Analysis of Pancreas Histological Images for Glucose Intolerance Identification Using Wavelet Decomposition
Subtle structural differences can be observed in the islets of Langerhans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic (glucose intolerant) situations. This paper proposes a way to automatically segment the islets of Langerhans region from the 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-diabetic type. The work has two stages: primarily, segmentation of the region of interest (roi), i.e., islets of Langerhans from the pancreatic cell and secondly, the extraction of the morphological features from the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentation of the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM, etc, are used for evaluation among which MLP performed the best.
KeywordsAutomatic segmentation Pancreas cell Morphological feature Wavelet analysis Connected component Feature extraction Classification
The authors thank Professor Fernando Capela e Silva, from the Department of Biology and Ana R. Costa and Célia M. Antunes, from the Department of Chemistry, University of Évora, Portugal, for the data set used in this article.
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