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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Main Authors: Petter Ranefall, Lars Egevad, Bo Nordin, Ewert Bengtsson
Format: Article
Language:English
Published: Hindawi Limited 1997-01-01
Series:Analytical Cellular Pathology
Online Access:http://dx.doi.org/10.1155/1997/304073
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spelling 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
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