Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach: A quantitative evaluation

Abstract In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in...

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Published inComputerized medical imaging and graphics Vol. 34; no. 6; pp. 453 - 461
Main Authors Di Cataldo, S, Ficarra, E, Acquaviva, A, Macii, E
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.09.2010
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Abstract Abstract In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations.
AbstractList In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours' limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations.
Abstract In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations.
Author Di Cataldo, S
Ficarra, E
Acquaviva, A
Macii, E
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Keywords Immunohistochemistry
Morphological operators
Nuclear segmentation
Tissue images
Active contours
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Snippet Abstract In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification...
In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for...
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SubjectTerms Active contours
Cell Nucleus - diagnostic imaging
Connective Tissue - diagnostic imaging
Connective Tissue - pathology
Diagnostic Imaging - methods
Humans
Image Processing, Computer-Assisted - methods
Immunohistochemistry
Internal Medicine
Morphological operators
Neoplasms - diagnostic imaging
Nuclear segmentation
Other
Radiography
Tissue images
Title Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach: A quantitative evaluation
URI https://www.clinicalkey.es/playcontent/1-s2.0-S089561110900158X
https://dx.doi.org/10.1016/j.compmedimag.2009.12.008
https://www.ncbi.nlm.nih.gov/pubmed/20060681
https://search.proquest.com/docview/733980371
https://search.proquest.com/docview/753687037
Volume 34
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