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

Abstract

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.