Volume 5, Issue 1, February 2016, Page: 5-16
Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans
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
Received: Jan. 1, 2016;       Accepted: Jan. 11, 2016;       Published: Feb. 4, 2016
DOI: 10.11648/j.ijiis.20160501.12      View  3519      Downloads  103
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%.
Knowledge-Based System, Medical Knowledge, Active Contour Image Segmentation, Automated Processes
To cite this article
Mahmoud Saleh Jawarneh, Mohammed Said Abual-Rub, Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans, International Journal of Intelligent Information Systems. Vol. 5, No. 1, 2016, pp. 5-16. doi: 10.11648/j.ijiis.20160501.12
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This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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