2013 IEEE 10th International Symposium on Biomedical Imaging 2013
DOI: 10.1109/isbi.2013.6556500
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Robust cell segmentation for schizosaccharomyces pombe images with focus gradient

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Cited by 4 publications
(5 citation statements)
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“…fluorescence), nuclei are sometimes brightly stained in a relatively uniform dark background such that a single global threshold can be used as the first step of nuclei segmentation [171], [172], [173], and further analysis is conducted to separate touching or overlapping nuclei. Callau et al [174] have first empirically converted the RGB color images into grayscale images and then employed a threshold to segment the epithelial cell areas for cytokeratin-19 evaluation in breast cancer TMA images.…”
Section: Nucleus and Cell Segmentation Methodsmentioning
confidence: 99%
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“…fluorescence), nuclei are sometimes brightly stained in a relatively uniform dark background such that a single global threshold can be used as the first step of nuclei segmentation [171], [172], [173], and further analysis is conducted to separate touching or overlapping nuclei. Callau et al [174] have first empirically converted the RGB color images into grayscale images and then employed a threshold to segment the epithelial cell areas for cytokeratin-19 evaluation in breast cancer TMA images.…”
Section: Nucleus and Cell Segmentation Methodsmentioning
confidence: 99%
“…The traditional GVF snake [243] is applied to schizosaccharomyces pombe cell segmentation in bright-field microscopy images [173], where focus gradient correction and image enhancement are performed in advance to generate better edge maps. In [196], [250], [251], the GVF snake is applied to the LUV color space for simultaneous segmentation of nuclei and cytoplasm in blood smear images; in [252], a repulsive force is incorporated to the GVF snake to separate adjacent neuronal axons, which is achieved by reversing the gradient direction of neighboring objects.…”
Section: Nucleus and Cell Segmentation Methodsmentioning
confidence: 99%
“…However, the discrimination of non-local features does not have a general solution 7 , so traditionally, different algorithms have been designed based on different cell shapes 85 . For example, algorithms for yeast cells are usually classified into either ball-shaped budding yeast algorithms 91011 or rod-shaped fission yeast algorithms 6,12,13 . In practice, we often need to integrate and discriminate many aspects of shape.…”
Section: Introductionmentioning
confidence: 99%
“…However, the discrimination of non‐local features does not have a general solution [7], so traditionally, different algorithms have been designed based on different cell shapes [5,8]. For example, algorithms for yeast cells are usually classified into either ball‐shaped budding yeast algorithms [9–11] or rod‐shaped fission yeast algorithms [6,12,13]. In practice, we often need to integrate and discriminate many aspects of shape.…”
Section: Introductionmentioning
confidence: 99%