In the analysis of in vitro biological samples is very common to use Petri dishes, in which the samples are cultured. Likewise, in the study of mouse behavior and learning, the Morris test is employed, which consists of placing the animal in a circular pool to observe its behavior within it. In both cases, usually, the detection of each circle in images and videos is done manually, which makes the process long, tedious and imprecise. Currently, there are several image processing methods that allow the detection of lines, circles and other geometric shapes; the most well-known being the technique based on the Hough transform, which allows the detection of geometric shapes that can be expressed by a mathematical equation, and the Random Sample Consensus (RANSAC), a robust estimation algorithm that allows a mathematical model to be found from data contaminated with numerous values that do not fit the model. The precise location of the circle's position is very important, as it can seriously affect the detection and counting of samples on the Petri dishes and the measurement of mouse paths in the Morris test. Therefore, in this paper we evaluate and present the results obtained with these two techniques in synthetic images, for the detection of Petri dishes in biological images and the circular pool in Morris' test videos, measuring their computational efficiency and the error in the location of the circles.