Abstract:
Image quality assessment has been a topic of intense research over the last decades. Although its application to other disciplines is growing tremendously, its use in ret...Show MoreMetadata
Abstract:
Image quality assessment has been a topic of intense research over the last decades. Although its application to other disciplines is growing tremendously, its use in retinal imaging is still immature and some fundamental challenges remain unsolved. Thus, we present a research methodology for the objective assessment of the quality in retinal images. The methodology can be used as a preliminary step in any computer-aided system, and is composed of four main steps: the location of the region-of-interest, the extraction of relevant image properties and their analysis by feature selection, and the final binary classification into two classes (good and poor quality). The experimental results demonstrate the adequacy of the proposed methodology in this context, being able to objectively assess the quality of retinal images with an accuracy over 99%.
Date of Conference: 14-19 May 2017
Date Added to IEEE Xplore: 03 July 2017
ISBN Information:
Electronic ISSN: 2161-4407
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Quality Assessment ,
- Objective Assessment ,
- Texture Features ,
- Imaging Assessment ,
- Retinal Images ,
- Objective Quality Assessment ,
- Poor Quality ,
- Image Quality ,
- Binary Classification ,
- Computer-aided System ,
- Support Vector Machine ,
- Input Image ,
- Grayscale Images ,
- Optic Nerve Head ,
- Color Space ,
- Texture Analysis ,
- Feature Subset ,
- Color Features ,
- Feature Selection Methods ,
- Color Properties ,
- Gaussian Markov Random Field ,
- RGB Color Space ,
- Co-occurrence Features ,
- CIELAB Color Space ,
- Mother Wavelet ,
- Gray Level Co-occurrence Matrix ,
- Color Model ,
- Angular Second Moment ,
- Correlation-based Feature Selection ,
- Good Image Quality
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Quality Assessment ,
- Objective Assessment ,
- Texture Features ,
- Imaging Assessment ,
- Retinal Images ,
- Objective Quality Assessment ,
- Poor Quality ,
- Image Quality ,
- Binary Classification ,
- Computer-aided System ,
- Support Vector Machine ,
- Input Image ,
- Grayscale Images ,
- Optic Nerve Head ,
- Color Space ,
- Texture Analysis ,
- Feature Subset ,
- Color Features ,
- Feature Selection Methods ,
- Color Properties ,
- Gaussian Markov Random Field ,
- RGB Color Space ,
- Co-occurrence Features ,
- CIELAB Color Space ,
- Mother Wavelet ,
- Gray Level Co-occurrence Matrix ,
- Color Model ,
- Angular Second Moment ,
- Correlation-based Feature Selection ,
- Good Image Quality