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2016 | OriginalPaper | Chapter

Stacked Autoencoders for Medical Image Search

Authors : S. Sharma, I. Umar, L. Ospina, D. Wong, H. R. Tizhoosh

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenario. To solve this challenge, content-based image retrieval (CBIR) attempts to characterize images (or image regions) with invariant content information in order to facilitate image search. This work presents a feature extraction technique for medical images using stacked autoencoders, which encode images to binary vectors. The technique is applied to the IRMA dataset, a collection of 14,410 x-ray images in order to demonstrate the ability of autoencoders to retrieve similar x-rays given test queries. Using IRMA dataset as a benchmark, it was found that stacked autoencoders gave excellent results with a retrieval error of 376 for 1,733 test images with a compression of 74.61%.

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Metadata
Title
Stacked Autoencoders for Medical Image Search
Authors
S. Sharma
I. Umar
L. Ospina
D. Wong
H. R. Tizhoosh
Copyright Year
2016
Book
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_5

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