Analysis & Evaluation of Image filtering Noise reduction technique for Microscopic Images

Image processing in the field of microscopy is gaining popularity with the use of advanced techniques used for accurate classification of cells. The abnormalities in the image can be detected accurately after the image has been processed using digital image processing techniques. Preprocessing is an important step in which the noise and other undesirable content will be removed. Preprocessing is essential because the noise will cause inaccuracy in the image processing techniques. Filtering the image to denoise is the first step in preprocessing. The accuracy of denoising using filters determines the quality of the entire image processing cycle. This paper proposes filters to denoise the microscopic images. In this paper, two filters - Wiener and Median filters are compared for accuracy in denoising the image in the preprocessing stage of the cell classification. The Wiener filter and the Median filter were implemented and compared for Peak Signal to Noise Ratio (PSNR) which can be used for better image classification in the later stages. The proposed method was tested using 35 real time images which has Gaussian noise. The two filters perform for the collected dataset whereas the median filter gives highest PSNR outperforming the Wiener filter.