Abstract
Automatic extraction of HEp-2 cells from an image is one key component for the diagnosis of connective tissue diseases. The gradual transition between cell and surrounding tissue renders this process difficult for any computer aided diagnostic systems. In this regard, the paper presents a new approach for automatic HEp-2 cell segmentation by incorporating a new probability distribution, called stomped normal (SN) distribution. The proposed method integrates judiciously the concept of rough sets and the merit of the SN distribution into an finite mixture model framework to provide an accurate delineation of HEp-2 cells. The intensity distribution of a class is represented by SN distribution, where each class consists of a crisp lower approximation and a probabilistic boundary region. Finally, experiments are performed on a set of HEp-2 cell images to demonstrate the performance of the proposed algorithm, along with a comparison with related methods.
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Acknowledgment
This work is partially supported by the Department of Science and Technology, Government of India, New Delhi (grant no. SB/S3/EECE/050/2015).
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Banerjee, A., Maji, P. (2015). Rough Sets for Finite Mixture Model Based HEp-2 Cell Segmentation. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_40
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DOI: https://doi.org/10.1007/978-3-319-25754-9_40
Publisher Name: Springer, Cham
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