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.
The study of drugs to combat neurodegenerative diseases, such as Alzheimer and Parkinson, is frequently done in animal models such as rats. To evaluate the effectiveness of drugs and administered medication, videos of rats in a swimming pool are recorded and their behavior is analyzed. Although, there are several commercial and free access computer programs that allow recording the movement of the rat, they do not do it in an automatic way, given that the identification of some reference points such as the position and ratio of the pool is done by hand. In addition, it is required to identify the frame when the rat is released. This makes the study of these videos long, tedious and not reproducible. Therefore, in this paper, a new technique for the evaluation of the Morris test is introduced. It automatically detects and localises the pool and the rat notably reducing the time consumed in the evaluation. For the pool identification a segmentation method, based on the projection of the video frames, is done, eliminating the rat, while conserving the shape of the pool. Then, the Hough transformation is used to recognize the position and radius of the pool. The frame when the rat is released is found by using mathematical morphology techniques. The software was developed as a plugin of the free access software imageJ. The results obtained were validated, allowing to verify the quality of the proposed method.
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