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Efficient retrieval of images with irregular patterns using morphological image analysis: applications to industrial and healthcare datasets

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posted on 2024-01-04, 09:06 authored by Jiajun Zhang, Georgina CosmaGeorgina Cosma, Sarah BugbySarah Bugby, Jason Watkins
Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets.

Funding

School of Science at Loughborough University

Railston & Co., Ltd

History

School

  • Science

Department

  • Computer Science
  • Physics

Published in

Journal of Imaging

Volume

9

Issue

12

Publisher

MDPI

Version

  • VoR (Version of Record)

Rights holder

© the authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2023-12-08

Publication date

2023-12-13

Copyright date

2023

eISSN

2313-433X

Language

  • en

Depositor

Prof Georgina Cosma. Deposit date: 21 December 2023

Article number

277

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