Comput. Biol. Med. - Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper proposes an unsupervised tumour segmentation approach for PET data. The method computes the volumes of interest (VOIs) with sub-voxel precision by considering the limited image resolution and partial volume effects. First, an improved anisotropic diffusion filter is used to remove image noise. A hierarchical local and global intensity active surface modelling scheme is then applied to segment VOIs, followed by an alpha matting step to further refine the segmentation boundary. The proposed method is validated on real PET images of head-and-neck cancer patients with ground truth provided by human experts, as well as custom-designed phantom PET images with objective ground truth. Experimental results show that our method outperforms previous automatic approaches in terms of segmentation accuracy.

Resumo Limpo

paper propos unsupervis tumour segment approach pet data method comput volum interest voi subvoxel precis consid limit imag resolut partial volum effect first improv anisotrop diffus filter use remov imag nois hierarch local global intens activ surfac model scheme appli segment voi follow alpha mat step refin segment boundari propos method valid real pet imag headandneck cancer patient ground truth provid human expert well customdesign phantom pet imag object ground truth experiment result show method outperform previous automat approach term segment accuraci

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