Automatic Diabetes Detection from Histological Images of Rats Phrenic Nerve Using Two-Dimensional Sample Entropy

In microscopy, morphological characteristic of the axon are the most common features assessed in histological images of nerves. Although morphometric indexes are widely used to describe histological data, the calculation of those indexes is a highly time-consuming task that demands great manual effo...

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Bibliographic Details
Published in:Journal of medical and biological engineering 2019-02, Vol.39 (1), p.70-75
Main Authors: da Silva Senra Filho, Antonio Carlos, Duque, Juliano Jinzenji, Silva, Luiz Eduardo Virgilio, Felipe, Joaquim Cesar, Sassoli Fazan, Valéria Paula, Junior, Luiz Otávio Murta
Format: Article
Language:English
Subjects:
Animals
Biomedical Engineering and Bioengineering
Cell Biology
Diabetes
Diabetes mellitus
Engineering
Entropy
Image classification
Image detection
Imaging
Insulin
Microscopy
Nerves
Original Article
Performance indices
Phrenic nerve
Physical characteristics
Radiology
Rodents
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Summary:In microscopy, morphological characteristic of the axon are the most common features assessed in histological images of nerves. Although morphometric indexes are widely used to describe histological data, the calculation of those indexes is a highly time-consuming task that demands great manual effort from the specialist. Recently, two-dimensional sample entropy (SampEn2D) was proposed to quantify the degree of irregularity present in an image, based on the spatial patterns of pixels. Here, we propose the use of SampEn2D as a suitable metric for classifying diabetic status of rats from histological images of the phrenic nerve. Microscopy images of three different Wistar rats groups (untreated diabetic (N = 24), insulin-treated diabetic (N = 9), and non-diabetic control (N = 11)) were analysed. The results show that for the optimal SampEn2D parameters (m = 1, r = 0.1), control rats have significantly (p 
ISSN:1609-0985
2199-4757
DOI:10.1007/s40846-018-0382-1