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Quality evaluation of digital fundus images through combined measures

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Detalhes bibliográficos
Resumo:The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.
Autores principais:Veiga, Diana
Outros Autores:Pereira, Carla; Ferreira, Manuel João Oliveira; Gonçalves, Luís; Monteiro, João L.
Assunto:Digital fundus photography Image quality Focus measures Image processing Fuzzy classifier
Ano:2014
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
Descrição
Resumo:The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.