Comparative Analysis of Efficient Impulse Noise Removal Techniques applied to Medical Images based on Mathematical Morphology

摘要

Analysis of medical images is an important area of interdisciplinary research. Accurate interpretation and understanding of medical images is increasingly demanding for providing accurate diagnosis and detection of diseases. During the image acquisition, imaging devices are frequently subjected to various noise sources. Impulse noise degrades medical image details such as edges, contours and texture. In this paper we present a novel technique for filtering impulse noise on degraded medical images. The proposed filter is based on noise detector and filtering approach. Impulse noise detector using mathematical residues is proposed to identify pixels contaminated by noise, restore them by applying specialized open-close sequence algorithm and recover degraded images by block smart erase method. The proposed method was applied on both simulated and clinical magnetic resonance images with different levels of noise. The results demonstrated the proposed method not only removed salt and pepper noise but also effectively preserved the image details till noise level of 90%. Compared with several existing noise filtering models, the novel filter has demonstrated to be effective for noise removal, image detail preservation and clinical practice. Findings suggest that by using denoising algorithms, a potential reduction of 90% in noise is possible with no loss of image quality.