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

Muramatsu, Chisako; Hayashi, Yasuhiro; Sawada, Akira; Hatanaka, Yuji; Hara, Takeshi; Yamamoto, Tetsuya; Fujita, Hiroshi

doi:10.1117/1.3322388

Cited by 70 publications

(42 citation statements)

References 27 publications

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“…A dynamic programming-based search is employed to first find such regions and edge strength and shape-based information are used to finally classify the region. This is adapted for colour retinal images in [9] by first enhancing the edges via Gabor filtering before edge-based classification similar to [14]. The classification is performed using linear discriminant analysis and a artificial neural network employing statistical image features including area, edge length, average pixel value of the candidate region and region contrast before and after enhancement.…”

Section: Introductionmentioning

confidence: 99%

Detection of Peri-Papillary Atrophy and RNFL Defect from Retinal Images

Joshi

Sivaswamy

Prashanth

et al. 2012

Lecture Notes in Computer Science

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Abstract. The task of detecting peri-papillary indicators associated with the glaucoma is challenging due to the high degree of intra-and inter-image variations commonly seen in colour retinal images. The existing approaches based on direct modeling of the region of interest fail to handle such image variations which compromises detection accuracy. In this paper, a novel detection strategy is proposed which exploits the saliency property associated with these indicators. The region of interest is modeled as a region substantially different from the adjacent image regions. This dissimilarity information is derived at the feature level, between the target and its adjacent regions. Based on the proposed strategy, two novel methods are presented for the detection of peri-papillary atrophy and RNFL defect from colour retinal images. Two different datasets have been used to evaluate the performance of developed solutions. The obtained results are encouraging and establish the strength of the proposed strategy in handling high degree of image variations. The preliminary results and comparative evaluation with direct modeling strategy show viability of proposed strategy to be used in the glaucoma assessment task.

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Section: Introductionmentioning

confidence: 99%

Detection of Peri-Papillary Atrophy and RNFL Defect from Retinal Images

Joshi

Sivaswamy

Prashanth

et al. 2012

Lecture Notes in Computer Science

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“…From a clinical viewpoint, the enhancement can be used for a more precise assessment of a patient. Likewise, the images are more suitable for subsequent processing such as for the detection of retinal pathology [23,24].…”

Section: A Space-invariant (Si) Deconvolutionmentioning

confidence: 99%

Retinal Image Analysis: Image Processing and Feature Extraction Oriented to the Clinical Task

Marrugo¹,

Millán²

2017

Opt. Pura Apl.

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ABSTRACT:Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract quantitative information about many diseases. Modern ophthalmology relies on the advances in digital imaging and computing power. In this paper we present an overview of the results from the doctoral dissertation by Andrés G. Marrugo. This dissertation contributes to the digital analysis of retinal images and the problems that arise along the imaging pipeline of fundus photography, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination compensation, poor image quality, automated focusing, image segmentation, change detection, space-invariant (SI) and space-variant (SV) blind deconvolution (BD). Digital retinal image analysis can be effective and cost-efficient for disease management, computeraided diagnosis, screening and telemedicine and applicable to a variety of disorders such as glaucoma, macular degeneration, and retinopathy.Key words: Computer-aided diagnosis, medical image, retinal image, telemedicine, ophthalmology. RESUMEN:La captura de imagen digital es una parte fundamental de los procedimientos médicos modernos. Provee de documentación visual, un registro permanente para los pacientes y la posibilidad de extraer información cuantitativa sobre muchas enfermedades. La oftalmología depende considerablemente del análisis digital de imágenes. En este trabajo se presentan los resultados principales de la tesis doctoral de Andrés G. Marrugo. Este trabajo contribuye al análisis digital de tales imágenes y los problemas que surgen a lo largo del proceso de formación de imagen. Este campo se le conoce como análisis de imagen retiniana. En esta tesis se han propuesto soluciones a problemas asociados con la adquisición de imagen retiniana y la detección de cambios temporales en patologías retinianas. Específicamente, los problemas de iluminación no-uniforme, baja calidad de imagen, enfoque automático, y análisis multi-canal. Sin embargo, existen situaciones inevitables en que se adquieren imágenes de baja calidad, como imágenes emborronadas debido a las aberraciones del ojo. Este problema lo hemos abordado utilizando dos metodologías para la deconvolución ciega de imágenes. En la primera ÓPTICA PURA Y APLICADA. www.sedoptica.esOpt. Pura Apl. 50 (1) 49-62 (2017) © Sociedad Española de Óptica 50 aproximación, consideramos que el emborronamiento es invariante espacialmente y en la segunda aproximación extendimos el trabajo y propusimos un método más general espacialmente variante.Palabras clave: diagnóstico asistido por computador, imagen médica, imagen retiniana, telemedicina, oftalmología. [24] L. Xu and S. Luo, "Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristi...

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“…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. Moreover, we tried to develop a depth analysis of optic nerve head (ONH) in stereo retinal fundus images pair [15], and a C/D ratio measurement by using the depth of ONH [16].…”

Section: Introductionmentioning

confidence: 99%

“…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

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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.

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

Cited by 70 publications

References 27 publications

Detection of Peri-Papillary Atrophy and RNFL Defect from Retinal Images

Detection of Peri-Papillary Atrophy and RNFL Defect from Retinal Images

Retinal Image Analysis: Image Processing and Feature Extraction Oriented to the Clinical Task

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

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