Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Nilanjan Dey (Autor) Outra; Amira S. Ashour (Autor) Outra; Samsad Beagum (Autor) Outra; Dimitra Sifaki Pistola (Autor) Outra; Mitko Gospodinov (Autor) Outra; Evgeniya Peneva Gospodinova (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Journal of Imaging

Vol. 1 Nº 1

Páginas: 60-84

Editora: MDPI

Indexação

Scopus - 70 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias

CORDIS: Ciências Tecnológicas

Outras Informações

ID Authenticus: P-00K-HPZ

DOI: 10.3390/jimaging1010060

Abstract (EN): Magnetic resonance imaging (MRI) is extensively exploited for more accurate pathological changes as well as diagnosis. Conversely, MRI suffers from various shortcomings such as ambient noise from the environment, acquisition noise from the equipment, the presence of background tissue, breathing motion, body fat, etc. Consequently, noise reduction is critical as diverse types of the generated noise limit the efficiency of the medical image diagnosis. Local polynomial approximation based intersection confidence interval (LPA-ICI) filter is one of the effective de-noising filters. This filter requires an adjustment of the ICI parameters for efficient window size selection. From the wide range of ICI parametric values, finding out the best set of tunes values is itself an optimization problem. The present study proposed a novel technique for parameter optimization of LPA-ICI filter using genetic algorithm (GA) for brain MR images de-noising. The experimental results proved that the proposed method outperforms the LPA-ICI method for de-noising in terms of various performance metrics for different noise variance levels. Obtained results reports that the ICI parameter values depend on the noise variance and the concerned under test image.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 25