Retinal image quality assessment, error identification and automatic quality correction

a retinal image and error identification technology, applied in the field of computer and computer imaging, can solve the problems of poor quality images that have to be discarded, poor generalization, and little exploration of medical image quality assessmen

Active Publication Date: 2017-09-21
IBM CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Medical image quality assessment has not be much explored since many studies report a significant percentage of acquired study images to be of insufficient quality for an automated assessment.
Poor quality images have to be discarded.
Existing approaches to IQA use hand crafted features which are not inclusive and do not generalize well to new datasets.

Method used

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  • Retinal image quality assessment, error identification and automatic quality correction
  • Retinal image quality assessment, error identification and automatic quality correction
  • Retinal image quality assessment, error identification and automatic quality correction

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Embodiment Construction

[0024]A system, method and techniques may be presented that determine image quality of a machine acquired image, for example, retinal scans. The system, method and techniques in one embodiment may include combining unsupervised information from visual saliency maps and supervised information from trained convolutional neural networks (CNNs). In one embodiment, neurobiological principles behind the working of the human visual system may be employed for classifying images as gradable or ungradable. Saliency values may be computed for every pixel, for example, instead of identifying salient regions as done in conventional approaches. Multiscale saliency maps for intensity, texture and curvature features, and filtering operations allows the system and method of the present disclosure in one embodiment to capture information from local and global scales. In one embodiment, additional neurobiological information from the trained CNNs may be extracted.

[0025]In one aspect, combining the two...

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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.

Description

FIELD[0001]The present application relates generally to computers and computer imaging, and more particularly to automatic image quality assessment and correction.BACKGROUND[0002]Retinal image quality assessment (IQA) is a step in screening systems for diseases like diabetic retinopathy (DR), glaucoma and age related macular degeneration (AMD) which require rapid and accurate evaluation. For example, color funds retinal image assessment is used to diagnose such diseases. Digital fundus photography of the retina is an effective non-invasive examination medium of many retinal conditions with the potential to reduce workload of ophthalmologists and increase the cost effectiveness of screening systems. Medical image quality assessment has not be much explored since many studies report a significant percentage of acquired study images to be of insufficient quality for an automated assessment. Poor quality images have to be discarded. Existing approaches to IQA use hand crafted features w...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06T7/00G06K9/62G06K9/66G06V10/764
CPCG06T7/0002G06T2207/30168G06K9/6267G06K9/66G06T2207/30041G06N3/082G06N20/20G06V40/193G06V10/993G06V10/454G06V2201/03G06V10/82G06V10/764G06N5/01G06N3/045G06F18/24143
Inventor GARNAVI, RAHILMAHAPATRA, DWARIKANATHROY, PALLAB K.SEDAI, SUMAN
Owner IBM CORP
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