Deriving liver vessel structure from CT scans manually is time consuming and error prone. An automatic procedure that could help the radiologist in her analysis is therefore needed. We present two algorithms to preprocess and segment the hepatic vessels. The first algorithm processes each CT slice individually, while the second algorithm applies processing on the whole CT scan at once. Matched filtering and anisotropic diffusion is used to emphasise the blood vessels, and entropy based thresholding and segmentation by local mean and variance are used to coarsely position the vessels. Node positions and sizes are derived from the skeleton and the distance transform of the segmentation results, respectively. From the skeleton and node data, interconnections are added forming a vessel graph. At the end, a search is executed to find the most likely vessel graph based on anatomical knowledge. Results have been inspected visually by medical staff and are promising with respect to clinical use in the future.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ntnu-9210 |
| Date | January 2005 |
| Creators | Eidheim, Ole Christian |
| Publisher | Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, Institutt for datateknikk og informasjonsvitenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0018 seconds