Abstract
In this paper, we present a novel superpixel based Region of Interest (ROI) search and segmentation algorithm. The proposed superpixel generation method differs from pioneer works due to its combination of boundary update and coarse-to-fine refinement for superpixel clustering. The former maintains the accuracy of segmentation, meanwhile, avoids much of unnecessary revisit to the ‘non-boundary’ pixels. The latter reduces the complexity by faster localizing those boundary blocks. The paper introduces the novel superpixel algorithm [10] to the problem of ROI detection and segmentation along with a coarse-to-fine refinement scheme over a set of image of different magnification. Extensive experiments indicates that the proposed method gives better accuracy and efficiency than other superpixel-based methods for lung cancer cell images. Moreover, the block-wise coarse-to-fine scheme enables a quick search and segmentation of ROIs in whole slide images, while, other methods still cannot.
Z. Huang—This work was partially supported by U.S. NSF IIS-1423056, CMMI-1434401, CNS-1405985.
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Li, R., Huang, J. (2015). Fast Regions-of-Interest Detection in Whole Slide Histopathology Images. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2015. Lecture Notes in Computer Science(), vol 9467. Springer, Cham. https://doi.org/10.1007/978-3-319-28194-0_15
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