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A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images

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Abstract

Day by day, huge amount of information is collected in medical databases. These databases include quite interesting information that could be exploited in diagnosis of illnesses and medical treatment of patients. Classification of these data is getting harder as the databases are expanded. On the other hand, automated image analysis and processing is one of the most promising areas of computer vision used in medical diagnosis and treatment. In this context, retinal fundus images, offering very high resolutions that are sufficient for most of the clinical cases, provide many indications that could be exploited in diagnosing and screening retinal degenerations or diseases. Consequently, there is a strong demand in developing automated evaluation systems to utilize the information stored in the medical databases. This study proposes an automatic method for segmentation of ARMD in retinal fundus images. The method used in the automated system extracts lesions of the ARMD by employing a statistical method. In order to do this, the statistical segmentation method is first used to extract the healthy area of the macula that is more familiar and regular than the unhealthy parts. Here, characteristic images of the patterns of the macula are extracted and used to segment the healthy textures of an eye. In addition to this, blood vessels are also extracted and then classified as healthy regions. Finally, the inverse image of the segmented image is generated which determines the unhealthy regions of the macula. The performance of the method is examined on various quality retinal fundus images. Segmented images are also compared with consecutive images of the same patient to follow up the changes in the disease.

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Acknowledgements

We are thankful to the staff of the Department of Ophthalmology at Faculty of Medicine at Karadeniz Technical University and especially Prof. Dr. Hidayet Erdöl for providing retinal fundus images and for his contributions to the study on especially medical issues.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Cemal Köse, Uğur Şevik & Cevat İkibaş

  2. Department of Data Processing Center, Faculty of Medicine, Karadeniz Technical University, 61080, Trabzon, Turkey

    Okyay Gençalioğlu

  3. Department of Electrical and Electronic Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Temel Kayıkıçıoğlu

Authors
  1. Cemal Köse
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  2. Uğur Şevik
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  4. Cevat İkibaş
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Correspondence to Cemal Köse.

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Köse, C., Şevik, U., Gençalioğlu, O. et al. A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images. J Med Syst 34, 1–13 (2010). https://doi.org/10.1007/s10916-008-9210-4

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  • DOI: https://doi.org/10.1007/s10916-008-9210-4

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