Segmentation of Macular Lesions Using Active Shape Contour Method

  • Jan Kubicek
  • Iveta Bryjova
  • Marek PenhakerEmail author
  • Jana Javurkova
  • Lukas Kolarcik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 423)


Age-related macular degeneration (ARMD) is one of the most widespread diseases of the eye fundus and is the most common cause of vision loss for those over the age of 60. There are several ways to diagnose ARMD. One of them is the Fundus Autofluorescence (FAF) method, and is one of the modalities of Heidelberg Engineering diagnostic devices. The BluePeak™ modality utilizes the fluorescence of lipofuscin (a pigment in the affected cells) to display the extent of the disease’s progression. The native image is further evaluated to more precisely specify the diagnosis of the disease—it is necessary to determine the size of the macular lesion area. Calculations of the geometric parameters of macular lesions were conducted in the MATLAB® software; the size of the lesion area was determined using the Image Processing Toolbox. The automated lesion detection method occurs using a parametric active contour (active contours driven by local Gaussian distribution fitting energy) that encloses the affected macular lesion, thereby allowing a precise determination of the affected area. This method is relatively quick for use in clinical practice and allows evaluation the macular lesions exactly based on the proportion with the feature extraction in advance. The proposed methodology is fully automatic. In the algorithm input we define area of interest and initial circle, which is placed inside of the object. Image background is suppressed by low pass filter. Final contour is formed in consecutive steps, up to shape of macular lesion.


Age-related macular degeneration Optical coherence tomography Blue peak™ Image processing Active contour Medical image segmentation 



The work and the contributions were supported by the project SP2015/179 ‘Biomedicínské inženýrské systémy XI’ and This work is partially supported by the Science and Research Fund 2014 of the Moravia-Silesian Region, Czech Republic and this paper has been elaborated in the framework of the project “Support research and development in the Moravian-Silesian Region 2014 DT 1—Research Teams” (RRC/07/2014). Financed from the budget of the Moravian-Silesian Region.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jan Kubicek
    • 1
  • Iveta Bryjova
    • 1
  • Marek Penhaker
    • 1
    Email author
  • Jana Javurkova
    • 1
  • Lukas Kolarcik
    • 2
  1. 1.The Department of Cybernetics and Biomedical EngineeringFEI VSB-TU OstravaOstrava-PorubaCzech Republic
  2. 2.Clinic of OphthalmologyUniversity Hospital OstravaOstrava-PorubaCzech Republic

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