Abstract
This paper presents a practical combination of image processing and pattern recognition techniques in order to identify pathological and atypical cells in phase contrast cytological images. The algorithms involved in the processing cover: oriented edge detection, ridge following, contour grouping and ellipse fitting. The Hough Transform and other techniques are discussed for comparison. Various pattern recognition techniques are tested and compared. All the exploited algorithms were customized to reflect specificity of phase contrast images and apriori–knowledge of cytological smear. Possible applications of this algorithm for automated screening systems are enumerated.
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Smereka, M., Glab, G. (2006). Detection of Pathological Cells in Phase Contrast Cytological Images. 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_75
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DOI: https://doi.org/10.1007/11864349_75
Publisher Name: Springer, Berlin, Heidelberg
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