The necessity of proposing algorithms that are effective in noisy image segmentation is clear in many real-world applications. This paper proposes a new algorithm for severely noisy image segmentation by looking at the proper choice of feature, and feature manipulation. We are using Discrete Wavelet Transformation (DWT) as a tool to provide our method with the proper feature, and then we manipulate it via wavelet shrinkage. Particle Swarm Optimization (PSO) is used to adaptively search for threshold values that produce the best segmentation results when applied in the wavelet shrinkage, and Fuzzy C-Means (FCM) is used as a fitness metric in PSO. The proposed method was tested on two different datasets being extremely contaminated with the common Gaussian noise. These tests indicate the superior performance and consistency of the proposed method in comparison to other state-of-the-art methods.