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Algorithmic Analysis of Vesselness and Blobness for Detecting Retinopathies Based on Fractional Gaussian Filters

Author

Listed:
  • Maria de Jesus Estudillo-Ayala

    (School of Biological Systems and Technological Innovation, Benito Juárez Autonomous University of Oaxaca, Oaxaca 68020, Mexico)

  • Hugo Aguirre-Ramos

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Juan Gabriel Avina-Cervantes

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8km, Comunidad de Palo Blanco, Salamanca 36885, Mexico
    Author thanks the Universidad de Guanajuato by the financial support of the APC.)

  • Jorge Mario Cruz-Duarte

    (Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Col. Tecnológico, Monterrey 64849, Nuevo León, Mexico)

  • Ivan Cruz-Aceves

    (CONACYT Research-Fellow, Center for Research in Mathematics (CIMAT), A.C., Jalisco S/N, Col. Valenciana, Guanajuato 36000, Mexico)

  • Jose Ruiz-Pinales

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

Abstract

All around the world, partial or total blindness has become a direct consequence of diabetes and hypertension. Visual disorders related to these diseases require automatic and specialized methods to detect early malformations, artifacts, or irregular structures for helping specialists in the diagnosis. This study presents an innovative methodology for detecting and evaluating retinopathies, particularly microaneurysm and hemorrhages. The method is based on a multidirectional Fractional-Order Gaussian Filters tuned by the Differential Evolution algorithm. The contrast of the microaneurysms and hemorrhages, regarding the background, is improved substantially. After that, these structures are extracted using the Kittler thresholding method under additional considerations. Then, candidate lesions are detected by removing the blood vessels and fovea pixels in the resulting image. Finally, candidate lesions are classified according to its size, shape, and intensity properties via Support Vector Machines with a radial basis function kernel. The proposed method is evaluated by using the publicly available database MESSIDOR for detecting microaneurysms. The numerical results are summarized by the averaged binary metrics of accuracy, sensitivity, and specificity giving the performance values of 0.9995, 0.7820 and 0.9998, respectively.

Suggested Citation

  • Maria de Jesus Estudillo-Ayala & Hugo Aguirre-Ramos & Juan Gabriel Avina-Cervantes & Jorge Mario Cruz-Duarte & Ivan Cruz-Aceves & Jose Ruiz-Pinales, 2020. "Algorithmic Analysis of Vesselness and Blobness for Detecting Retinopathies Based on Fractional Gaussian Filters," Mathematics, MDPI, vol. 8(5), pages 1-19, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:744-:d:355259
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    References listed on IDEAS

    as
    1. Aguirre-Ramos, Hugo & Avina-Cervantes, Juan Gabriel & Cruz-Aceves, Ivan & Ruiz-Pinales, José & Ledesma, Sergio, 2018. "Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 568-587.
    2. Edmundo Capelas de Oliveira & José António Tenreiro Machado, 2014. "A Review of Definitions for Fractional Derivatives and Integral," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-6, June.
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    Cited by:

    1. Ricardo López-Ruiz, 2022. "Mathematical Biology: Modeling, Analysis, and Simulations," Mathematics, MDPI, vol. 10(20), pages 1-2, October.

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