COMPARATIVE ANALYSIS OF VARIOUS FILTERS FOR DENOISING OF THE SPINAL CORD MRIs

Abstract

Magnetic Resonance Imaging (MRI) techniques are a fundamental and imperative part of the medical image processing field. The images acquired from the MRI machines are affected by the noise. This noise degrades the quality of the images. Acquisition of MRI with noise may give erroneous results. Hence, to enhance the image quality, it is necessary to reduce or remove this noise. To enhance the image quality of MRI, a plethora of filtering algorithms are available along with the morphological operations. In this paper, we have implemented numerous filters like Adaptive Median filter, Median filter, Mean filter, bilateral filter, NLM filter, Gaussian filter, Weiner filter, and morphological operations to eliminate the noise in the MRI of the spinal cord. The scenarios considered are 1. Application of filters, 2. Application of filters followed by morphological operations, and 3. Morphological operations followed by the application of filters. Statistical parameters like Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are found for all three approaches and are used to analyze the performance of these techniques. NLM filters are found to give the best performance when compared to other filters. Morphological operations affect the performance of the filters. Application of morphological operations before filtering degrades the filter performance while applying them after improves the performance. The dataset comprises of 250 spinal cord MRIs with noise. The author inferred that the performance of the filters is improved by applying the filtering techniques after the morphological operation.