Automatic Cell Segmentation Using a Shape-Classification Model in Immunohistochemically Stained Cytological Images
Summary :
This paper presents a segmentation method for detecting cells in immunohistochemically stained cytological images. A two-phase approach to segmentation is used where an unsupervised clustering approach coupled with cluster merging based on a fitness function is used as the first phase to obtain a first approximation of the cell locations. A joint segmentation-classification approach incorporating ellipse as a shape model is used as the second phase to detect the final cell contour. The segmentation model estimates a multivariate density function of low-level image features from training samples and uses it as a measure of how likely each image pixel is to be a cell. This estimate is constrained by the zero level set, which is obtained as a solution to an implicit representation of an ellipse. Results of segmentation are presented and compared to ground truth measurements.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.7 pp.1955-1962
- Publication Date
- 2008/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.7.1955
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision and its Applications)
- Category
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Shishir SHAH, "Automatic Cell Segmentation Using a Shape-Classification Model in Immunohistochemically Stained Cytological Images" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 1955-1962, July 2008, doi: 10.1093/ietisy/e91-d.7.1955.
Abstract: This paper presents a segmentation method for detecting cells in immunohistochemically stained cytological images. A two-phase approach to segmentation is used where an unsupervised clustering approach coupled with cluster merging based on a fitness function is used as the first phase to obtain a first approximation of the cell locations. A joint segmentation-classification approach incorporating ellipse as a shape model is used as the second phase to detect the final cell contour. The segmentation model estimates a multivariate density function of low-level image features from training samples and uses it as a measure of how likely each image pixel is to be a cell. This estimate is constrained by the zero level set, which is obtained as a solution to an implicit representation of an ellipse. Results of segmentation are presented and compared to ground truth measurements.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.1955/_p
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@ARTICLE{e91-d_7_1955,
author={Shishir SHAH, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Cell Segmentation Using a Shape-Classification Model in Immunohistochemically Stained Cytological Images},
year={2008},
volume={E91-D},
number={7},
pages={1955-1962},
abstract={This paper presents a segmentation method for detecting cells in immunohistochemically stained cytological images. A two-phase approach to segmentation is used where an unsupervised clustering approach coupled with cluster merging based on a fitness function is used as the first phase to obtain a first approximation of the cell locations. A joint segmentation-classification approach incorporating ellipse as a shape model is used as the second phase to detect the final cell contour. The segmentation model estimates a multivariate density function of low-level image features from training samples and uses it as a measure of how likely each image pixel is to be a cell. This estimate is constrained by the zero level set, which is obtained as a solution to an implicit representation of an ellipse. Results of segmentation are presented and compared to ground truth measurements.},
keywords={},
doi={10.1093/ietisy/e91-d.7.1955},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Automatic Cell Segmentation Using a Shape-Classification Model in Immunohistochemically Stained Cytological Images
T2 - IEICE TRANSACTIONS on Information
SP - 1955
EP - 1962
AU - Shishir SHAH
PY - 2008
DO - 10.1093/ietisy/e91-d.7.1955
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - This paper presents a segmentation method for detecting cells in immunohistochemically stained cytological images. A two-phase approach to segmentation is used where an unsupervised clustering approach coupled with cluster merging based on a fitness function is used as the first phase to obtain a first approximation of the cell locations. A joint segmentation-classification approach incorporating ellipse as a shape model is used as the second phase to detect the final cell contour. The segmentation model estimates a multivariate density function of low-level image features from training samples and uses it as a measure of how likely each image pixel is to be a cell. This estimate is constrained by the zero level set, which is obtained as a solution to an implicit representation of an ellipse. Results of segmentation are presented and compared to ground truth measurements.
ER -
English
