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Chan, T.F. and Vese, L.A. (1999) An Active Contour Model without Edges. In: Nielsen, M., Johansen, P., Fogh Olsen, O. and Weickert, J., Eds., Scale-Space 1999, Springer, Berlin, Lecture Notes in Computer Science, Vol. 1682, 141-151.
https://doi.org/10.1007/3-540-48236-9_13

has been cited by the following article:

• TITLE: Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

  AUTHORS: S. Zimeras

  KEYWORDS: Segmentation Techniques, Big Data Analysis, Contour Model, Shape Model, Radial Basis Function, Active Contours, Snakes

  JOURNAL NAME: Advances in Computed Tomography, Vol.10 No.1, March 12, 2021

  ABSTRACT: 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.