Abstract
In this paper the problem of image segmentation using the random walker algorithm was considered. When applied to the segmentation of 3D images the method requires an extreme amount of memory and time resources in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system. Having in mind these limitations the optimization of the random walker approach is proposed. In particular, certain techniques for the graph size reduction and method parallelization are proposed. The results of applying the introduced improvements to the segmentation of 3D CT datasets are presented and discussed. The analysis of results shows that the modified method can be successfully applied to the segmentation of volumetric images and on a single PC provides results in a reasonable time.
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Gocławski, J., Węgliński, T., Fabijańska, A. (2015). Accelerating the 3D Random Walker Image Segmentation Algorithm by Image Graph Reduction and GPU Computing. In: Choraś, R. (eds) Image Processing & Communications Challenges 6. Advances in Intelligent Systems and Computing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-10662-5_6
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DOI: https://doi.org/10.1007/978-3-319-10662-5_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10661-8
Online ISBN: 978-3-319-10662-5
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