Secondary Observer System for Detection of Microaneurysms in Fundus Images Using Texture Descriptors.

The increase of diabetic retinopathy patients and diabetic mellitus worldwide yields lot of challenges to ophthalmologists in the screening of diabetic retinopathy. Different signs of diabetic retinopathy were identified in retinal images taken through fundus photography. Among these stages, the ear...

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Publicado en:Journal of Digital Imaging Vol. 33; no. 1; pp. 159 - 168
Autores principales: Derwin, D. Jeba, Selvi, S. Tami, Singh, O. Jeba
Formato: equations & formulas pictorial tables/charts Journal Article
Publicado: Springer Nature Feb2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Feb2020
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      pub: Springer Nature
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        atl: Secondary Observer System for Detection of Microaneurysms in Fundus Images Using Texture Descriptors.
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          Derwin, D. Jeba
          Selvi, S. Tami
          Singh, O. Jeba
        affil: Department of ECE, Arunachala College of Engineering for Women, Kanyakumari, Tamilnadu, India
      sug:
        subj:
          Microaneurysm Diagnosis
          Diabetic Retinopathy Diagnosis
          Image Processing, Computer Assisted
          Diagnostic Imaging Methods
          Vision Screening Methods
          Deaf-Blind Disorders Prevention and Control
          Image Interpretation, Computer Assisted
          ROC Curve
      ab: The increase of diabetic retinopathy patients and diabetic mellitus worldwide yields lot of challenges to ophthalmologists in the screening of diabetic retinopathy. Different signs of diabetic retinopathy were identified in retinal images taken through fundus photography. Among these stages, the early stage of diabetic retinopathy termed as microaneurysms plays a vital role in diabetic retinopathy patients. To assist the ophthalmologists, and to avoid vision loss among diabetic retinopathy patients, a computer-aided diagnosis is essential that can be used as a second opinion while screening diabetic retinopathy. On this vision, a new methodology is proposed to detect the microaneurysms and non-microaneurysms through the stages of image pre-processing, candidate extraction, feature extraction, and classification. The feature extractor, generalized rotational invariant local binary pattern, contributes in extracting the texture-based features of microaneurysms. As a result, our proposed system achieved a free-response receiver operating characteristic score of 0.421 with Retinopathy Online Challenge database.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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