Comput Methods Programs Biomed - Generalized rough fuzzy c-means algorithm for brain MR image segmentation.

Tópicos

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Resumo

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations.

Resumo Limpo

fuzzi set rough set wide use mani cluster algorithm medic imag segment recent combin togeth better deal uncertainti impli observ imag data despit wide spread applic tradit hybrid approach sensit empir weight paramet random initi henc may produc less accur result paper novel hybrid cluster approach name general rough fuzzi cmean grfcm algorithm propos brain mr imag segment algorithm cluster character three automat determin roughfuzzi region accord membership pixel estim respect region locat import region balanc weight paramet bias field mr imag model linear combin orthogon polynomi weight paramet estim bias field correct incorpor iter cluster process algorithm compar exist rough cmean hybrid cluster algorithm synthet clinic brain mr imag experiment result demonstr propos algorithm robust initi nois bias field can produc accur reliabl segment

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