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|Title: ||Analysis of pancreas histological images for glucose intolerance identificationusing ImageJ-preliminary results|
|Authors: ||Rato, L.M.|
Capela e Silva, F.
|Editors: ||Tavares, J.M.|
Natal Jorge, R.M.
|Keywords: ||Analysis of Histological Images|
|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.|
eBook ISBN 978-1-315-81292-2
|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
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