Precision is definitely required in medical treatments, however, most three-dimensional (3-D) renderings of medical images lack for required precision. This study aimed at the development of a precise 3-D image processing method to discriminate clearly the edges. Since conventional Computed Tomography (CT), Positron Emission Tomography (PET), or Magnetic Resonance Imaging (MRI) medical images are all slice-based stacked 3-D images, one effective way to obtain precision 3-D rendering is to process the sliced data with high precision first then to stack them together carefully to reconstruct a desired 3-D image. A recent two-dimensional (2-D) image processing method known as the entropy maximization procedure proposed to combine both the gradient and the region segmentation approaches to achieve a much better result than either alone seemed to be our best choice to extend it into 3-D processing. Three examples of CT scan data of medical images were used to test the validity of our method. We found our 3-D renderings not only achieved the precision we sought but also has many interesting characteristics that shall be of significant influence to the medical practice. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Lee, T. F., Cho, M. Y., Shieh, C. S., Chao, P. J., & Chang, H. Y. (2005). Precise segmentation rendering for medical images based on maximum entropy processing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3683 LNAI, pp. 366–373). Springer Verlag. https://doi.org/10.1007/11553939_53
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