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Title: Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition
Authors: Bandyopadhyay, Tathagata
Mitra, Sreetama
Mitra, Shyamali
Rato, Luís
Das, Nibaran
Editors: Satapathy, S.C.
Bhateja, V.
Udgata, S.K.
Pattnaik, P.K.
Keywords: image
Issue Date: Sep-2016
Publisher: Springer
Citation: Bandyopadhyay, T., Mitra, (Sretama), Mitra, (Shyamali), Rato, L., Das, N., Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition, Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2016, Springer, 2016.
Abstract: Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best.
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

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