title: Morphological gradient based adapted selective filter for removal of Rician noise from magnetic resonance images.
abstract: Feature/edge-preserving noise removal techniques have a strong potential in several application domains including medical image processing. Magnetic resonance (MR) images have a tendency to gain Rician noise during acquisition. In this article, we have presented genetic algorithms based adapted selective non-local means (GASNLM) filter-based scheme for noise suppression of MR images while preserving the image features as much as possible. We have applied GASNLM filter with optimal parameter values for different frequency image regions to remove the noise. Filter parameter values are optimized by genetic algorithm (GA). A change in NLM filter known as selective weight matrix is also proposed to preserve the image features. The results prove soundness of the method. We have compared results with many well known and latest techniques, and the improvements are discussed.
authors.affiliation: Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
authors.forename: Sultan
authors.initials: S
authors.lastname: Zia
authors.forename: M Arfan
authors.initials: MA
authors.lastname: Jaffar
authors.forename: Tae-Sun
authors.initials: TS
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology; D019295:Computational Biology / Q000379:methods / Q000592:standards; D006801:Humans; D007089:Image Enhancement / Q000379:methods* / Q000592:standards; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted