Comput. Biol. Med. - Fast segmentation of bone in CT images using 3D adaptive thresholding.

Tópicos

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Resumo

Fast bone segmentation is often important in computer-aided medical systems. Thresholding-based techniques have been widely used to identify the object of interest (bone) against dark backgrounds. However, the darker areas that are often present in bone tissue may adversely affect the results obtained using existing thresholding-based segmentation methods. We propose an automatic, fast, robust and accurate method for the segmentation of bone using 3D adaptive thresholding. An initial segmentation is first performed to partition the image into bone and non-bone classes, followed by an iterative process of 3D correlation to update voxel classification. This iterative process significantly improves the thresholding performance. A post-processing step of 3D region growing is used to extract the required bone region. The proposed algorithm can achieve sub-voxel accuracy very rapidly. In our experiments, the segmentation of a CT image set required on average less than 10s per slice. This execution time can be further reduced by optimizing the iterative convergence process.

Resumo Limpo

fast bone segment often import computeraid medic system thresholdingbas techniqu wide use identifi object interest bone dark background howev darker area often present bone tissu may advers affect result obtain use exist thresholdingbas segment method propos automat fast robust accur method segment bone use d adapt threshold initi segment first perform partit imag bone nonbon class follow iter process d correl updat voxel classif iter process signific improv threshold perform postprocess step d region grow use extract requir bone region propos algorithm can achiev subvoxel accuraci rapid experi segment ct imag set requir averag less s per slice execut time can reduc optim iter converg process

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