Retinal image quality assessment, error identification and automatic quality correction
US9779492B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 15, 2016 |
| Grant date | Oct 3, 2017 |
| Priority date | — |
| Expiry date | Mar 31, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Automatically determining image quality of a machine generated image may generate a local saliency map of the image to obtain a set of unsupervised features. The image is run through a trained convolutional neural network (CNN) to extract a set of supervised features from a fully connected layer of the CNN, the image convolved with a set of learned kernels from the CNN to obtain a complementary set of supervised features. The set of unsupervised features and the complementary set of supervised features are combined, and a first decision on gradability of the image is predicted. A second decision on gradability of the image is predicted based on the set of supervised features. Whether the image is gradable is determined based on a weighted combination of the first decision and the second decision.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.