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 color space and con- nected component analysis are used on 134 images among which 56 are of...

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Bibliographic Details
Main Author: Bandyopadhyay, Tathagata (author)
Other Authors: Mitra, Shyamali (author), Mitra, Sreetama (author), Nibaran, Das (author), Rato, Luis (author), Naskar, Mrinal (author)
Format: article
Language:por
Published: 2020
Subjects:
Automatic Segmentation
Histological image
Islets of Langerhans
β-cell
Diabetes,
Computerized Diagnostic System
Extreme Learning Machine
Online Access:http://hdl.handle.net/10174/27558
Country:Portugal
Oai:oai:dspace.uevora.pt:10174/27558
Description
Summary: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 color space and con- nected component analysis are used on 134 images among which 56 are of nor- mal and rest 78 are of pre-diabetic type. The paper also talks about a supervised learning approach for classifying the images based on their morphological fea- tures. 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.

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