Quantitative analysis of digital images requires detection and
segmentation of the borders of the object of interest. Accurate segmentation is
required for volume determination, 3D rendering, radiation therapy, and surgery
planning. In medical images, segmentation has traditionally been done by human
experts. Substantial computational and storage requirements become especially
acute when object orientation and scale have to be considered. Therefore,
automated or semi-automated segmentation techniques are essential if these
software applications are ever to gain widespread clinical use. Many methods have
been proposed to detect and segment 2D shapes, most of which involve template
matching. Advanced segmentation techniques called Snakes or active contours
have been used, considering deformable models or templates. The main purpose of
this work is to apply segmentation techniques for the definition of 3D organs
(anatomical structures) when big data information has been
stored and must be organized by the doctors for medical diagnosis. The
processes would be implemented in the CT images from patients with COVID-19.
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