Knowledge Guided Automatic Contour Initialization And Segmentation Of Abdominal Structures In CT Images
Computed Tomography (CT) scans are becoming a priceless means of diagnosing abdominal structures. CT scans result in a huge number of 2D slices of the acquired anatomical part in abdominal imaging. CT are more preferred compared to sensitive imaging techniques such as MRI in abdominal imaging owi...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.usm.my/45658/1/MAHMOUD%20SALEH%20MAHMOUD%20JAWARNEH_HJ.pdf |
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| Summary: | Computed Tomography (CT) scans are becoming a priceless means of diagnosing
abdominal structures. CT scans result in a huge number of 2D slices of the acquired
anatomical part in abdominal imaging. CT are more preferred compared to sensitive
imaging techniques such as MRI in abdominal imaging owing to their high signal to
noise and good spatial resolution. In the area of medical image processing, the
current interests are in the automated analysis and visualization of liver, spleen, and
kidney to assist in diagnosis, radiation therapy planning and surgical planning.
Delineation of these structures which is still an open research problem is the first and
fundamental step in all of these studies. Automation of medical image segmentation
reduces time-consuming, tedious, subjective human interaction tasks and may aid
radiologists, who are normally required to view thousands of images daily. Thus,
automatic segmentation is the main focus of several research efforts. In this research,
we propose an automatic knowledge-based segmentation framework based on active
contour methods. The proposed segmentation system is generic, and employs
multiple sources of medical knowledge: medical atlas; expert’s rules; multiple
views: axial, coronal and sagittal; image features and image DICOM Meta data. The
focus in this research is on level set active contour segmentation methods which
provide promising results, robust to dataset variations and do not require extensive
prior training. As such, they can be reliably used for segmentation of major
structures in 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%. |
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