An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing
Optical image processing is part of many applications used for brain surgeries. Microscope camera, or patient movement, like brain-movement through the pulse or a change in the liquor, can cause the image processing to fail. One option to compensate movement is feature detection and spatial allocati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2019-09-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2019-0069 |
| _version_ | 1811246902439575552 |
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| author | Sieler Konstantin Naber Ady Nahm Werner |
| author_facet | Sieler Konstantin Naber Ady Nahm Werner |
| author_sort | Sieler Konstantin |
| collection | DOAJ |
| description | Optical image processing is part of many applications used for brain surgeries. Microscope camera, or patient movement, like brain-movement through the pulse or a change in the liquor, can cause the image processing to fail. One option to compensate movement is feature detection and spatial allocation. This allocation is based on image features. The frame wise matched features are used to calculate the transformation matrix. The goal of this project was to evaluate different feature detectors based on spatial density and temporal robustness to reveal the most appropriate feature. The feature detectors included corner-, and blob-detectors and were applied on nine videos. These videos were taken during brain surgery with surgical microscopes and include the RGB channels. The evaluation showed that each detector detected up to 10 features for nine frames. The feature detector KAZE resulted in being the best feature detector in both density and robustness. |
| first_indexed | 2024-04-12T15:00:19Z |
| format | Article |
| id | doaj.art-9a927c0ad2114d86b26e07d6e3e484a6 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-04-12T15:00:19Z |
| publishDate | 2019-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-9a927c0ad2114d86b26e07d6e3e484a62022-12-22T03:28:06ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042019-09-015127327610.1515/cdbme-2019-0069cdbme-2019-0069An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image ProcessingSieler Konstantin0Naber Ady1Nahm Werner2Karlsruhe Institute of Technology (KIT), Institute of Biomedical Engineering (IBT), Kaiserstrasse 12,Karlsruhe, GermanyKarlsruhe Institute of Technology (KIT), Institute of Biomedical Engineering (IBT),Karlsruhe, GermanyKarlsruhe Institute of Technology (KIT), Institute of Biomedical Engineering (IBT),Karlsruhe, GermanyOptical image processing is part of many applications used for brain surgeries. Microscope camera, or patient movement, like brain-movement through the pulse or a change in the liquor, can cause the image processing to fail. One option to compensate movement is feature detection and spatial allocation. This allocation is based on image features. The frame wise matched features are used to calculate the transformation matrix. The goal of this project was to evaluate different feature detectors based on spatial density and temporal robustness to reveal the most appropriate feature. The feature detectors included corner-, and blob-detectors and were applied on nine videos. These videos were taken during brain surgery with surgical microscopes and include the RGB channels. The evaluation showed that each detector detected up to 10 features for nine frames. The feature detector KAZE resulted in being the best feature detector in both density and robustness.https://doi.org/10.1515/cdbme-2019-0069feature detectionkazesurfsiftharrismsermineigenbriskneurovascularspatialtemporal |
| spellingShingle | Sieler Konstantin Naber Ady Nahm Werner An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing Current Directions in Biomedical Engineering feature detection kaze surf sift harris mser mineigen brisk neurovascular spatial temporal |
| title | An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing |
| title_full | An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing |
| title_fullStr | An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing |
| title_full_unstemmed | An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing |
| title_short | An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing |
| title_sort | evaluation of image feature detectors based on spatial density and temporal robustness in microsurgical image processing |
| topic | feature detection kaze surf sift harris mser mineigen brisk neurovascular spatial temporal |
| url | https://doi.org/10.1515/cdbme-2019-0069 |
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