Image segmentation is a challenging task due to image variations, such as illumination, background, noise, and others. There are several segmentation methods, but the requirement of prior knowledge and parameter setting makes it hard to perform a good segmentation, especially in medical images where an expert is needed to make the segmentation accordingly with the prior knowledge to determine the area of interest. In this work, we are focused on the feature construction to make an image segmentation of computerized tomography scans of lungs affected by COVID-19. Genetic Programming (GP) is used to evolve a program to extract and construct features from the image to make a segmentation where the target is to find the affected area. The flexibility that offers GP allows us to face the segmentation task and know which functions are used in the final program, leading to an interpretable solution. The results of the experiments demonstrate that GP is capable of extracting and constructing features from the Computerized Tomography images to perform the segmentation of lungs affected by COVID-19, achieving values of 0.59 of \(F_{1}-score\) metric to measure the segmentation performance. Furthermore, the experimental results determine the appropriate parameters for the evolutionary process.