Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/12/277 |
| _version_ | 1797380478461804544 |
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| author | Jiajun Zhang Georgina Cosma Sarah Bugby Jason Watkins |
| author_facet | Jiajun Zhang Georgina Cosma Sarah Bugby Jason Watkins |
| author_sort | Jiajun Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:37:50Z |
| format | Article |
| id | doaj.art-708b65f15a6c4a94a39313ab32bf8361 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-08T20:37:50Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-708b65f15a6c4a94a39313ab32bf83612023-12-22T14:18:13ZengMDPI AGJournal of Imaging2313-433X2023-12-0191227710.3390/jimaging9120277Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare DatasetsJiajun Zhang0Georgina Cosma1Sarah Bugby2Jason Watkins3Department of Computer Science, School of Science, Loughborough University, Loughborough LE11 3TT, UKDepartment of Computer Science, School of Science, Loughborough University, Loughborough LE11 3TT, UKDepartment of Physics, School of Science, Loughborough University, Loughborough LE11 3TT, UKRailston & Co., Ltd., Nottingham NG7 2TU, UKImage 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.https://www.mdpi.com/2313-433X/9/12/277image retrievalmorphological defect characteristicsirregular pattern analysis |
| spellingShingle | Jiajun Zhang Georgina Cosma Sarah Bugby Jason Watkins Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets Journal of Imaging image retrieval morphological defect characteristics irregular pattern analysis |
| title | Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets |
| title_full | Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets |
| title_fullStr | Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets |
| title_full_unstemmed | Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets |
| title_short | Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets |
| title_sort | efficient retrieval of images with irregular patterns using morphological image analysis applications to industrial and healthcare datasets |
| topic | image retrieval morphological defect characteristics irregular pattern analysis |
| url | https://www.mdpi.com/2313-433X/9/12/277 |
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