Yang, M. and Yuan, Y. and Li, Xuelong and Yan, P. (2011) Medical image segmentation using descriptive image features. In: Hoey, J. and McKenna, S. and Trucco, E. (eds.) Procedings of the British Machine Vision Conference. Manchester, UK: BMVA Press, 94.1-94.11. ISBN 978190172543X.
Abstract
Segmentation of medical images is an important component for diagnosis and treatment of diseases using medical imaging technologies. However, automated accurate medical image segmentation is still a challenge due to the difficulties in finding a robust feature descriptor to describe the object boundaries in medical images. In this paper, a new normal vector feature profile (NVFP) is proposed to describe the local image information of a contour point by concatenating a series of local region descriptors along the normal direction at that point. To avoid trapping by false boundaries caused by nonboundary image features, a modified scale invariant feature transform (SIFT) descriptor is developed. The number and locations of sample points for building NVFP are determined for each contour point, which are constrained by the neighboring anatomical structures and the statistical consistency of the training features. NVFP is incorporated into a model based method for image segmentation. The performance of our proposed method was demonstrated by segmenting prostate MR images. The segmentation results indicated that our method can achieve better performance compared with other existing methods.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 07 Jun 2013 09:34 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7366 |
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