Medical imaging segmentation provides vital information for surgical diagnosis, and usually demands an accurate segmentation. A fully automated computed tomography image segmentation method is proposed. This method is unsupervised and automatic estimation of the required parameters for identifying the human body as a region of interest. The proposed methodology consists of four steps: First, a body region of interest is masked by a method based on thresholding and basic morphological operations. Second, a body region of interest is identified using chain codes and a method for collecting adjacent contours. Next, the identification of background non-regions of interest is performed using an entropy algorithm. Finally, the human body segment is identified using a GrabCut algorithm. According to the visual evaluation results, segmentation of the human body, from the Computed Tomography images, was seen to be precise and accurate. The analysis provided evidence that the human body segmentation method could be applied to segmenting other organs, registering different image modalities or speeding-up the generation of digitally reconstructed radiographs.

Coding and Information Theory

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Automatic body segmentation from computed tomography image

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Automatic body segmentation from computed tomography image

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