Comput Methods Programs Biomed - Efficient inhomogeneity compensation using fuzzy c-means clustering models.

Tópicos

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Resumo

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels. The theoretical assumptions are demonstrated using the fuzzy c-means algorithm, but the proposed modification is compatible with a various range of c-means clustering based INU compensation and MR image segmentation algorithms. Experiments carried out using synthetic phantoms and real MR images indicate that the proposed approach produces practically the same segmentation accuracy as the conventional formulation, but 20-30 times faster.

Resumo Limpo

intens inhomogen intens nonuniform inu undesir phenomenon repres main obstacl magnet reson mr imag segment registr method various techniqu propos elimin compens inu embed classif cluster algorithm general difficulti inu reach high amplitud usual suffer high comput load studi reformul design cmean cluster base inu compens techniqu identifi separ global work comput cost oper can appli gray intens level instead individu pixel theoret assumpt demonstr use fuzzi cmean algorithm propos modif compat various rang cmean cluster base inu compens mr imag segment algorithm experi carri use synthet phantom real mr imag indic propos approach produc practic segment accuraci convent formul time faster

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