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Medical imaging generates the visual representation of the interior body parts for the clinical analysis/ medical intervention. Now a days, an advanced medical imaging technique i.e. MRI provides acute dissection anatomical information about the human soft tissues. MRI generally suffers from poor contrast, low quality due to improper brightness & blurriness. So contrast manipulation is compulsively needed. Image enhancement is taken as the initial step which defines the accuracy of result. The prime objective is to enhance the visual appearance for further image analysis i.e. detection, segmentation, feature extraction/selection and even classification. Out of all the current image enhancement methods, the appropriate choice must be influenced by the facts i.e. visual perspective, modality and climatic conditions. The noise model and the filter reconstruction mainly decide the trade-off between noise reduction and feature preservation of the original image. In this paper, Median filter (MF), Average filter (AF), Wiener Filter (WF) and Gaussian filter (GF) are used to compare the effects of most dominant noises in MR images by calculating the statistical parameters i.e. Mean Square Error, PSNR, RMSE and MAE. Also, the MR images impinge with the variable noise density for effective comparative analysis of the filters. Further, the proposed algorithm detected the tumor region appropriately.

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