OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis
The accurate diagnosis of otitis media (OM) and other middle ear and eardrum abnormalities is difficult, even for experienced otologists. In our earlier studies, we developed computer-aided diagnosis systems to improve the diagnostic accuracy. In this study, we investigate a novel approach, called O...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1831 |
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| author | Seda Camalan Aaron C. Moberly Theodoros Teknos Garth Essig Charles Elmaraghy Nazhat Taj-Schaal Metin N. Gurcan |
| author_facet | Seda Camalan Aaron C. Moberly Theodoros Teknos Garth Essig Charles Elmaraghy Nazhat Taj-Schaal Metin N. Gurcan |
| author_sort | Seda Camalan |
| collection | DOAJ |
| description | The accurate diagnosis of otitis media (OM) and other middle ear and eardrum abnormalities is difficult, even for experienced otologists. In our earlier studies, we developed computer-aided diagnosis systems to improve the diagnostic accuracy. In this study, we investigate a novel approach, called OtoPair, which uses paired eardrum images together rather than using a single eardrum image to classify them as ‘normal’ or ‘abnormal’. This also mimics the way that otologists evaluate ears, because they diagnose eardrum abnormalities by examining both ears. Our approach creates a new feature vector, which is formed with extracted features from a pair of high-resolution otoscope images or images that are captured by digital video-otoscopes. The feature vector has two parts. The first part consists of lookup table-based values created by using deep learning techniques reported in our previous OtoMatch content-based image retrieval system. The second part consists of handcrafted features that are created by recording registration errors between paired eardrums, color-based features, such as histogram of a* and b* component of the L*a*b* color space, and statistical measurements of these color channels. The extracted features are concatenated to form a single feature vector, which is then classified by a tree bagger classifier. A total of 150-pair (300-single) of eardrum images, which are either the same category (normal-normal and abnormal-abnormal) or different category (normal-abnormal and abnormal-normal) pairs, are used to perform several experiments. The proposed approach increases the accuracy from 78.7% (±0.1%) to 85.8% (±0.2%) on a three-fold cross-validation method. These are promising results with a limited number of eardrum pairs to demonstrate the feasibility of using a pair of eardrum images instead of single eardrum images to improve the diagnostic accuracy. |
| first_indexed | 2024-03-09T00:45:12Z |
| format | Article |
| id | doaj.art-efe7ce2ed0ca464ab8ba082b80a7a966 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T00:45:12Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-efe7ce2ed0ca464ab8ba082b80a7a9662023-12-11T17:34:32ZengMDPI AGApplied Sciences2076-34172021-02-01114183110.3390/app11041831OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image AnalysisSeda Camalan0Aaron C. Moberly1Theodoros Teknos2Garth Essig3Charles Elmaraghy4Nazhat Taj-Schaal5Metin N. Gurcan6Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC 27101, USADepartment of Otolaryngology, Ohio State University, Columbus, OH 43212, USAUniversity Hospitals Seidman Cancer Center, Cleveland, OH 44106, USADepartment of Otolaryngology, Ohio State University, Columbus, OH 43212, USADepartment of Otolaryngology, Ohio State University, Columbus, OH 43212, USADepartment of Internal Medicine, Ohio State University, Columbus, OH 43210, USACenter for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC 27101, USAThe accurate diagnosis of otitis media (OM) and other middle ear and eardrum abnormalities is difficult, even for experienced otologists. In our earlier studies, we developed computer-aided diagnosis systems to improve the diagnostic accuracy. In this study, we investigate a novel approach, called OtoPair, which uses paired eardrum images together rather than using a single eardrum image to classify them as ‘normal’ or ‘abnormal’. This also mimics the way that otologists evaluate ears, because they diagnose eardrum abnormalities by examining both ears. Our approach creates a new feature vector, which is formed with extracted features from a pair of high-resolution otoscope images or images that are captured by digital video-otoscopes. The feature vector has two parts. The first part consists of lookup table-based values created by using deep learning techniques reported in our previous OtoMatch content-based image retrieval system. The second part consists of handcrafted features that are created by recording registration errors between paired eardrums, color-based features, such as histogram of a* and b* component of the L*a*b* color space, and statistical measurements of these color channels. The extracted features are concatenated to form a single feature vector, which is then classified by a tree bagger classifier. A total of 150-pair (300-single) of eardrum images, which are either the same category (normal-normal and abnormal-abnormal) or different category (normal-abnormal and abnormal-normal) pairs, are used to perform several experiments. The proposed approach increases the accuracy from 78.7% (±0.1%) to 85.8% (±0.2%) on a three-fold cross-validation method. These are promising results with a limited number of eardrum pairs to demonstrate the feasibility of using a pair of eardrum images instead of single eardrum images to improve the diagnostic accuracy.https://www.mdpi.com/2076-3417/11/4/1831acute otitis mediaeardrum classificationotoscopytransfer learning |
| spellingShingle | Seda Camalan Aaron C. Moberly Theodoros Teknos Garth Essig Charles Elmaraghy Nazhat Taj-Schaal Metin N. Gurcan OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis Applied Sciences acute otitis media eardrum classification otoscopy transfer learning |
| title | OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis |
| title_full | OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis |
| title_fullStr | OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis |
| title_full_unstemmed | OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis |
| title_short | OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis |
| title_sort | otopair combining right and left eardrum otoscopy images to improve the accuracy of automated image analysis |
| topic | acute otitis media eardrum classification otoscopy transfer learning |
| url | https://www.mdpi.com/2076-3417/11/4/1831 |
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