Edge detection is a key technique in image processing. The detected edge quality has a direct and significant impact on performance. There is a multitude of methods for edge detection but they are strongly associated with the application and the quality of the images. However, more precise outcomes and a reduced execution time remain the primary objectives for extracting edges. To address these issues, we propose a novel technique based on a complex system called Cellular Automata (CA). They are successfully applied in edge detection due to their simplicity and local interactions. This undertook shed new light on a novel method using Outer Totalistic Cellular Automata (OTCA) to perform efficiently edge detection. We have tested images from Berkeley dataset. RMSE and SSIM are used as fitness functions for estimating numerical performance of OTCA rules. Comparisons were made with classical edge detectors like: Canny, Scharr, Sobel, Roberts. Experimental results showed that OTCA rules provide excellent performance and outperforms other edge detectors in terms of precision and execution time, particularly when dealing with noisy images.