Abstract
This document presents the stage of research concerning an automatic diagnosis system of breast cancer based on cytological images of FNB (Fine Needle Biopsy). The work concentrates on the image segmentation phase, which is employed to find nucleus in cytological images. The accuracy and correctness of the image segmentation algorithm is a critical factor for successful diagnosis due to the fact that case classification is based on morphometrical features extracted form segmented nucleus. The presented approach to image nucleus segmentation is based on the FCMS (Fuzzy C-Means with Shape function) clustering algorithm. Traditional approaches to image segmentation using clustering algorithms consider clustering pixels in color space in order to recognize objects. The novelty of the presented approach is that the clustering process is conducted in color space but the searched objects must have an arbitrarily defined shape. Simulations and experimental results are included in the work to illustrate the effectiveness of the proposed approach.
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Kowal, M., Korbicz, J. (2010). Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_20
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DOI: https://doi.org/10.1007/978-3-642-05177-7_20
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