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" Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel Density. "
Luca Giancardo
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BL
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| Record Number
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780163
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b600170
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| Main Entry
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Luca Giancardo
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| Title & Author
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Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel Density.\ Luca Giancardo
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| Publication Statement
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INTECH Open Access Publisher, 2010
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| ISBN
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9537619575
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: 9789537619572
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| Abstract
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At the beginning of the chapter, the quality assessment for fundus images was defined as "the characteristics of an image that allow the retinopathy diagnosis by a human or software expert". The literature was surveyed to find techniques which could help to achieve this goal. General image QA does not seem well suited for our purposes, as they are mainly dedicated to the detection of artefacts due to compression and they often require the original non-degraded image, something that does not make much sense in the context of QA for retinal images. Our survey found five publications which tackled a problem comparable to the one of this project. They were divided into 3 categories: "Histogram Based", "Retina Morphology" and "Bag-of-Words". The authors of the first category approached the problem by computing relatively simple features and comparing them to a model of a good quality image. Although this approach might have advantages like speed and ease of training, it does not generalise well on the natural variability of fundus images as highlighted by Niemeijer et al. (2006) and Fleming et al. (2006). "Retina Morphology" methods started to take into account features unique to the retina, such as vessels, optic nerve or temporal arcades. This type approach considerably increased the QA accuracy. Remarkably, Fleming et al. developed a very precise way to judge the quality of image clarity and field definition which closely resembled what an ophthalmologist would do. The main drawbacks are time required to locate the various structures and the fact that if the image quality is too poor, some of the processing steps might fail, giving unpredictable results. This is unlikely to happen in the problem domain of Fleming et al. because they worked with images taken by trained ophthalmologists, but this is not the case with systems that can be used by personnel with basic training. The only method of the "Bag-of-Words" category is the one developed by Niemeijer et al. Their technique is based on pattern recognition algorithms which gave high accuracy and specificity. The main drawback is again speed of execution. The new approach described in this chapter was partially inspired by all these techniques: colour was used as features as in the "Histogram Based" technique, the vessels were segmented as a preprocessing step like in the "Retina Morphology" techniques and the QA was computed by a classifier similar to the one used in the "Bag-of-Words" techniques. New features were developed and used such as ELVD, VFOV and the use of the HSV colour space, which was not evaluated by any of the previous authors for QA of fundus images. This made possible the creation of a method capable of classifying the quality of an image with a score from 0 to 1 in a period of time much shorter than "Retina Morphology" and "Bag-of-Words" techniques. Features, classifier types and other parameters were selected based on the results of empirical tests. Four different types of datasets were used. Although none are very large (none contained more than 100 images) they were fairly good representative of the variation of fundus images in terms of quality, camera used and patient's ethnicity. In the literature, the method which seemed to perform best and which had the best generalisation was the one of Niemeijer et al. It was implemented and compared to our algorithm. Our results are in favour of the.
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Open Access Collection.
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| LC Classification
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R318.L833 2010
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Edward Chaum
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Fabrice Meriaudeau
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Kenneth Tobin
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Luca Giancardo
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Thomas P Karnowski
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