Vision-based, real-time retinal image quality assessment

Davis, Herbert; Russell, Stephen R.; Barriga, E. Simon; Abràmoff, Michael D.; Solíz, Peter

doi:10.1109/cbms.2009.5255437

Cited by 48 publications

(43 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are several image based features which have been represent different retinal structures in fundus images such as colour, illumination, intensity, skewness, texture, histogram, sharpness etc [4,14,5]. For reducing computational complexity, grid analysis containing small patches of the image has been proposed.…”

Section: Literature Surveymentioning

confidence: 99%

“…For reducing computational complexity, grid analysis containing small patches of the image has been proposed. [4] and the mean response of each feature aggregated over each patch was taken into account. The features of Region of Interest (ROI) of anatomical structures such as Optic Nerve Head (ONH) and Fovea have also been analyzed [10].…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Superpixel Based Retinal Area Detection in SLO Images

Haleem

Han

Hemert

et al. 2014

Computer Vision and Graphics

View full text Add to dashboard Cite

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Superpixel Based Retinal Area Detection in SLO Images

Haleem

Han

Hemert

et al. 2014

Computer Vision and Graphics

View full text Add to dashboard Cite

“…[6][7][8][9][10] A shortcoming of local analysis is the processing time that, usually, is longer than in global techniques. Segmentation of retinal features such as optic disc, fovea, and retinal vasculature is also included in some methods to augment specificity of the algorithms to fundus images.…”

Section: Introductionmentioning

confidence: 99%

Quality evaluation of digital fundus images through combined measures

Veiga

Pereira²,

Ferreira

et al. 2014

J. Med. Imag

View full text Add to dashboard Cite

Abstract. The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.

show abstract

“…PLS selects the most relevant features required for classification. Apart from calculating image features for whole image, grid analysis containing small patches of the image has also been proposed for reducing computational complexity [8]. For determining image quality, the features of Region of Interest (ROI) of anatomical structures such as Optic Nerve Head (ONH) and Fovea have also been analyzed [23].…”

Section: Introductionmentioning

confidence: 99%

“…The characterisation of retinal images were performed in terms of image features such as intensity, skewness, textural analysis, histogram analysis, sharpness etc [8], [12], [28]. Dias et al [9] determined four different classifiers using four types of features.…”

Section: Introductionmentioning

confidence: 99%

Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Haleem

Han

Hemert

et al. 2015

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

• This is the accepted manuscript version of the paper. c 2014 IEEE. Abstract-Scanning Laser Ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide Field of View (FOV), which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.

show abstract

Vision-based, real-time retinal image quality assessment

Cited by 48 publications

References 12 publications

Superpixel Based Retinal Area Detection in SLO Images

Superpixel Based Retinal Area Detection in SLO Images

Quality evaluation of digital fundus images through combined measures

Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Contact Info

Product

Resources

About