A novel geodesic distance based clustering approach to delineating boundaries of touching cells

Xu Chen; Yanqiao Zhu; Fuhai Li; Zeyi Zheng; Eric Chang; Jinwen Ma; Stephen T C Wong

doi:10.1007/978-3-642-39068-5_39

A novel geodesic distance based clustering approach to delineating boundaries of touching cells

Xu Chen, Yanqiao Zhu, Fuhai Li, Zeyi Zheng, Eric Chang, Jinwen Ma, Stephen T C Wong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In this paper, we propose a novel geodesic distance based clustering approach for delineating boundaries of touching cells. In specific, the Riemannian metric is firstly adopted to integrate the spatial distance and intensity variation. Then the distance between any two given pixels under this metric is computed as the geodesic distance in a propagational way, and the K-means-like algorithm is deployed in clustering based on the propagational distance. The proposed method was validated to segment the touching Madin-Darby Canine Kidney (MDCK) epithelial cell images for measuring their N-Ras protein expression patterns inside individual cells. The experimental results and comparisons demonstrate the advantages of the proposed method in massive cell segmentation and robustness to the initial seeds selection, varying intensity contrasts and high cell densities in microscopy images.

Original language	English (US)
Title of host publication	Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings
Publisher	Springer-Verlag
Pages	315-322
Number of pages	8
Edition	PART 2
ISBN (Print)	9783642390678
DOIs	https://doi.org/10.1007/978-3-642-39068-5_39
State	Published - 2013
Event	10th International Symposium on Neural Networks, ISNN 2013 - Dalian, China Duration: Jul 4 2013 → Jul 6 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7952 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	10th International Symposium on Neural Networks, ISNN 2013
Country/Territory	China
City	Dalian
Period	7/4/13 → 7/6/13

Keywords

Cell Segmentation
Clustering Analysis
Distance Propagation
Riemannian Metric

ASJC Scopus subject areas

General Computer Science
Theoretical Computer Science

Access to Document

10.1007/978-3-642-39068-5_39

Cite this

Chen, X., Zhu, Y., Li, F., Zheng, Z., Chang, E., Ma, J., & Wong, S. T. C. (2013). A novel geodesic distance based clustering approach to delineating boundaries of touching cells. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings (PART 2 ed., pp. 315-322). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7952 LNCS, No. PART 2). Springer-Verlag. https://doi.org/10.1007/978-3-642-39068-5_39

A novel geodesic distance based clustering approach to delineating boundaries of touching cells. / Chen, Xu; Zhu, Yanqiao; Li, Fuhai et al.
Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2. ed. Springer-Verlag, 2013. p. 315-322 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7952 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, X, Zhu, Y, Li, F, Zheng, Z, Chang, E, Ma, J & Wong, STC 2013, A novel geodesic distance based clustering approach to delineating boundaries of touching cells. in Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7952 LNCS, Springer-Verlag, pp. 315-322, 10th International Symposium on Neural Networks, ISNN 2013, Dalian, China, 7/4/13. https://doi.org/10.1007/978-3-642-39068-5_39

Chen X, Zhu Y, Li F, Zheng Z, Chang E, Ma J et al. A novel geodesic distance based clustering approach to delineating boundaries of touching cells. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2 ed. Springer-Verlag. 2013. p. 315-322. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-39068-5_39

Chen, Xu ; Zhu, Yanqiao ; Li, Fuhai et al. / A novel geodesic distance based clustering approach to delineating boundaries of touching cells. Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 2. ed. Springer-Verlag, 2013. pp. 315-322 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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AU - Ma, Jinwen

AU - Wong, Stephen T C

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AB - In this paper, we propose a novel geodesic distance based clustering approach for delineating boundaries of touching cells. In specific, the Riemannian metric is firstly adopted to integrate the spatial distance and intensity variation. Then the distance between any two given pixels under this metric is computed as the geodesic distance in a propagational way, and the K-means-like algorithm is deployed in clustering based on the propagational distance. The proposed method was validated to segment the touching Madin-Darby Canine Kidney (MDCK) epithelial cell images for measuring their N-Ras protein expression patterns inside individual cells. The experimental results and comparisons demonstrate the advantages of the proposed method in massive cell segmentation and robustness to the initial seeds selection, varying intensity contrasts and high cell densities in microscopy images.

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A novel geodesic distance based clustering approach to delineating boundaries of touching cells

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Keywords

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