@inproceedings{7e1b3e9780d5416faf4a027f847ff057,
title = "Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers",
abstract = "Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific domain. The decision from the mixture of experts determines how likely a new pixel is a cell pixel. We demonstrate the effectiveness of this approach on four cell types with diverse morphologies under different microscopy imaging modalities.",
keywords = "Bayesian classifier, Cell segmentation, Microscopy image, Mixture of experts",
author = "Zhaozheng Yin and Ryoma Bise and Mei Chen and Takeo Kanade",
year = "2010",
doi = "10.1109/ISBI.2010.5490399",
language = "English",
isbn = "9781424441266",
series = "2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings",
publisher = "IEEE Computer Society",
pages = "125--128",
booktitle = "2010 7th IEEE International Symposium on Biomedical Imaging",
note = "7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 ; Conference date: 14-04-2010 Through 17-04-2010",
}