Abstract
In the context of several pathologies, the presence of lymphocytes has been correlated with disease outcome. The ability to automatically detect lymphocyte nuclei on histopathology imagery could potentially result in the development of an image based prognostic tool. In this paper we present a method based on the estimation of a mixture of Gaussians for determining the probability distribution of the principal image component. Then, a post-processing stage eliminates regions, whose shape is not similar to the nuclei searched. Finally, a Transferable Belief Model is used to detect the lymphocyte nuclei, and a shape based algorithm possibly splits them under an equal area and an eccentricity constraint principle.
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Keywords
- Ground Truth
- Positive Predictive Value
- Polygonal Approximation
- Histopathology Image
- Geodesic Active Contour
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Panagiotakis, C., Ramasso, E., Tziritas, G. (2010). Lymphocyte Segmentation Using the Transferable Belief Model. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds) Recognizing Patterns in Signals, Speech, Images and Videos. ICPR 2010. Lecture Notes in Computer Science, vol 6388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17711-8_26
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DOI: https://doi.org/10.1007/978-3-642-17711-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17710-1
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