Comput Methods Programs Biomed - Liver segmentation in MRI: A fully automatic method based on stochastic partitions.

Tópicos

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Resumo

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ? 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.

Resumo Limpo

fulli autom method liver segment magnet reson imag mri despit benefit type acquisit comparison radiolog techniqu comput tomographi ct motiv medic requir liver segment mri carri purpos present new method liver segment base watersh transform stochast partit classic watersh oversegment reduc use markercontrol algorithm improv accuraci select contour gradient origin imag success enhanc appli new variant stochast watersh moreov final classifi perform order obtain final liver mask optim paramet method tune use train dataset appli rest studi dataset obtain result jaccard coeffici comparison method demonstr new variant stochast watersh robust tool automat segment liver mri

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