2008
DOI: 10.1007/s10916-008-9210-4
|View full text |Cite
|
Sign up to set email alerts
|

A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images

Abstract: Day by day, huge amount of information is collected in medical databases. These databases include quite interesting information that could be exploited in diagnosis of illnesses and medical treatment of patients. Classification of these data is getting harder as the databases are expanded. On the other hand, automated image analysis and processing is one of the most promising areas of computer vision used in medical diagnosis and treatment. In this context, retinal fundus images, offering very high resolutions… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
46
0

Year Published

2010
2010
2022
2022

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 53 publications
(46 citation statements)
references
References 23 publications
0
46
0
Order By: Relevance
“…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%
“…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%
“…Hard drusen have a well defined border, while soft drusen have boundaries that often blend into the retinal background. Figure 2 [4,13,22,23,29] as opposed to AMD classification. The work proposed here however approaches the AMD screening problem without the need for identification of the physical existence of drusen and aims to classify images as either "AMD" or "non-AMD".…”
Section: Age-related Macular Degenerationmentioning
confidence: 99%
“…More recent work [4] used a wavelet analysis technique to extract drusen patterns, and multi-level classification (based on various criteria) for drusen categorisation. Other works on the identification of drusen in retina images has focuses on segmentation coupled with image enhancement approaches [22,23,29]. Rapantzikos et al [29] adopted a multilevel histogram equalisation to enhance the image contrast followed by drusen segmentation, in which two types of threshold, global and local, were applied to retinal images.…”
Section: Previous Workmentioning
confidence: 99%
See 2 more Smart Citations