In image processing, the most challenging task is removing the noise from the images since the segmentation of image patches from noisy images has high complexity. To tackle this challenge, an Optimum Adaptive parameterized Mask Non-Harmonic Analysis (OAMNHA) based image denoising technique was proposed in Curvelet Transform (CT) domain. In this technique, the image was converted into frequency domain representation to decompose it into different subband. Then, edge-preserving segmentation using canny edge detection was applied for each subband to extract the edges and identify the edge regions from a noisy image. However, this process has high time consumption due to its complex computation. Hence in this article, a Neuro-Fuzzy (NF) methodology is proposed as edge-preserving segmentation method. Initially, noisy images represented in the frequency domain are given to the NF edge detector to segment the edge regions and homogeneous textures from a noisy image. Moreover, OAMNHA technique is applied for each region excluding edge regions to restore the noiseless image accurately. This segmentation of noisy images covers Neuro-fuzziness in the choice of the region boundary. Based on this segmentation, the boundary distortion is efficiently minimized since it defines texture boundaries with less time consumption and computational complexity. Also, the accuracy of edge-preserving segmentation is increased significantly. Finally, the experimental results prove that the proposed NF-OAMNHA-CT based image denoising technique has better performance than the OAMNHA-CT technique.