Magnetic Resonance Image is one of the technologies used for diagnosing brain cancer. Radiographers use the information obtained from MRI images to diagnose the disease and plan further treatment for the patient. MRI images are always corrupted with noise. Removing noise from images is crucial but it is not an easy task. Filtering algorithm is the most common method used to remove noise. A segmentation technique is normally used to process the image in order to detect the abnormality that has been observed, specifically in the brain. However, segmentation alone would be best to implement when the images are in good condition. In the case where the images are corrupted with noise, there are pre-processing steps that need to be implemented first before we can proceed to the next task. Therefore, in this project, we have proposed a simpler method that can de-noise and at the same time segment the image into several significant regions. The proposed method is called the switching-based clustering algorithm. The algorithm is implemented on the MRI brain images which are corrupted with a certain level of salt-and-pepper noise. During the segmentation process, the results show that the proposed algorithm has the ability to minimize the effect of noise without degrading the original images. The density of noise in the MRI images varies from 5% to 20%. The results are compared with the conventional clustering algorithm. Based on the experimental result obtained, the switching-based algorithm provides a better segmentation performance with fewer noise effects than the conventional clustering algorithm. Quantitative and qualitative analyses have shown positive results for the proposed switching-based clustering algorithm.