Three-dimensional thinning algorithms on graphics processing units and multicore CPUs

Concurrency and Computation: Practice and Experience

Volumn 24, Issue 14, 2012, Pages 1551-1571

  Jimenez J ^a   Ruiz De Miras, J ^a  

^a UNIVERSITY OF JAÉN   (Spain)

Author keywords

3D curve skeleton; CUDA; OpenCL; thinning algorithm

Indexed keywords

GRAPHICS PROCESSING UNIT; IMAGE CODING; MEDICAL IMAGING; MULTICORE PROGRAMMING; MUSCULOSKELETAL SYSTEM; PARALLEL PROGRAMMING; PROGRAM PROCESSORS;

3D CURVE-SKELETON; COMPUTE UNIFIED DEVICE ARCHITECTURE(CUDA); CUDA; OPENCL; PARALLEL PROGRAMMING MODEL; THINNING ALGORITHM; THREE DIMENSIONAL CURVES; THREE-DIMENSIONAL OBJECT;

THREE DIMENSIONAL COMPUTER GRAPHICS;

EID: 84866038087     PISSN: 15320626     EISSN: 15320634     Source Type: Journal    
DOI: 10.1002/cpe.1900     Document Type: Article

References (41)

1. Cornea N, Silver D, Min P,. Curve-skeleton properties, applications and algorithms. IEEE Transactions on Visualization and Computer Graphics 2007; 13: 530-548.
2. Ruiz-De-Miras J, Navas J, Villoslada P, Esteban FJ,. UJA-3DFD: a program to compute the 3D fractal dimension from MRI data. Computers Methods and Programs in Biomedicine 2011. DOI: 10.1016/j.cmpb.2010.08.015. In Press.
3. Klette G, Pan M,. 3D topological thinning by identifying non-simple voxels. Lecture Notes in Computer Science 2005; 3322: 164-175.
4. Palágyi K, Kuba A,. A parallel 3D 12-subiteration thinning algorithm. Graphical Models and Image Processing 1999; 61: 199-221.
5. Arcelli C, Sanniti G, Serino L,. Distance-driven skeletonization in voxel images. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011; 33 (4): 709-720.
6. Sud A, Otaduy M, Manocha D,. DiFi: last 3D distance field computation using graphics hardware. Computer Graphics Forum 2004; 23 (3): 557-566.
7. van Dortmont M, van de Wetering H, Telea A,. Skeletonization and distance transforms of 3D volumes using graphics hardware, Proceedings of the 13th International Conference on Discrete Geometry for Computer Imagery, Szeged, Hungary, October 25-27, 2006; 617-629.
8. Cao T, Tang K, Mohamed A, Tan T,. Parallel banding algorithm to compute exact distance transform with the GPU. Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Hyatt Regency Bethesda, Washington DC,February 19th-21st, 2010; 83-90.
9. Hesselink WH, Roerdink J,. Euclidean skeletons of digital image and volume data in linear time by the integer medial axis transform. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008; 30 (12): 2204-2217.
10. Esteban F, Padilla N, Sanz-Cortés M, Ruiz-De-Miras J, Bargalló N, Villoslada P, Gratacós E,. Fractal-dimension analysis detects cerebral changes in preterm infants with and without intrauterine growth restriction. NeuroImage 2010; 53: 1225-1232.
11. NVIDIA. NVIDIA CUDA C programming guide, version 3.2, 2010.http://developer.nvidia.com/object/gpucomputing.html.
12. Khronos OpenCL working group: the OpenCL specification, version 1.1., 2010. http://www.khronos.org/opencl.
13. Kirk DB, Hwu WW,. Programming Massively Parallel Processors. Hands-on Approach. Morgan Kaufmann Publishers: Burlington, MA. USA, 2010.
14. Sanders J, Kandrot E,. CUDA by Example. An Introduction to general-purpose GPU Programming. Addison-Wesley: Boston, MA. USA, 2010.
15. Garland M, Grand SL, Nickolls J, Anderson J, Hardwick J, Morton S, Phillips E, Zhang Y, Volkov V,. Parallel computing experiences with CUDA. IEEE Micro 2008; 28 (4): 13-27.
16. Leist A, Playne DP, Hawick KA,. Exploiting graphical processing units for data-parallel scientific applications. Concurrency and Computation: Practice and Experience 2009 21: 2400-2437.
