ABSTRACT
We present a framework for segmenting and storing filament networks from scalar volume data. Filament structures are commonly found in data generated using high-throughput microscopy. These data sets can be several gigabytes in size because they are either spatially large or have a high number of scalar channels. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet single filaments can span large data sets. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and under-sampled data. We use a GPU-based scheme to accelerate the tracing algorithm, making it more useful for large data sets. After the initial structure is traced, we can use this information to create a bounding volume around the network and encode the volumetric data associated with it. Taken together, this framework provides a convenient method for accessing network structure and connectivity while providing compressed access to the original volumetric data associated with the network.
- Al-Kofahi, K., Lasek, S., Szarowski, D., Pace, C., Nagy, G., Turner, J., and Roysam, B. 2002. Rapid automated three-dimensional tracing of neurons from confocal image stacks. IEEE Transactions on Information Technology in Biomedicine 6, 171--186. Google ScholarDigital Library
- Doddapaneni, P. 2004. Segmentation Strategies for Polymerized Volume Data Sets. PhD thesis, Department of Computer Science, Texas A&M University.Google Scholar
- Gonzalez, R. C., and Woods, R. E. 2002. Digital Image Processing, 2nd ed. Prentice Hall. Google ScholarDigital Library
- Hahn, H. K., Preim, B., Selle, D., and Peitgen, H. O. 2001. Visualization and interaction techniques for the exploration of vascular structures. Proceedings of the conference on Visualization '01, 395--402. Google ScholarDigital Library
- Kirbas, C., and Quek, F. 2004. A review of vessel extraction techniques and algorithms. ACM Computing Surveys 36, 81--121. Google ScholarDigital Library
- Mayerich, D., Abbott, L. C., and McCormick, B. H. 2008. Knife-edge scanning microscopy for imaging and reconstruction of three-dimensional anatomical structures of the mouse brain. Journal of Microscopy in press.Google ScholarCross Ref
- McCormick, B., Busse, B., Doddapaneni, P., Melek, Z., and Keyser, J. 2004. Compression, segmentation, and modeling of filamentary volumetric data. 333--338. Google ScholarDigital Library
- Micheva, K. D., and Smith, S. J. 2007. Array tomography: A new tool for imaging the molecular architecture and ultrastructure of neural circuits. Neuron 55, 25--36.Google ScholarCross Ref
- Nielsen, and Museth. 2006. Dynamic tubular grid: An eicient data structure and algorithms for high resolution level sets. Journal of Scientic Computing 26, 261--299. Google ScholarDigital Library
- Osher, S. J., and Fedkiw, R. P. 2002. Level Set Methods and Dynamic Implicit Surfaces. Springer.Google Scholar
- Sarwal, A., and Dhawan, A. 1994. 3-d reconstruction of coronary arteries. IEEE Conference on Engineering in Medicine and Biology 1, 504--505.Google Scholar
- Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., and Kikinis, R. 1998. Three-dimensional multi-scale line Iter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis 2, 143--168.Google ScholarCross Ref
- Sethian, J. A. 1999. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press.Google Scholar
- Tozaki, T., Kawata, Y., Niki, N., Ohmatsu, H., Eguchi, K., and Moriyama, N. 1996. Three-dimensional analysis of lung areas using thin slice ct images. Proc. SPIE 2709, 1--11.Google Scholar
- Yu, Z., and Bajaj, C. A segmentation-free approach for skeletonization of gray-scale images via anisotropic vector diffusion. Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on 1.Google Scholar
- Zhang, Y., Bazilevs, Y., Goswami, S., Bajaj, C. L., and Hughes, T. J. R. 2007. Patient-specific vascular nurbs modeling for isogeometric analysis of blood flow. Computer Methods in Applied Mechanics and Engineering 196, 2943--2959.Google ScholarCross Ref
Index Terms
- Filament tracking and encoding for complex biological networks
Recommendations
Hardware Accelerated Segmentation of Complex Volumetric Filament Networks
We present a framework for segmenting and storing filament networks from scalar volume data. Filament networks are encountered more and more commonly in biomedical imaging due to advances in high-throughput microscopy. These data sets are characterized ...
Automated brain tractography segmentation using curvature points
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image ProcessingClassification of brain fiber tracts is an important problem in brain tractography analysis. We propose a supervised algorithm which learns features for anatomically meaningful fiber clusters, from labeled DTI white matter data. The classification is ...
Comments