A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei
A new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a new supervised algorithm for creating a pixe...
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1997-01-01
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Series: | Analytical Cellular Pathology |
Online Access: | http://dx.doi.org/10.1155/1997/304073 |
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doaj-859b37d670a641aeaa8abe851bce182c2020-11-24T22:26:03ZengHindawi LimitedAnalytical Cellular Pathology0921-89121878-36511997-01-0115314515610.1155/1997/304073A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell NucleiPetter Ranefall0Lars Egevad1Bo Nordin2Ewert Bengtsson3Centre for Image Analysis, Uppsala University, SwedenDepartment of Pathology, Uppsala University, SwedenCentre for Image Analysis, Uppsala University, SwedenCentre for Image Analysis, Uppsala University, SwedenA new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a new supervised algorithm for creating a pixel classifier is applied to an image that is typical for the sample. The training phase of the classifier is very user friendly since only a few typical pixels for each class need to be selected. The classifier is robust in that it is non‐parametric and has a built‐in metric that adapts to the colour space. After the training the classifier can be applied to all images from the same staining session. Then, all pixels classified as belonging to nuclei of cells are grouped into individual nuclei through a watershed segmentation and connected component labelling algorithm. This algorithm also separates touching nuclei. Finally, the nuclei are classified according to their fraction of positive pixels.http://dx.doi.org/10.1155/1997/304073 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Petter Ranefall Lars Egevad Bo Nordin Ewert Bengtsson |
spellingShingle |
Petter Ranefall Lars Egevad Bo Nordin Ewert Bengtsson A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei Analytical Cellular Pathology |
author_facet |
Petter Ranefall Lars Egevad Bo Nordin Ewert Bengtsson |
author_sort |
Petter Ranefall |
title |
A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei |
title_short |
A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei |
title_full |
A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei |
title_fullStr |
A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei |
title_full_unstemmed |
A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei |
title_sort |
new method for segmentation of colour images applied to immunohistochemically stained cell nuclei |
publisher |
Hindawi Limited |
series |
Analytical Cellular Pathology |
issn |
0921-8912 1878-3651 |
publishDate |
1997-01-01 |
description |
A new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a new supervised algorithm for creating a pixel classifier is applied to an image that is typical for the sample. The training phase of the classifier is very user friendly since only a few typical pixels for each class need to be selected. The classifier is robust in that it is non‐parametric and has a built‐in metric that adapts to the colour space. After the training the classifier can be applied to all images from the same staining session. Then, all pixels classified as belonging to nuclei of cells are grouped into individual nuclei through a watershed segmentation and connected component labelling algorithm. This algorithm also separates touching nuclei. Finally, the nuclei are classified according to their fraction of positive pixels. |
url |
http://dx.doi.org/10.1155/1997/304073 |
work_keys_str_mv |
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