Gökay AKINCI, Ediz POLAT, Orhan Murat KOÇAK

A video-based eye pupil detection system for diagnosing bipolar disorder

Eye pupil detection systems have become increasingly popular in image processing and computer vision applications in medical systems. In this study, a video-based eye pupil detection system is developed for diagnosing bipolar disorder. Bipolar disorder is a condition in which people experience changes in cognitive processes and abilities, including reduced attentional and executive capabilities and impaired memory. In order to detect these abnormal behaviors, a number of neuropsychological tests are also designed to measure attentional and executive abilities. The system acquires the position and radius information of eye pupils in video sequences using an active contour snake model with an ellipse-fitting algorithm. The system also determines the time duration of the eye pupils looking at certain regions and the duration of making decisions during the neuropsychological tests. The tests are applied to 2 different groups consisting of people with bipolar disorder (bipolar group) and people without bipolar disorder (control group) in order to mathematically model the people with bipolar disorder. The mathematical modeling is performed by using the support vector machines method. It is a supervised learning method that analyzes data and recognizes patterns for classification. The developed system acquires data from the being tested and it classifies the person as bipolar or nonbipolar based on the learned mathematical model.

Anahtar Kelimeler:

Bipolar disorder, Eye pupil detection and tracking, support vector machines

A video-based eye pupil detection system for diagnosing bipolar disorder

Keywords:

Bipolar disorder, Eye pupil detection and tracking, support vector machines,

PDF

___

J.R. Leigh, D.S. Zee, The Neurology of Eye Movements, Oxford, Oxford University Press, 1983.
D.A. Leopold, N.K. Logothetis, “Activity changes in early visual cortex reflect monkeys’ percepts during binocular rivalry”, Nature, Vol. 379, pp. 549–553, 1996.
I. Kagan, M. Gur, D. M. Snodderly, “Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings”, Journal of Neurophysiology, Vol. 88, pp. 2557–2574, 2002.
K. Rayner, “Eye movements in reading and information processing: 20 years of research”, Psychological Bulletin, Vol. 124, pp. 372–422, 1998.
K. Rayner, T.J. Smith, G.L Malcolm, J.M. Henderson, “Eye movements and visual encoding during scene perception”, Psychological Science, Vol. 20, pp. 6–10, 2008.
J.M. Henderson, A. Hollingworth, “Eye movements, visual memory, and scene representation”, in M.A. Peterson and G. Rhodes (Eds.), Analytic and Holistic Processes in the Perception of Faces, Objects, and Scenes”, New York, Oxford University Press, pp. 356–383, 2003.
J. Treuting, “Eye tracking and the cinema: a study of film theory and visual perception”, SMPTE Motion Imaging Journal, Vol. 115, pp. 31–40, 2006.
A.T. Duchowski, Eye Tracking Methodology: Theory and Practice, Berlin, Springer, 2007.
R. Bates, H. Istance, L. Oosthuizen, P. Majaranta, “Survey of de-facto standards in eye tracking”, COGAIN Conference on Communication by Gaze Interaction, 2005.
J.G. Daugman, “How iris recognition works”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, pp. 21–30, 2004.
R Wildes, “Iris recognition: an emerging biometric technology”, Proceedings of the IEEE, Vol. 85, pp. 1348–1363, 19
M. Kass, A. Witkin, D. Terzopoulos, “Snakes: active contours models”, International Journal of Computer Vision, Vol 1988, pp. 321–331, 1987.
V.N. Vapnik, The Nature of Statistical Learning Theory, Berlin, Springer 1995.
G. Akıncı, E. Polat, O.M. Ko¸ cak, “Bipolar bozukluk te¸shisi i¸ cin video tabanlı g¨ oz izleme sistemi”, 20. IEEE Sinyal ˙I¸sleme ve ˙Ileti¸sim Uygulamaları Kurultayı, 2012 (in Turkish).
J. Daugman, “New methods in iris recognition”, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, Vol. 37, pp. 1167–1175, 2007.
G. Bradski, “The OpenCV Library”, Dr. Dobb’s Journal of Software Tools, Vol. 25, pp. 122–125, 2000. G. Akıncı, E. Polat, O.M. Ko¸ cak, “Neuropsychiatric disorders classification using a video based pupil detection system”, International Symposium on Innovations in Intelligent Systems and Applications, 2012.