Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration

Rapantzikos, Konstantinos; Zervakis, Michalis; Balas, K.

doi:10.1016/s1361-8415(02)00093-2

Cited by 129 publications

(94 citation statements)

References 15 publications

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“…The earliest work [24] used a morphological mechanism to localise drusen. Other image processing techniques that have been applied include: (i) histogram-based adaptive local thresholding [22] , (ii) region growing [19,20]; (iii) wavelet based feature identification coupled with multilevel classification [3]; (iv) anomaly detection based approaches, that employ Support Vector Data Description (SVDD), to segment anomalous pixels [11]; and (v) signal based approaches, namely amplitude-modulation frequency-modulation (AM-FM), to generate multi-scale features for drusen classification [1,2]. Content-Based Image Retrieval (CBIR) techniques have also been applied.…”

Section: Previous Workmentioning

confidence: 99%

Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

Hijazi

Coenen

Zheng

2011

Machine Learning and Knowledge Discovery in Databases

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Abstract. Age-related Macular Degeneration (AMD) is the most common cause of adult blindness in the developed world. This paper describes a new image mining technique to perform automated detection of AMD from colour fundus photographs. The technique comprises a novel hierarchical image decomposition mechanism founded on a circular and angular partitioning. The resulting decomposition is then stored in a tree structure to which a weighted frequent sub-tree mining algorithm is applied. The identified sub-graphs are then incorporated into a feature vector representation (one vector per image) to which classification techniques can be applied. The results show that the proposed approach performs both efficiently and accurately.

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Section: Previous Workmentioning

confidence: 99%

Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

Hijazi

Coenen

Zheng

2011

Machine Learning and Knowledge Discovery in Databases

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“…Histogram-based adaptive local thresholding was developed to extract useful information from an image without being affected by the presence of other structures 52 . Drusen visibility in various colour spaces was investigated, and found to agree with previous investigation 68 that the green band is most informative and least affected by the overall variation of illumination.…”

Section: A Histogram-based Adaptive Local Thresholding (Halt)mentioning

confidence: 99%

“…The darker areas belonging to larger drusen need to be enhanced further. The second stage involves generating more distance between the 'hidden' anomalies and their surrounding areas 52 .…”

Section: A Histogram-based Adaptive Local Thresholding (Halt)mentioning

confidence: 99%

Use of Fundus Imaging in Quantification of Age-related Macular Change

Bartlett

Eperjesi

2007

Survey of Ophthalmology

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“…These methods first detect local maxima, then further classify the candidates according to contrast, size and shape. The second category consists of the local threshold based methods, e.g., Histogram based Adaptive Local Thresholding (HALT) [9], and Otsu method based adaptive threshold [10]. The third category includes the ones from frequency domain, e.g., wavelet [3], Fourier transform [12], and amplitude-modulation frequency modulation (AM-FM) [1].…”

Section: Introductionmentioning

confidence: 99%

ACHIKO-D350: A dataset for early AMD detection and drusen segmentation

Liu

Wong

et al. 2014

Proceedings of the Ophthalmic Medical Image Analysis First International Workshop

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Abstract. Age related macular degeneration is the third leading cause of global blindness. Its prevalence is increasing in these years for the coming of "aging population". Early detection and grading can prevent it from becoming severe and protect vision. Drusen is an important indicator for AMD. Thus automatic drusen detection and segmentation has attracted much research attention in the past years. However, a barrier handicapping the research of drusen segmentation is the lack of a public dataset and test platform. To address this issue, in this paper, we publish a dataset, named ACHIKO-D350, with manually marked drusen boundary. ACHIKO-D350 includes 254 healthy fundus images and 96 fundus images with drusen. The images with drusen cover a wide range of types, including images with sparsely distributed drusen or clumped drusen, images of poor quality, and both well macular centered images and mis-centered images. ACHIKO-D350 will be used for performance evaluation of drusen segmentation methods. It will facilitate an objective evaluation and comparison.

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Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration

Cited by 129 publications

References 15 publications

Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

Use of Fundus Imaging in Quantification of Age-related Macular Change

ACHIKO-D350: A dataset for early AMD detection and drusen segmentation

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