Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments
Significant information extraction from the images that are geometrically distorted or transformed is mainstream procedure in image processing. It becomes difficult to retrieve the relevant region when the images get distorted by some geometric deformation. Hu's moments are helpful in extractin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2020.591827/full |
| Summary: | Significant information extraction from the images that are geometrically distorted or transformed is mainstream procedure in image processing. It becomes difficult to retrieve the relevant region when the images get distorted by some geometric deformation. Hu's moments are helpful in extracting information from such distorted images due to their unique invariance property. This work focuses on early detection and gradation of Knee Osteoarthritis utilizing Hu's invariant moments to understand the geometric transformation of the cartilage region in Knee X-ray images. The seven invariant moments are computed for the rotated version of the test image. The results demonstrated are found to be more competitive and promising, which are validated by ortho surgeons and rheumatologists. |
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| ISSN: | 2296-9144 |