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Cited by 167

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Morphological feature extraction for the classification of digital images of cancerous tissues

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Thiran, J.-P., Macq, B.·1996·IEEE Transactions on Biomedical Engineering

Abstract

This paper presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of Geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size, and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.

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Crossref · 10.1109/10.536902

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Metadata

Authors
Thiran, J.-P., & Macq, B.
Venue
IEEE Transactions on Biomedical Engineering
Published
1996
Volume
43
Issue
10
Pages
1011-1020
ISSN
0018-9294 (ISSN-L)
OpenAlex
W2153547712
Cited by
167
References
2
FWCI
2.40
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Morphological feature extraction for the classification of digital images of cancerous tissues. https://app.cochat.ai/papers/10.1109/10.536902
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Thiran, J.-P., & Macq, B. (1996). Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.536902

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.