Colonoscopy and wireless capsule endoscopy are the most common techniques to monitor and detect abnormalities in the colon. During this process, probe or capsule movement causes blurry images. Detection and removal of blurry images is critical for further automatic abnormality detection procedures. Several methods based on wavelet transform, Canny edge detection, discrete Fourier and cosine transform have been proposed so far. The Laplacian operator-based approaches have not been used on colon images yet. In this study, we extracted four features from colon images based on Laplacian operator for the discrimination of blurry images from visually normal images. The features were the energy and variance of the Laplacian of the images, the average of pixels obtained using diagonal and modified Laplacian operator. We used 80 frames (40 of them were blurry) selected from videos that are available in an open-source database (https://www.gastrointestinalatlas.com/). The features were utilized as the inputs to various classification methods, where cubic support vector machines resulted in the best performance. The classification accuracy values we obtained were 82.5%. The results of this study indicated that for the automatic detection of blurry images in colon videos Laplacian operator-based features were feasible.