Automated quantification of retinal nerve fiber layer atrophy in fundus photograph

Lee, S.Y.; Kim, K.K.; Seo, Jong-Mo; Kim, D.M.; Chung, Hum; Park, K.S.; Kim, H.C.

doi:10.1109/iembs.2004.1403394

Cited by 15 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Few papers deal with RNFL texture analysis using FC images. The paper [5] presents simple analysis of RNFL damage considering only brightness information around the optic disc (OD -terminal of the neural bundle on retina). In the paper [1], Gabor filtering was applied to enhance the dark bands in areas containing atrophy of RNFL; 71% of atrophied areas were detected.…”

Section: Introductionmentioning

confidence: 99%

Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography

Gazárek

Jan

Kolář

et al. 2011

2011 International Conference on Image Information Processing

View full text Add to dashboard Cite

Two approaches to evaluation of the retinal nerve fibre layer (RNFL) status are compared in the contribution: detection of the local existence of the RNFL in the fundus camera (FC) images versus the objective measurement of the RNFL thickness at identical retinal locations by the optical coherence tomography (OCT). The local detection results based on a compound texture analysis in FC images were compared with the objective OCT measurements considered a contemporary gulden standard, after precisely registering the image data from both modalities. The contribution shows that the reliability of the textural detection in FC images is rather high, reaching the specificity 88.9 % and sensitivity 88.6 % on the so far available material of pairs of FC images and corresponding OCT measurements. The comparison shows the feasibility of the cheap FC image based approach suitable particularly also for screening purposes.

show abstract

Section: Introductionmentioning

confidence: 99%

Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography

Gazárek

Jan

Kolář

et al. 2011

2011 International Conference on Image Information Processing

View full text Add to dashboard Cite

show abstract

“…Thus, many researches have been trying to develop computer-aided diagnosis (CAD) systems, which analyze retinal fundus images for diagnosis of glaucoma [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Measurement of vertical cup-to-disc (C/D) ratio methods [2][3][4], a glaucoma risk assessment based on pixels analysis in an optic nerve head (ONH) [5], peripapillary chorioretinal atrophy (PPA) detection methods [6,7] and nerve fiber layer defects (NFLDs) detection methods [8][9][10][11][12][13][14][15][16] were reported. To help glaucoma diagnosis, we developed a measurement of vertical C/D ratio method [4], a PPA detection method [7], and a NFLDs detection method [14] on retinal fundus images, respectively.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma risk assessment based on clinical data and automated nerve fiber layer defects detection

Hatanaka

Muramatsu

Sawada

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Glaucoma is the first leading cause of vision loss in Japan, thus developing a scheme for helping glaucoma diagnosis is important. For this problem, automated nerve fiber layer defects (NFLDs) detection method was proposed, but glaucoma risk assessment using this method was not evaluated. In this paper, computerized risk assessment for having glaucoma was attempted by use of the patients' clinical information, and the performances of the NFLDs detection and the glaucoma risk assessment were compared. The clinical data includes the systemic data, ophthalmologic data, and right and left retinal images. Glaucoma risk assessment was built by using machine learning technique, which were artificial neural network, radial basis function (RBF) network, k-nearest neighbor algorithm, and support vector machine. The inputting parameter was ten clinical ones with/without the results of NFLDs detection. As a result, proposed glaucoma risk assessment showed the higher performance than the NFLD detection. The result of the glaucoma risk assessment indicates that the computerized assessment may be useful for the determination of glaucoma risk.

show abstract

“…6,7 There have been studies about the analysis of RNFL using fundus photographs, 8,9 scanning laser polarimetry, [10][11][12][13] and optical coherence tomography [13][14][15][16][17] for diagnosis of glaucoma. Although more studies were focused on new technologies, computerized detection and quantification of NFLDs in fundus photographs [18][19][20][21][22][23] can be important because of its routine use.…”

Section: Introductionmentioning

confidence: 99%

“…18 Lee et al introduced an automated method for quantifying nerve fiber layer based on the intensity profile around the optic disk. 19 By taking the first derivative of the intensity plot, the edges of NFLDs can be found; however, the method was influenced by the presence of blood vessels. Song et al suggested a computer-based glaucoma diagnosis system, a part of which includes image processing techniques for enhancement of NFLDs.…”

Section: Introductionmentioning

confidence: 99%

Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma

et al. 2010

View full text Add to dashboard Cite

Retinal nerve fiber layer defect (NFLD) is a major sign of glaucoma, which is the second leading cause of blindness in the world. Early detection of NFLDs is critical for improved prognosis of this progressive, blinding disease. We have investigated a computerized scheme for detection of NFLDs on retinal fundus images. In this study, 162 images, including 81 images with 99 NFLDs, were used. After major blood vessels were removed, the images were transformed so that the curved paths of retinal nerves become approximately straight on the basis of ellipses, and the Gabor filters were applied for enhancement of NFLDs. Bandlike regions darker than the surrounding pixels were detected as candidates of NFLDs. For each candidate, image features were determined and the likelihood of a true NFLD was determined by using the linear discriminant analysis and an artificial neural network (ANN). The sensitivity for detecting the NFLDs was 91% at 1.0 false positive per image by using the ANN. The proposed computerized system for the detection of NFLDs can be useful to physicians in the diagnosis of glaucoma in a mass screening.

show abstract

Automated quantification of retinal nerve fiber layer atrophy in fundus photograph

Cited by 15 publications

References 4 publications

Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography

Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography

Glaucoma risk assessment based on clinical data and automated nerve fiber layer defects detection

Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma

Contact Info

Product

Resources

About