Email updates

Keep up to date with the latest news and content from Diagnostic Pathology and BioMed Central.

This article is part of the supplement: Proceedings of the 11th European Congress on Telepathology and 5th International Congress on Virtual Microscopy

Open Access Proceedings

A multistep image analysis method to increase automated identification efficiency in immunohistochemical nuclear markers with a high background level

Marylène Lejeune1, Vanessa Gestí3, Barbara Tomás3, Anna Korzyńska4, Albert Roso1, Cristina Callau1, Ramon Bosch3, Jordi Baucells5, Joaquín Jaén3 and Carlos López12*

Author Affiliations

1 Molecular Biology and Research Section, Hospital de Tortosa Verge de la Cinta, IISPV, URV, Tortosa, Spain

2 Unitat de Suport a la Recerca de la Gerencia Territorial Terres de l’Ebre, IDIAP Jordi Gol, IISPV, URV, UAB, Tortosa, Spain

3 Department of Pathology, Hospital de Tortosa Verge de la Cinta, IISPV, Tortosa, Spain

4 Laboratory of Processing Systems of Microscopic Image Information, Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

5 Department of Informatics, Hospital de Tortosa Verge de la Cinta, IISPV, Tortosa, Spain

For all author emails, please log on.

Diagnostic Pathology 2013, 8(Suppl 1):S13  doi:10.1186/1746-1596-8-S1-S13

Published: 30 September 2013

First paragraph (this article has no abstract)

In anatomical and surgical pathology, the customary method of manual observation and measurement of immunohistochemically stained markers from microscopic images is tedious, expensive and time consuming. There is great demand for automated procedures for analyzing digital images (DIs) of these markers [1] given that they reduce human variability in the evaluation of stained markers [2,3] and increase the speed and efficiency of the analysis [4]. Computerized DI analysis software generally involves a stained objects/nuclei segmentation method to detect and quantify the number of positively stained markers in combination with the standard evaluation of their morphometric and/or densitometric features [5,6]. However, automatic segmentation often fails due to the presence of spurious stain deposits in tissue sections (background). The “removal” of the background from noisy DIs, so that only the objects of interest are identified, is difficult due to the color values of pixels in the nuclei and background overlapping during the color segmentation processes.