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D etecting the excessive activation of the ciliaris muscle on thermal images

This research aims to develop a system that can detect excessive activity of the ciliaris muscle by analyzing thermal images. The goal is to automatically diagnose and alert if excessive activation is suspected.

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D etecting the excessive activation of the ciliaris muscle on thermal images

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  1. Detecting the excessive activation of the ciliaris muscle on thermal images Balázs Harangi Faculty of Informatics, University of Debrecen SSIP 2009, 10 July 2009, Debrecen, Hungary

  2. Overview Aim of research: • Our primary aim in this field is to set up a system which is able to alert, if the activity of the ciliaris muscle is suspected to be excessive. • The main line of the research is to detect the extra quantity of heat caused by the excessive activity of the ciliaris muscle on thermal images. • Final aim is to realize a system that is able to automatically diagnose.

  3. Background of the research The ciliaris muscle • A ring shaped muscle surrounds the crystalline lens in the eye. • The muscle contracts when someone looks at a near object and relaxes when someone looks far.

  4. Background of the research Problem of the ciliaris muscle • The muscle does not relax in all cases, and thus, the crystalline lens does not flatten perfectly. • The traditional ophthalmologic examination by a refractometer may provide a false dioptre value in this case. • Fault of measurement cases vision improvement, head-ache, reading and other sight disorder.

  5. Background of the research Aim and possible exploitation of the research Since the ciliaris muscle is close to the exterior surface of the eye, we have the opportunity to take advantage of thermal monitoring of it.

  6. Steps of Research Images captured by Somatoinfra 384 x 288 pixels,8-bit intensity, 256-color images

  7. Steps of Research The usage of grayscale images Usually color palettes are applied for displaying in order to let smaller differences to be easily detectable for a human observer, but for simplicity, we change the color representation to grayscale.

  8. Steps of Research Image normalization The temperature of the skin depends on the external weather or the internal temperature of the examine room.For these reasons, we inserted a normalization step into our system to eliminate these differences.

  9. Steps of Research Localization of the eye We modeled the eyes with ellipses which are subdivided into subregions.Thus, we can focus to the interesting regions only.

  10. Steps of Research First-order statistical descriptors: • Mean of intensity histogram: • Variance of intensity histogram: • Skewness of intensity histogram: • Kurtosis of intensity histogram : • Energy of intensity histogram : • Entropy of intensity histogram : WALK RUN

  11. Steps of Research Training and classification We gain 144 dimensional feature vectors per eye if all thesubregions are involved. After then we considered the kNN classifier (with k=10) to decide whether a test image was labeled as healthy or diseased.

  12. Program development Program language: • The sourcecode is written in Matlab, we used the following tools: • Image Processing Toolbox: • imread(); imshow() • imellipse(); vertex(); poly2mask() • graycomatrix(); graycoprops() • Statistics Toolbox: • skewness(); kurtosis() • and Bioinformatics Toolbox: • knnclassify()

  13. Program development Functions: • Normalization of heat scale • Definition of region of eyes • Extraction of features • Classification

  14. Conclusion and Discussion Database Our initial training database contains 20 healthy and 20 diseased images manually labeled by a clinical expert. (diseased) (healty)

  15. Conclusion and Discussion Result: Our simple algorithm is tested on small databased. Result of this test:

  16. Conclusion and Discussion Fault results: True positive False positive

  17. Conclusion and Discussion Fault results: True negative False negative

  18. Conclusion and Discussion • Summary • Basic functions of the finally system are ready and acting. • Results of the decision are good enough, but we have to refine. • Plans • A current database should be extended • More specify results • Automatic location of the eye region • Finding the critical point of eye on normal picture • (corner, pupil, ciliaris muscle) • Find an appropriate physical model to get rid of thermal distortion of orbit.

  19. Decision of ophthalmologist One way to more exact results: The practical diagnostics is based on comparing the thermal value of the ciliaris muscle with the centre of the cornea.

  20. Thank you! Thank you for your attention!

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