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Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels

  • Patient Facing Systems
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Abstract

Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.

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Acknowledgment

This research is funded by National ICT R&D fund, Pakistan. We are also thankful to Armed Forces Institute of Ophthalmology (AFIO) for their clinical support and help.

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Authors and Affiliations

  1. Department of Computer Engineering College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi, Pakistan

    Amna Waheed, M. Usman Akram, Zahra Waheed, Muazzam A Khan & Arslan Shaukat

  2. Computer & Software Engineering Department, Bahria University, Islamabad, Pakistan

    Shehzad Khalid

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  1. Amna Waheed
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  2. M. Usman Akram
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  3. Shehzad Khalid
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  4. Zahra Waheed
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  5. Muazzam A Khan
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Correspondence to M. Usman Akram.

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The authors declare that there is no conflict of interests regarding the publication of this manuscript.

This article is part of the Topical Collection on Patient Facing Systems

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Waheed, A., Akram, M.U., Khalid, S. et al. Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels. J Med Syst 39, 128 (2015). https://doi.org/10.1007/s10916-015-0316-1

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  • DOI: https://doi.org/10.1007/s10916-015-0316-1

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