Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks.
The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This pa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6231616?pdf=render |
| _version_ | 1818580400560144384 |
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| author | Obioma Pelka Felix Nensa Christoph M Friedrich |
| author_facet | Obioma Pelka Felix Nensa Christoph M Friedrich |
| author_sort | Obioma Pelka |
| collection | DOAJ |
| description | The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basis for this work. To extensively evaluate the image enhancement techniques, five classification schemes including the complete IRMA code were adopted. The deep convolutional neural network systems Inception-v3 and Inception-ResNet-v2, and Random Forest models with 1000 trees were trained using extracted Bag-of-Keypoints visual representations. The classification model performances were evaluated using the ImageCLEF 2009 Medical Annotation Task test set. The applied visual enhancement techniques proved to achieve better annotation accuracy in all classification schemes. |
| first_indexed | 2024-12-16T07:17:00Z |
| format | Article |
| id | doaj.art-2ec83ce1ea4c4319bd2c54dd7dc1a595 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-16T07:17:00Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2ec83ce1ea4c4319bd2c54dd7dc1a5952022-12-21T22:39:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011311e020622910.1371/journal.pone.0206229Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks.Obioma PelkaFelix NensaChristoph M FriedrichThe number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basis for this work. To extensively evaluate the image enhancement techniques, five classification schemes including the complete IRMA code were adopted. The deep convolutional neural network systems Inception-v3 and Inception-ResNet-v2, and Random Forest models with 1000 trees were trained using extracted Bag-of-Keypoints visual representations. The classification model performances were evaluated using the ImageCLEF 2009 Medical Annotation Task test set. The applied visual enhancement techniques proved to achieve better annotation accuracy in all classification schemes.http://europepmc.org/articles/PMC6231616?pdf=render |
| spellingShingle | Obioma Pelka Felix Nensa Christoph M Friedrich Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks. PLoS ONE |
| title | Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks. |
| title_full | Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks. |
| title_fullStr | Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks. |
| title_full_unstemmed | Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks. |
| title_short | Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks. |
| title_sort | annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks |
| url | http://europepmc.org/articles/PMC6231616?pdf=render |
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