Abstract
In this paper, we propose a segmentation method for an automated differential counter using image analysis. The segmentation here is to extract leukocytes (white blood cells) and separate its constituents, nucleus and cytoplasm, in blood smear images. For this purpose, a region-based active contour model is used where region information is estimated using a statistical analysis. The role of the regional statistics is mainly to attract evolving contours toward the boundaries of leukocytes, avoiding problems with initialization. And contour deformation near to the boundaries is constrained by an additional regularizer. The active contour model is implemented using a level set method and validated with a leukocyte image database.
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Eom, S., Kim, S., Shin, V., Ahn, B. (2006). Leukocyte Segmentation in Blood Smear Images Using Region-Based Active Contours. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_79
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DOI: https://doi.org/10.1007/11864349_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
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