Medical imaging modalities provide effective information for anatomic or
metabolic activity of tissues and organs in the body. Therefore, medical
imaging technology is a critical component in diagnosis and treatment of
various illnesses. Medical image segmentation plays an important role in
converting medical images into anatomically, functionally or surgically
identifiable structures, and is used in various applications. In this study,
some of the major medical image segmentation methods are examined and
applied to 2D CT images of upper torso for segmentation of heart, lungs,
bones, and muscle and fat tissues. The implemented medical image
segmentation methods are thresholding, region growing, watershed
transformation, deformable models and a hybrid method / watershed
transformation and region merging. Moreover, a comparative analysis is
performed among these methods to obtain the most efficient segmentation
method for each tissue and organ in torso. Some improvements are
proposed for increasing accuracy of some image segmentation methods.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/2/12607431/index.pdf |
| Date | 01 July 2006 |
| Creators | Demirkol, Onur Ali |
| Contributors | Serinagaoglu Dogrusoz, Yesim |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
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