A Fast Algorithm for Automatic Segmentation of Pancreas Histological Images for Glucose Intolerance Identification
This paper describes a novel fast algorithm for automatic segmentation of islets of Langerhans and β-cell region from pancreas histological images for automatic identification of glucose intolerance. Here, LUV colour space and connected component analysis are used on 134 images among which 56 are of normal and rest 78 are of prediabetic type. The paper also talks about a supervised learning approach for classifying the images based on their morphological features. In the present work, we have introduced a modern classifier weighted ELM (Extreme Learning Machine) for prediabetes identification. Performances of weighted ELM are comparable with all the present-day’s robust classifiers such as Support Vector Machines (SVM), Multilayer Perceptron (MLP), etc. We have also compared the result with traditional ELM and observed better performance in the present skewed dataset with substantial improvement in training time.
KeywordsAutomatic segmentation Histological image Islets of Langerhans β-cell Diabetes Computerized diagnostic system Extreme learning machine
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 dataset used in this article.
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