This Article 
 Bibliographic References 
 Add to: 
Hardware Accelerated Segmentation of Complex Volumetric Filament Networks
July/August 2009 (vol. 15 no. 4)
pp. 670-681
David Mayerich, Texas A&M University, College Station
John Keyser, Texas A&M University, College Station
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 by a complex volumetric network of thin filaments embedded in a scalar volume field. High-throughput microscopy volumes are also difficult to manage since they can require several terabytes of storage, even though the total volume of the embedded structure is much smaller. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet these networks can span large regions of the data set. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and undersampled data. We use graphics hardware to accelerate the tracing algorithm, making it more useful for large data sets. After the initial network is traced, we use an efficient encoding scheme to store volumetric data pertaining to the network.

[1] K. Al-Kofahi, S. Lasek, D. Szarowski, C. Pace, G. Nagy, J. Turner, and B. Roysam, “Rapid Automated Three-Dimensional Tracing of Neurons from Confocal Image Stacks,” IEEE Trans. Information Technology in Biomedicine, vol. 6, pp. 171-186, 2002.
[2] P.J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “InVivo Fiber Tractography Using DT-MRI Data,” Magnetic Resonance in Medicine, vol. 44, pp. 625-632, 2000.
[3] A. Dima, M. Scholz, and K. Obermayer, “Automatic Segmentation and Skeletonization of Neurons from Confocal Microscopy Images Based on the 3-D Wavelet Transform,” IEEE Trans. Image Processing, vol. 11, pp. 790-801, 2002.
[4] P. Doddapaneni, “Segmentation Strategies for Polymerized Volume Data Sets” PhD thesis, Dept. of Computer Science, Texas A&M Univ., 2004.
[5] H.K. Hahn, B. Preim, D. Selle, and H.O. Peitgen, “Visualization and Interaction Techniques for the Exploration of Vascular Structures,” Proc. Conf. Visualization '01, pp. 395-402, 2001.
[6] M. Harris, GPU Gems 2: Mapping Computational Concepts to GPUs. Addison Wesley, Mar. 2005.
[7] X. He, E. Kischell, M. Rioult, and T.J. Holmes, “Three-Dimensional Thinning Algorithm that Peels the Outmost Layer with Application to Neuron Tracing,” J. Computer-Assisted Microscopy, vol. 10, pp. 123-135, 1998.
[8] C. Kirbas and F. Quek, “A Review of Vessel Extraction Techniques and Algorithms,” ACM Computing Surveys, vol. 36, pp. 81-121, 2004.
[9] D. Mayerich, L.C. Abbott, and B.H. McCormick, “Knife-Edge Scanning Microscopy for Imaging and Reconstruction of Three-Dimensional Anatomical Structures of the Mouse Brain,” J.Microscopy, vol. 231, pp. 134-143, July 2008.
[10] D. Mayerich, J. Kwon, Y. Choe, L. Abbott, and J. Keyser, “Constructing High Resolution Microvascular Models,” Proc. Third Microscopic Image Analysis with Applications in Biology Workshop (MIAAB), 2008.
[11] B. McCormick, B. Busse, P. Doddapaneni, Z. Melek, and J. Keyser, “Compression, Segmentation, and Modeling of Filamentary Volumetric Data,” Proc. Ninth ACM Symp. Solid Modeling and Applications (SM '04), pp. 333-338, 2004.
[12] Z. Melek, D.M. Mayerich, C. Yuksel, and J. Keyser, “Visualization of Fibrous and Thread-Like Data,” IEEE Trans. Visualization and Computer Graphics, vol. 12, pp. 1165-1172, 2006.
[13] K.D. Micheva and S.J. Smith, “Array Tomography: A New Tool for Imaging the Molecular Architecture and Ultrastructure of Neural Circuits,” Neuron, vol. 55, pp. 25-36, 2007.
[14] Nielsen and Museth, “Dynamic Tubular Grid: An Efficient Data Structure and Algorithms for High Resolution Level Sets,” J.Scientific Computing vol. 26, pp. 261-299, 2006.
[15] N. Niki, Y. Kawata, H. Satoh, and T. Kumazaki, “3D Imaging of Blood Vessels Using X-Ray Rotational Angiographic System,” Proc. IEEE Nuclear Science Symp. and Medical Imaging Conf., vol. 3, pp. 1873-1877.
[16] J.F. O'Brien and N.F. Ezquerra, “Automated Segmentation of Coronary Vessels in Angiographic Image Sequences Utilizing Temporal, Spatial and Structural Constraints,” Proc. SPIE Visualization in Biomedical Computing, 1994.
[17] J.B. Pauley, ed., Handbook of Biological Confocal Microscopy. PlenumPress, 1995.
[18] F.H. Post, B. Vrolijk, H. Hauser, R.S. Laramee, and H. Doleisch, “The State of the Art in Flow Visualisation: Feature Extraction and Tracking,” Computer Graphics Forum, vol. 22, pp. 775-792, 2003.
[19] A. Sarwal and A. Dhawan, “3-D Reconstruction of Coronary Arteries from Two Views,” Proc. IEEE Conf. Eng. in Medicine and Biology, vol. 1, pp. 504-505, 1994.
[20] Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-Dimensional Multi-Scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images,” Medical Image Analysis, vol. 2, pp. 143-168, 1998.
[21] H. Schmitt, M. Grass, V. Rasche, O. Schramm, S. Haehnel, and K. Sartor, “An X-Ray-Based Method for the Determination of the Contrast Agent Propagation in 3-D Vessel Structures,” IEEE Trans. Medical Imaging, vol. 21, pp. 251-262, 2002.
[22] C. Stoll, S. Gumhold, and H.-P. Seidel, “Visualization with Stylized Line Primitives,” Proc. 16th Conf. Visualization '05, pp.695-702, 2005.
[23] T. Tozaki, Y. Kawata, N. Niki, H. Ohmatsu, K. Eguchi, and N. Moriyama, “Three-Dimensional Analysis of Lung Areas Using Thin Slice CT Images,” Proc. SPIE, vol. 2709, pp. 1-11, 1996.
[24] M. Woo, J. Neider, T. Davis, and D. Shreiner, OpenGL Programming Guide, third ed. Addison Wesley, 1999.
[25] Z. Yu and C. Bajaj, “A Segmentation-Free Approach for Skeletonization of Gray-Scale Images via Anisotropic Vector Diffusion,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2004.
[26] Y. Zhang, Y. Bazilevs, S. Goswami, C.L. Bajaj, and T.J.R. Hughes, “Patient-Specific Vascular Nurbs Modeling for Isogeometric Analysis of Blood Flow,” Computer Methods in Applied Mechanics and Eng., vol. 196, pp. 2943-2959, 2007.

Index Terms:
Microscopy, vessel, neuron, segmentation, tracking.
David Mayerich, John Keyser, "Hardware Accelerated Segmentation of Complex Volumetric Filament Networks," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 4, pp. 670-681, July-Aug. 2009, doi:10.1109/TVCG.2008.196
Usage of this product signifies your acceptance of the Terms of Use.