Abstract
For quantification of drug’s delivery using small animals measuring biochemical changes in abdominal organs based on functional images is essential. However, in those images, the object boundaries are not clearly enough to locate its shape and position. And even though the structural information is compensated using image registration technique, delineation of organs is difficult and time-consuming. So we suggested an automatic procedure for delineation of organs in mouse PET image with the aid of atlas as a priori anatomical information. Prior information was given by voxel label number. CT used to construct an atlas is transformed to match mouse CT to be segmented. For each label corresponding voxels represent the same organ. Then, mouse CT-PET pairs should be aligned to identify organ area in PET. After all images are aligned and fused each other both structural and functional information can be observed simultaneously for several organs.
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References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Intl. J. of Computer Vision, 321–331 (1988)
Mdeical Image Segmentation: Methods and Software, Proceedings of NFSI & ICFBI (2007)
Zhu, G., Zhang, S., Zeng, Q., Wang, C.: Directional geodesic active contour for image segmentation. J. of Electronic Imaging (2007)
Liu, C., Ma, J., Ye, G.: Medical image segmentation by geodesic active contour incorporating region statistical information. In: Intl. Conf. on Fuzzy Systems and Knowledge Discovery (2007)
Rousson, M., Deriche, R.: Dynamic segmentation of vector valued images. In: Level Methods in Imaging, Vision and Graphics. Springer, Heidelberg (2003)
Tohlfing, T., Brandt, T., Menzel, R., Maurer, C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21, 1428–1442 (2004)
Maintz, J.B., et al.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)
Maroy, R., Boisgard, R., Comtat, C., Frouin, V., Cathier, P., Duchesnay, E., Dolle, F., Nielsen, P.E., Trebossen, R., Tavitian, B.: Segmentation of rodent whole-body dynamic PET images: an unsupervised method based on voxel dynamics. IEEE Trans. on Med. Img. 27(3), 342–354 (2008)
Jan, M.-L., et al.: A three-Dimensional Registration Method for Automated Fusion of micro PET-CT-SPECT Whole-Body Images. IEEE Trans. on Med. Img. 24(7), 886–893 (2005)
Chow, P.L., et al.: A Method of Image Registration for Animal, Multi-modality Imaging. Physics in Medicine and Biology 51, 379–390 (2006)
Rowland, D.J., et al.: Registration of 18f-FDG microPET and small-animal MRI. Nuclear Medicine and Biology 32, 567–572 (2006)
Shen, D., et al.: Coregistration of Magnetic Resonance and Single Photon Emission Computed Tomography Images for Noninvasive Localization of Stem Cells Grafted in the Infarcted Rat Myocardium. Mol. Imaging Biol. 9, 24–31 (2007)
Dogdas, B., Stout, D., Chatziioannou, A., Leahy, R.M.: Digimouse: A 3D Whole Body Mouse Atlas from CT and Cryosection Data. Phys. Med. Biol. 52(3), 577–587 (2007)
Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context. Radiotherapy and Oncology 87, 93–99 (2007)
Viola, P., Wells, W.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)
Studholme, V., Hill, D., Hawkes, D.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1999)
Kakadiaris, I.A., Bello, M., Arunachalam, S., Kang, W., Ju, T., Warren, J., Carson, J., Chiu, W., Thaller, C., Eichele, G.: Landmark-driven, atlas-based segmentation of mouse brain tissue images containing gene expression data. In: Proc. Medical Image Computing and Computer-Assisted Intervention, Saint-Malo, France, pp. 192–199 (2004)
Suri, J., Wilson, D.L., Laximinarayan, S.: The handbook of medical image analysis: segmentation and registration models. Springer, Heidelberg (2005)
Evaluation of atlas-based segmentation of hippocampal in healthy humans. Magnetic Resonance Imaging (2009)
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Song, S., Kim, MH. (2010). Segmentation of Abdominal Organs Incorporating Prior Knowledge in Small Animal CT. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_22
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DOI: https://doi.org/10.1007/978-3-642-17277-9_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
Online ISBN: 978-3-642-17277-9
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