The task of reducing noise from an image is known as image denoising. Although there are various methods and algorithms proposed in the literature, the methods still have limitations. The approaches generally either fail to reduce noise adequately or cause to be lost while effectively reducing noise. Conventional methods have poor performance when considering the success of preserving region boundaries and small structures. Conversely, modern techniques are more effective to smooth images without over smoothing edge details. To address these deficiencies and benefits, in this paper, we aim to develop a framework, which is capable of detecting whether a pixel is a part of edges or textures in an image so the framework can decide which filter should be used depending on region information. The Rank Order Test Method is used to detect image edges. In this way, we determine both which neighbors should be included to build a filter mask in the calculation for each pixel and which filter method should be implemented. We have compared the performance of Bilateral Filter-based methods. Experiments demonstrate that the proposed framework outperforms in terms of both PSNR, SSIM and visual perception for the noise with standard deviations 10, 30, 50. While the average PSNR value was 30.33 DB for the proposed model, the method with the closest result achieved an average score of 28.33 DB.