17. Kong J, Dimitrov M, Yang Y, Liyanage J, Cao L, Staples J, Mantor M, Zhou H,. Accelerating MATLAB image processing toolbox functions on GPUs. Proceedings of the Third Workshop on General-Purpose Computation on Graphics Processing Units(GPGPU-3), Pittsburgh, PA, USA, 2010.
18. Khanna G, McKennon J,. Numerical modeling of gravitational wave sources accelerated by OpenCL. Computer Physics Communications 2010; 181 (9): 1605-1611.
19. Sharma B, Vydyanathan N,. Parallel discrete wavelet transform using the Open Computing Language: a performance and portability study. Proceedings of the 2010 IEEE International Symposium on Parallel & Distributed Processing, Atlanta, GA, USA, 2010.
20. Weber R, Gothandaraman A, Hinde RJ, Peterson GD,. Comparing hardware accelerators in scientific applications: a case study. IEEE Transactions on Parallel and Distributed Systems 2011; 22 (1): 58-68.
21. Vassilev TI,. Comparison of several parallel API for cloth modelling on modern GPUs. Proceedings of the 11th International Conference on Computer Systems and Technologies, Sofia, Bulgaria, 2010; 131-136.
22. NVIDIA. Nvidia CUDA best practices guide v3.0, 2010.http://developer. nvidia.com/object/gpucomputing.html.
23. Kong T,. On topology preservation in 2D and 3D thinning. International Journal of Pattern Recognition and Artificial Intelligence 1995; 9: 813-844.
24. Hall R,. Parallel connectivity-preserving thinning algorithms. Machine Intelligence and Pattern Recognition 1996 19: 145-179.
25. Lohou C, Bertrand G,. A 3D 12-subiteration thinning algorithm based on p-simple points. Discrete Applied Mathematics 2004; 139 (1-3).
26. Palágyi K,. A 3D fully parallel surface-thinning algorithm. Theoretical Computer Science 2008; 406 (1-2): 119-135.
27. AMD: ATI stream computing OpenCL programming guide rev. 1.03, 2010.
28. AMD: introduction to OpenCL programming. Training guide. rev: A, 2010.
29. AMD: OpenCL SDK, 2011.http://developer.amd.com/gpu/AMDAPPSDK/Pages/ default.aspx.
30. INTEL: OpenCL SDK, 2011.http://software.intel.com/en-us/articles/intel- opencl-sdk.
31. INTEL: Intel core i7-920 processor. Specifications, 2011.http://ark. intel.com/Product.aspx?id=37147.
32. Rice University: NVIDIA CUDA library documentation, 2011.http://www. clear.rice.edu/comp422/resources/cuda/html.
33. Stanford university: the stanford 3D scanning repository, 2010.http://graphics.stanford.edu/data/3Dscanrep.
34. Aim@shape repository, 2011. http://shapes.aimatshape.net.
35. 3DVIA repository, 2011. http://www.3dvia.com.
36. Process.h c library specification.http://www.digitalmars.com/rtl/process. html.
37. Park IK, Singhal N, Lee MH, Cho S, Kim C,. Design and performance evaluation of image processing algorithms on GPUs. IEEE Transactions on Parallel and Distributed Systems 2011; 22 (1): 91-104.
38. Che S, Boyer M, Meng J, Tarjan D, Sheaffer JW, Skadron K,. A performance study of general-purpose applications on graphics processors using CUDA. Journal of Parallel and Distributed Computing 2008; 68: 1370-1380.
39. Cardoso N, Bicudo P,. Su (2) lattice gauge theory simulations on fermi gpus. Journal of Computational Physics 2011; 230 (10): 3998-4010.
40. Stpiczynski P, Potiopa J,. Solving a kind of boundary-value problem for ordinary difierential equations using fermi-the next generation CUDA computing architecture. Journal of Computational and Applied Mathematics 2011; 236 (3): 384-393.
41. Kouassi EK, Amagasa T, Kitagawa H,. Eifficient probabilistic latent semantic indexing using graphics processing unit. Procedia Computer Science 2011; 4: 382-391.