Retinal Spectral Image Analysis Methods Using Spectral Reflectance Pattern Recognition

  • G. M. Atiqur Rahaman
  • Jussi Parkkinen
  • Markku Hauta-Kasari
  • Ole Norberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7786)


Conventional 3-channel color images have limited information and quality dependency on parametric conditions. Hence, spectral imaging and reproduction is desired in many color applications to record and reproduce the reflectance of objects. Likewise RGB images lack sufficient information to successfully analyze diabetic retinopathy. In this case, spectral imaging may be the alternative solution. In this article, we propose a new supervised technique to detect and classify the abnormal lesions in retinal spectral reflectance images affected by diabetes. The technique employs both stochastic and deterministic spectral similarity measures to match the desired reflectance pattern. At first, it classifies a pixel as normal or abnormal depending on the probabilistic behavior of training spectra. The final decision is made evaluating the geometric similarity. We assessed several multispectral object detection methods developed for other applications. They could not proof to be the solution. The results were interpreted using receiver operating characteristics (ROC) curves analysis.


Spectral reflectance image Diabetic retinopathy Spectral information divergence ROC curves Object detection Objects classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • G. M. Atiqur Rahaman
    • 1
  • Jussi Parkkinen
    • 2
  • Markku Hauta-Kasari
    • 3
  • Ole Norberg
    • 1
  1. 1.DPC, Department of Applied Science and DesignMid Sweden UniversitySweden
  2. 2.School of EngineeringMonash UniversityMalaysia
  3. 3.SIB Labs, School of ComputingUniversity of Eastern FinlandFinland

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