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Optical nerve segmentation using The Active shape method


The paper deals with the segmentation procedure for optical nerve localization and the consequent determination of geometrical parameters such as optical nerve area, radius and diameter. An extraction of these geometrical parameters is especially important for clinical practice particularly in the case where retinal lesions are present. On the base of the optical nerve extraction, we are capable of comparing it with area of retinal lesions. Via this approach it is possible to track time evaluation of retinal lesions. The proposed algorithm for segmentation of optical nerve area is performed within two main steps. In the first step, the active contour method is used specially for the localization of the optical nerve. This part of the algorithm generates mathematical model of the optical nerve in binary form. Consequently, on the base of this mathematical model of the optical nerve respective geometrical parameters are worked out for future comparison with retinal lesions. Image preprocessing is an integral part of the segmentation procedure, improving the observability of the optical nerve to ensure as relevant detection of the optical nerve as possible.

Keywords:
Optical nerve, retinopathy, retinal lesions, active contour, image segmentation, extraction of geometrical parameters


Autoři: Jan Kubicek 1;  Juraj Timkovic 2,3,4,5;  Jakub Slonka 1;  Marek Penhaker 1;  Martin Augustynek 1;  Iveta Bryjová 1
Působiště autorů: VSB–Technical University of Ostrava, FEECS, K 0, Ostrava, Czech Republic 1;  Clinic of Ophthalmology, University Hospital Ostrava, Czech Republic 2;  Faculty of Medicine, Masaryk University, Brno, Czech Republic 3;  Faculty of Medicine, University of Ostrava, Czech Republic 4;  University Hospital Ostrava, Czech Republic 5
Vyšlo v časopise: Lékař a technika - Clinician and Technology No. 1, 2016, 46, 13-20
Kategorie: Původní práce

Souhrn

The paper deals with the segmentation procedure for optical nerve localization and the consequent determination of geometrical parameters such as optical nerve area, radius and diameter. An extraction of these geometrical parameters is especially important for clinical practice particularly in the case where retinal lesions are present. On the base of the optical nerve extraction, we are capable of comparing it with area of retinal lesions. Via this approach it is possible to track time evaluation of retinal lesions. The proposed algorithm for segmentation of optical nerve area is performed within two main steps. In the first step, the active contour method is used specially for the localization of the optical nerve. This part of the algorithm generates mathematical model of the optical nerve in binary form. Consequently, on the base of this mathematical model of the optical nerve respective geometrical parameters are worked out for future comparison with retinal lesions. Image preprocessing is an integral part of the segmentation procedure, improving the observability of the optical nerve to ensure as relevant detection of the optical nerve as possible.

Keywords:
Optical nerve, retinopathy, retinal lesions, active contour, image segmentation, extraction of geometrical parameters


Zdroje

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[4] Angmo, D., Nayak, B., Gupta, V. Post-strabismus surgery aqueous misdirection syndrome (2015) BMJ Case Reports, 2015.

[5] Kubicek, J., Timkovic, J., Augustynek, M., Penhaker, M., Pokrývková, M. Optical nerve disc segmentation using circual integro differencial operator (2016) Lecture Notes in Electrical Engineering, 362, pp. 387-396.

[6] Osareh A., Mirmehd M., Thomas B., Markham R.: Comparison of colour spaces for optic disc localisation in retinal images. In: Proceedings 16th International Conference on Pattern Recognition. Quebec City, Quebec, Canada, 2002, pp 743–746.

[7] Osareh A., Mirmehd M., Thomas B., Markham R.: Comparison of colour spaces for optic disc localisation in retinal images. In: Proceedings 16th International Conference on Pattern Recognition. Quebec City, Quebec, Canada, 2002, pp 743–746.

[8] Hoover A., Goldbaum M. Locating the optic nerve disc in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imag. 2003;22(8):951–958. doi: 10.1109/TMI.2003.815900.

[9] Peterek, T., Augustynek, M., Zurek, P., & Penhaker, M. (2009). Global Courseware for Visualization and Processing Biosignals. In O. Dossel & W. C. Schlegel (Eds.), World Congress on Medical Physics and Biomedical Engineering, Vol 25, Pt 12 (Vol. 25, pp. 404-407). New York: Springer.

[10] Vasickova, Z., Penhaker, M., & Augustynek, M. (2010). Using Frequency Analysis of Vibration for Detection of Epileptic Seizure. In O. Dossel & W. C. Schlegel (Eds.), World Congress on Medical Physics and Biomedical Engineering, Vol 25, Pt 4: Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics (Vol. 25, pp. 2155-2157). New York: Springer.

[11] Kubicek, J., Penhaker, M., Bryjova, I., Kodaj, M., Articular cartilage defect detection based on image segmentation with colour mapping (2014), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8733, pp. 214-222.

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[13] Penhaker, M., Stula, T., Cerny, M., Society, I. C.: Automatic Ranking of Eye Movement in Electro-oculographic Records. 2010 Second International Conference on Computer Engineering and Applications: Iccea 2010, Proceedings, Vol 2, 2010, s. 456-460.

[14] Penhaker, M., Kasik, V., Snasel, V.: 2013. Biomedical distributed signal processing and analysis: 12th IFIP TC8 International Conference on Computer Information Systems and Industrial Management, CISIM 2013. Krakow: 2013. s. 88-95.

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