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Abstract
Intensity inhomogeneity represents a significant challenge in image processing. Popular image segmentation algorithms produce inadequate results in images with intensity inhomogeneity. Existing correction methods are often computationally expensive. Therefore, efficient implementations for the bias field estimation and inhomogeneity correction are required. In this work, we propose an extended mask-based version of the levelset method, recently presented by Li et al. [1]. We develop efficient CUDA implementations for the original full domain and the extended mask-based versions. We compare the methods in terms of speed, efficiency, and performance. Magnetic resonance (MR) images are one of the main application in practice.
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References
Li, C., Huang, R., Ding, Z., et al.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. on Image Processing 20, 2007–2016 (2011)
Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. on Medical Imaging 26(3), 405–421 (2007)
Hou, Z.: A review on mr image intensity inhomogeneity correction. International Journal of Biomedical Imaging 1, 1–11 (2006)
Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. on Image Processing 19(12), 3243–3254 (2010)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)
Gonzalez, R.C., Woods, R.E.: Digital Image processing. Prentice Hall International (2008)
Young, I.T., van Vliet, L.: Recursive implementation of the Gaussian filter. Signal Proc. 44, 139–151 (1995)
Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU computing. Proceedings of the IEEE 96(5), 879–899 (2008)
Ivanovska, T., Linsen, L., Hahn, H.K., Voelzke, H.: GPU implementations of a relaxation scheme for image partitioning: GLSL vs. CUDA. Computing and Visualization in Science 14(5), 217–226 (2012)
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Authors and Affiliations
Ernst-Moritz-Arndt University Greifswald, Germany
Tatyana Ivanovska, René Laqua, Henry Völzke & Katrin Hegenscheid
Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany
Lei Wang
- Tatyana Ivanovska
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- René Laqua
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- Lei Wang
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- Henry Völzke
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- Katrin Hegenscheid
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Editor information
Editors and Affiliations
Institute for Systems and Robotics, Instituto Superior Técnico, Portugal
João M. Sanches
University of Alicante, Spain
Luisa Micó
INESC and University of Porto, Porto, Portugal
Jaime S. Cardoso
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Ivanovska, T., Laqua, R., Wang, L., Völzke, H., Hegenscheid, K. (2013). Fast Implementations of the Levelset Segmentation Method With Bias Field Correction in MR Images: Full Domain and Mask-Based Versions. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_80
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