Many practical applications in the field of medical image processing require valid and reliable segmentation of images. In this dissertation, we propose three different semi-automatic segmentation frameworks for 2D-upper torso medical images to
construct 3D geometric model of the torso structures. In the first framework, an extended version of the Otsu&rsquo / s method for three level thresholding and a recursive connected component algorithm are combined. The segmentation process is accomplished by first using Extended Otsu&rsquo / s method and then labeling in each consecutive slice. Since there is no information about pixel positions in the outcome of Extended Otsu&rsquo / s method, we perform some processing after labeling to connect pixels belonging with the same tissue. In the second framework, Chan-Vese (CV) method, which is an example of active contour models, and a recursive connected component algorithm
are used together. The segmentation process is achieved using CV method without egde information as stopping criteria. In the third and last framework, the combination of watershed transformation and K-means are used as the segmentation method. After segmentation operation, the labeling is performed for the determination of the medical structures. In addition, segmentation and labeling operation is realized for each consecutive slice in each framework. The results of each framework are compared quantitatively with manual segmentation results to evaluate their performances.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12612866/index.pdf |
| Date | 01 January 2011 |
| Creators | Oz, Sinan |
| 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 public access |
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