@inproceedings{036c6e41263e48a492be03ddc713b93a,
title = "A novel adaptive local thresholding approach for segmentation of HEp-2 cell images",
abstract = "The patterns of Human Epithelial type 2 (HEp-2) cell provide useful information for the diagnosis of systemic autoimmune diseases. However, the recognition of cell patterns requires manual annotation by experienced physicians, which is subject to inter-observer variability. Therefore, an automatic diagnosis system is desirable. As the crucial pre-processing step for cell pattern recognition, the performance of cell segmentation is crucial. In this paper, a novel adaptive local thresholding approach is proposed to solve the issue. The approach divides cell images into overlapping sub-images and applies adaptive threshold estimator to each of them. The ICPR 2014 HEp-2 cell datasets are employed to assess the segmentation performance of our framework. The results show that the system achieves an average segmentation accuracy of 66.95%, which outperforms the typical thresholding approaches.",
keywords = "Adaptive thresholding, Cell images, Local thresholding, THRESHOLD estimator",
author = "Xiande Zhou and Yuexiang Li and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016 ; Conference date: 13-08-2016 Through 15-08-2016",
year = "2017",
month = mar,
day = "27",
doi = "10.1109/SIPROCESS.2016.7888247",
language = "English",
series = "2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "174--178",
booktitle = "2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016",
address = "United States",
}
