| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Pearlman, P. | |
| dc.contributor.author | Haaitsma, L.K. | |
| dc.date.accessioned | 2014-06-04T17:00:24Z | |
| dc.date.available | 2014-06-04T17:00:24Z | |
| dc.date.issued | 2014 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/16695 | |
| dc.description.abstract | Liver tumor segmentation and volumetry can help medical experts determine the rate of tumor growth and the effectiveness of cancer treatment. This thesis compares the algorithms and results of 9 key publications and will attempt to determine which algorithms performed best. Furthermore, suggestions to improve performance benchmarking are given. Due to the different goals and evaluation metrics used, not all publications could be compared with each other. Of the algorithms that were compared, one was found to be best suited for small tumors (diameter < 5 cm), one is most generally applicable and one automatic method is easiest to use. | |
| dc.description.sponsorship | Utrecht University | |
| dc.format.extent | 709210 | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en_US | |
| dc.title | Liver tumor segmentation in CT images | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | Liver, tumor, segmentation, lesion, hepatic, CT, computed tomography, tumour, delineation, review, volumetry | |
| dc.subject.courseuu | Biomedical Image Sciences | |
