Please use this identifier to cite or link to this item:

Title: Analysis of pancreas histological images for glucose intolerance identificationusing ImageJ-preliminary results
Authors: Rato, L.M.
Capela e Silva, F.
Costa, A.R.
Antunes, C.M.
Editors: Tavares, J.M.
Natal Jorge, R.M.
Keywords: Analysis of Histological Images
Glucose Intolerance
Issue Date: 2013
Publisher: CRC Press
Citation: Rato LM, Capela e Silva F, Costa AR, Antunes CM. (2013) Analysis of pancreas histological images for glucose intolerance identificationusing ImageJ-preliminary results. Computational Vision and Medical Image Processing IV, VIPIMAGE 2013. Edited by João Manuel R. S. Tavares and R.M. Natal Jorge. CRC Press 2013, pp: 319–322.
Abstract: The observation in microscopy of histological sections allows us to evaluate structural differences, in pancreatic cells, between rats with normal glucose tolerance and with glucose intolerance (pre-diabetic) situation. Nevertheless, this pre-diabetic condition implies subtle changes in islets of Langerhans structure. This and the normal variability among sampled cells makes difficult the task of identifying glucose intolerance (pre-diabetic situation) with a low level of error. This paper presents preliminary results in the processing of histological pancreas images with the goal of identifying pre-diabetic situation in Wistar rats. The immediate goal of this work is to evaluate the performance of a classifier based in a morphometric measurement of the histological images and to assess the potential for image based automatic processing and classification. A set of 90 images, were used (58 from rats with normal glucose tolerance, and 32 from pre-diabetic ones). These images were segmented manually using ImageJ. This segmentation and area measurements have been speedup by the application of ImageJ macros which were defined for this purpose. The ratio, between the area of -cells and the islets of Langerhans , was used has the indicator of the prediabetic situation. Considering this feature, a receiver operating characteristic analysis has been performed. True positive rate, vs. false positive rate shows the predicted performance of a binary classifier as its discrimination threshold is varied.
ISBN: 978-1-138-00081-0
eBook ISBN 978-1-315-81292-2
Type: article
Appears in Collections:BIO - Artigos em Livros de Actas/Proceedings
MED - Artigos em Livros de Actas/Proceedings
QUI - Artigos em Livros de Actas/Proceedings
INF - Artigos em Livros de Actas/Proceedings

Files in This Item:

File Description SizeFormat
Analysis of Pancreas Histological Images.pdf274.17 kBAdobe PDFView/Open
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois