meta data introduced by this knowledge system. We classify the system in different domains in way can be manage properly to guide active contour segmentation methods for abdominal CT scans. The obtained results are very promising showing significant improvements over other methods where the volume measurements error is 7% and the processing time was improved by 68%.
VL  - 5
IS  - 1
ER  -

Copy | Download