Our study investigates the impact of denoising preprocessing on the accuracy of image segmentation. Specifically, images with Gaussian noise were segmented using the fuzzy c-means method (FCM), local binary fitting (LBF), the adaptive active contour model coupling local and global information (EVOL_LCV), and the U-Net semantic segmentation method. These methods were then quantitatively evaluated. Subsequently, various denoising techniques, such as mean, median, Gaussian, bilateral filtering, and feed-forward denoising convolutional neural network (DnCNN), were applied to the original images, and the segmentation was performed using the methods mentioned above, followed by another round of quantitative evaluations. The two quantitative evaluations revealed that the segmentation results were clearly enhanced after denoising. Specifically, the Dice similarity coefficient of the FCM segmentation improved by 4% to 44%, LBF improved by 16%, and EVOL_LCV presented limited changes. Additionally, the U-Net network trained on denoised images attained a segmentation improvement of over 5%. The accuracy of traditional segmentation and semantic segmentation of Gaussian noise images is improved effectively using DnCNN.