Tesis doctoralsDepartament d'Enginyeria Informàtica i Matemàtiques

Image Segmentation Methods for Automatic Detection of the Anatomical Structure of the Eye in People with Diabetic Retinopathy

  • Datos identificativos

    Identificador:  TDX:3244
    Autores:  Escorcia Gutierrez, José
    Resumen:
    This thesis is framed within the comprehensive plan for early prevention of Diabetic Retinopathy (DR) launched by the Spain government following the World Health Organization to promote initiatives that raise awareness of the importance of regular eye exams among people with diabetes. To determine the level of diabetic retinopathy, we need to find and identify different types of lesions in the eye fundus. First, the normal anatomic structures of the eye (blood vessels, optic disc and fovea) must be removed from the image, in order to make visible the abnormalities. This thesis has focused on this step of image cleaning. This thesis proposes a novel framework for fast and fully automatic optic disc segmentation based on Markowitz's Modern Portfolio Theory to generate an innovative color fusion model capable of admitting any segmentation methodology in the medical imaging field. This approach acts as a powerful and real-time pre-processing stage that could be integrated into daily clinical practice to accelerate the diagnosis of DR due to its simplicity, performance, and speed. This thesis's second contribution is a method to simultaneously make a blood vessel segmentation and foveal avascular zone detection, considerably reducing the required image processing time. In addition, the first component of the xyY color space representing the chrominance values is the most supported according to the approach developed in this thesis for blood vessel segmentation and fovea detection. Finally, several samples are collected for a color interpolation procedure based on statistic color information and are used by the well-known Convexity Shape Prior segmentation algorithm. The thesis also proposes another blood vessel segmentation method that relies on an effective feature selection based on decision tree learning. This method is validated using three different classification techniques (i.e., Decision Tree, Artificial Neural Network, and Support Vector Machine).
  • Otros:

    Editor: Universitat Rovira i Virgili
    Fecha: 2021-04-21, 2021-10-18T02:00:18Z, 2021-05-05T11:38:15Z
    Identificador: http://hdl.handle.net/10803/671543
    Departamento/Instituto: Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili.
    Idioma: eng
    Autor: Escorcia Gutierrez, José
    Director: Valls Mateu, Aïda, Romero Aroca, Pedro, Puig Valls, Domènec
    Fuente: TDX (Tesis Doctorals en Xarxa)
    Formato: application/pdf, application/pdf, 159 p.
  • Palabras clave:

    Fundus image segmentation
    Medical image analysis
    Diabetic retinopathy
    Segmentación de la imagen del fondo de ojo
    Análisis de imágenes médicas
    Retinopatía diabética
    Segmentació de la imatge del fons
    Anàlisi d'imatges mèdiques
    Retinopatia diabètica
    621.3
    Ingeniería y arquitectura
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