Applying Digital Image Processing in Psychological Assessment: Automating the Interpretation of the Tree-Drawing Test in Psychotherapy

This study investigates the application of digital image processing techniques in the automation of Tree Drawing Test (TDT) interpretation, a tool frequently used in psychological assessment and psychotherapy. The research was conducted within the Multidisciplinary Informatics Research Center at UTT, using an extensive dataset of images from which morphological indicators such as drawing size (M), crown width (PM), and trunk direction were automatically extracted through segmentation and morphological analysis methods implemented in a complete OpenCV-based processing pipeline. The results show high accuracy in recognizing trunk direction (96.04%), as well as a significant correlation between several graphical indicators. The study highlights the potential of automated methods to support psychological evaluation by increasing standardization, reducing inter-evaluator variability, and enabling the future integration of machine learning techniques for the classification of psychological traits. The conclusions support the integration of these approaches into psychotherapeutic practice, with implications for monitoring client progress and developing digital decision-support tools.

digital image processing, Tree Drawing Test, psychological assessment, psychotherapy, automation, machine learning, automatic classification

1. A.J. Kaat, S. Bishop, E. Condy, N.R. Sullivan, L. Soory, A. Thurm. (2021). Prerequisite skills in cognitive testing: Innovations in theory and recommendations for practice. Cognitive Development, Volume 58, ISSN 0885-2014. https://doi.org/10.1016/j.cogdev.2021.101038 [Google Scholar] [Crossref]

2. Ciorbea, I. (2015). Evaluarea psihologică aplicată. Repere teoretice și practice. Bucuresti: Edit. Trei. ISBN 978-606-719-355-8 [Google Scholar] [Crossref]

3. Crișan, C. & Suciu, B. & Siserman, C. (2020). Violent behaviour and psychiatric disorders. International Conference on Legal Medicine from Cluj. Vol. 2(3).DOI:10.26352/ex01bis_legal-medicine_vol.2-2020 [Google Scholar] [Crossref]

4. D. B. Olawade, O. Z. Wada, A. Odetayo, A. C. David-Olawade, F. Asaolu, J. Eberhardt. (2024). Enhancing mental health with Artificial Intelligence: Current trends and future prospects, Journal of Medicine, Surgery, and Public Health, Vol. 3. https://doi.org/10.1016/j.glmedi.2024.100099 [Google Scholar] [Crossref]

5. Dolokov, A., Andresen, N., Hohlbaum, K., Thöne-Reineke, C., Lewejohann, L. & Hellwich, O. (2023). Upper Bound Tracker: A Multi-Animal Tracking Solution for Closed Laboratory Settings. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Vol. 5, pp. 945-952). ISSN 2184-4321, SciTePress. https://doi.org/10.5220/0011609500003417 [Google Scholar] [Crossref]

6. Gu S., Liu Y., Liang F., Feng R., Li Y., Liu G., Gao M., Liu W., Wang F. & Huang J. H. (2020). [Google Scholar] [Crossref]

7. Screening Depressive Disorders With TreeDrawing Test, Frontiers in Psychology Journal, Vol. 11. ISSN=1664-1078. https://doi.org/10.3389/fpsyg.2020.01446 [Google Scholar] [Crossref]

8. Guo, H., Feng, B., Liu, T., Zhao, R., Fan, H., Dong, Z., Gong, Q., & Chen, T. Tree imagery in drawing tests for screening mental disorders: A systematic review and meta-analysis (Preprint). [Google Scholar] [Crossref]

9. https://doi.org/10.21203/rs.3.rs-4584440/v1 [Google Scholar] [Crossref]

10. J. A. Thompson, S. L. Wilson. (1982). Automated Psychological Testing, International Journal of Man-Machine Studies, Vol. 17(3). Pp. 279-289. https://doi.org/10.1016/S0020-7373(82)80028X [Google Scholar] [Crossref]

11. Liu, G., Liu, W., Wen, J., Liu, Y., Jin, H., Li, Z., & Zhang, Y. Evaluating the Tree Drawing Test Depression Assessment Scale for adolescent depression screening. Scientific Reports Journal, 15 (1), 20984. (2025). https://doi.org/10.1038/s41598-025-99254-8 [Google Scholar] [Crossref]

12. Liu Y., Zheng J., Zeng Y., Luo F. & Tian X. (2025). Graph learning based suicidal ideation detection via tree-drawing test. Frontiers in Psychiatry Journal. Vol. 16. ISSN=1664-0640 https://doi.org/10.3389/fpsyt.2025.1617650 [Google Scholar] [Crossref]

13. Maliki, I., and M. Ibnu Tri Yuono. Personality classification system based on tree drawing test with machine learning. In AIP Conference Proceedings (Vol. 2510(1), Article 030035). AIP Publishing LLC. https://doi.org/10.1063/5.0129097 [Google Scholar] [Crossref]

14. Trautner M. (2024). The Impact of Technology on Psychological Testing: Online Assessments and AI Integration. Abnormal and Behavioural Psychology Perspective. Vol. 10(4). [Google Scholar] [Crossref]

15. Pintea F.A. & Ciorba V.L. (2020). Automatic image processing of tree drawings for psychological tests. International Conference on Legal Medicine Cluj. Vol. 2, pp. 166–71) [14] [Google Scholar] [Crossref]

16. Robens S., Heymann P., Gienger R., et all. (2019). The Digital Tree Drawing Test for Screening of Early Dementia: An Explorative Study Comparing Healthy Controls, Patients with Mild Cognitive Impairment, and Patients with Early Dementia of the Alzheimer Type. Journal of Alzheimer’s Disease. 68(4), pp. 1561-1574. https://doi.org/10.3233/JAD-181029 [Google Scholar] [Crossref]

17. Santillo, G., et all. (2025). Projective in Time: A Systematic Review on the Use of Projective Tests in Developmental Age. Children Journal, 12(4), 406. https://doi.org/10.3390/children12040406 [Google Scholar] [Crossref]

18. Takemura, K., Takasaki, I. & Iwamitsu, Y. (2005). Statistical image analysis of psychological projective drawings. Journal of Advanced Computational Intelligence and Intelligent Informatics. Vol. 9(5), pp.453-459. https://doi.org/10.20965/jaciii.2005.p0453 [Google Scholar] [Crossref]

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