Open-source tool for eye image annotation
Keywords:
tool, data labeling, eye trackingAbstract
Introduction: the tracking of the pupil center using videoculography images is widely used for the diagnosis of diseases of the nervous system. The difference between the automatically detected value of the pupil center and the reference value marked by a specialist (annotation) determines the accuracy of the diagnosis. The manual annotation process is very laborious, tedious, and prone to human error. Annotations are essential to develop and evaluate algorithms in the area of artificial vision, mainly those based on supervised learning, however, there are few interactive tools to perform reliable annotation of the center of the pupil.
Objective: to develop an open source tool to annotate the center of the pupil.
Method: The functional and non-functional requirements of the tool are defined and two algorithms are implemented for the semi-automatic annotation of the center of the pupil based on the ellipse and circle adjustment methods, from several points marked by the specialist.
Results: The software application, called PUPILA, was developed in Python, from March 2020 to September 2020, and provides various auxiliary functions that facilitate the annotator's task.
Conclusions: The new tool provides an agreeable and interactive environment to record the center of the pupil, guaranteeing comfort, accuracy and reduction of subjectivities in the specialist's work. It is open source and cross-platform, allowing it to be compatible with various devices and free to use. It has made it possible to annotate images from public databases and others acquired experimentally.
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References
2. Min-Allah N, Jan F, Alrashed S. Pupil detection schemes in human eye: a review. Multimed Syst [Internet]. 2021 [citado 3 Ene 2022]; 27:753-777. DOI: https://doi.org/10.1007/s00530-021-00806-5
3. Park J, Lee YW. A Comparative Study on the Center-based Iterative Hough Transform. J Stud Res [Internet]. 2020 [citado 7 Ene 2022]; 9(2):1-10, DOI: https://doi.org/10.47611/jsrhs.v9i2.1206
4. Manchalwar MD, Warhade KK. Histogram of Oriented Gradient based Automatic Detection of Eye Diseases. En: International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017. IEEE; 2017. [citado 7 Ene 2022]. Disponible en: https://ieeexplore.ieee.org/document/8463671
5. Vázquez Romaguera L, Perdigón Romero F, Vázquez Seisdedos CR, Fernandes Costa Filho CF, Fernándes Costa MG. Detecção automática da pupila usando ajuste de elipse, 2016. En: XXV Congresso Brasileiro de Engenharia Biomédica, 2016. Brazil: Anais do CBEB; 2016. [citado 7 Ene 2022]. Disponible en: https://www.researchgate.net/publication/312041891_AUTOMATIC_PUPIL_DETECTION_USING_ELLIPSE_FITTING
6. Araujo GM, Ribeiro, FML, Júnior WSS, da Silva EAB, Goldenstein SK. Weak Classifier for Density Estimation in Eye Localization and Tracking. IEEE Trans Image Proc [Internet]. 2017 [citado 12 Ene 2022]; 26(7):3410-3424. Disponible en: https://ieeexplore.ieee.org/document/7898792
7. Riyazuddin YMD, Mahaboob Basha S, Krishna Reddy K, Naseera Banu S. Effective Usage of Support Vector Machine in Face Detection. Int J Eng Res Adv Technol [Internet]. 2020 [citado 12 Ene 2022]; 9(3):1336-1340, Disponible en: https://www.ijeat.org/wp-content/uploads/papers/v9i3/C5406029320.pdf
8. Han SY, Kwon HJ, Kim Y, Cho NI. Noise-Robust Pupil Center Detection Through CNN-Based Segmentation with Shape-Prior Loss. IEEE Access [Internet]. 2020 [citado 12 Ene 2022]; 8:64739-64749. Disponible en: https://ieeexplore.ieee.org/abstract/document/9055424
9. Vázquez Romaguera T, Hubert Sánchez F, Vázquez Seisdedos CR. Herramienta para anotación de imágenes oculares. En: XVIII Convención y Feria Informática Internacional, 2020, V Conferencia Internacional en Ciencias Computacionales e Informática CICCI, Marzo 16-20, 2020. La Habana; 2020. [citado 12 Ene 2022]; Disponible en: http://www.informaticahabana.cu/es/node/5588
10. Roli F, Vitulano S. Image Analysis and Processing. En: 13th International Conference, Cagliari, Italy, Sep 6-8, 2005. Italia: Springer; 2005. Disponible en: https://link.springer.com/book/10.1007/11553595
11. Center of Biometrics and Security Research. CASIA Iris Image Database [Internet]. Beijing, China: CASIA; 2005 Disponible en: http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp
12. Kothari RS, Chaudhary AK, Bailey RJ, Pelz JB, Diaz GJ. Ellseg: An ellipse segmentation framework for robust gaze tracking. IEEE Trans Visual Comp Grap [Internet]. 2021 [citado 13 Ene 2022]; 27(5):2757-2767. Disponible en: https://ieeexplore.ieee.org/abstract/document/9389650
13. Rubio J de. Stability analysis of the modified Levenberg–Marquardt algorithm for the artificial neural network training. IEEE Trans Neural Netw Learn Syst [Internet]. 2020 [citado 13 Ene 2022], 32(8):3510-3524. Disponible en: https://ieeexplore.ieee.org/abstract/document/9170566
