Comput. Biol. Med. - Effective fuzzy c-means based kernel function in segmenting medical images.

Tópicos

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Resumo

The objective of this paper is to develop an effective robust fuzzy c-means for a segmentation of breast and brain magnetic resonance images. The widely used conventional fuzzy c-means for medical image segmentations has limitations because of its squared-norm distance measure to measure the similarity between centers and data objects of medical images which are corrupted by heavy noise, outliers, and other imaging artifacts. To overcome the limitations this paper develops a novel objective function based standard objective function of fuzzy c-means that incorporates the robust kernel-induced distance for clustering the corrupted dataset of breast and brain medical images. By minimizing the novel objective function this paper obtains effective equation for optimal cluster centers and equation to achieve optimal membership grades for partitioning the given dataset. In order to solve the problems of clustering performance affected by initial centers of clusters, this paper introduces a specialized center initialization method for executing the proposed algorithm in segmenting medical images. Experiments are performed with synthetic, real breast and brain images to assess the performance of the proposed method. Further the validity of clustering results is obtained using silhouette method and this paper compares the results with the results of other recent reported fuzzy c-means methods. The experimental results show the superiority of the proposed clustering results.

Resumo Limpo

object paper develop effect robust fuzzi cmean segment breast brain magnet reson imag wide use convent fuzzi cmean medic imag segment limit squarednorm distanc measur measur similar center data object medic imag corrupt heavi nois outlier imag artifact overcom limit paper develop novel object function base standard object function fuzzi cmean incorpor robust kernelinduc distanc cluster corrupt dataset breast brain medic imag minim novel object function paper obtain effect equat optim cluster center equat achiev optim membership grade partit given dataset order solv problem cluster perform affect initi center cluster paper introduc special center initi method execut propos algorithm segment medic imag experi perform synthet real breast brain imag assess perform propos method valid cluster result obtain use silhouett method paper compar result result recent report fuzzi cmean method experiment result show superior propos cluster result

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