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7 February 2011Segmentation and visualization of anatomical structures from
volumetric medical images
This paper presents a method that can extract and visualize anatomical structures from volumetric medical images by
using a 3D level set segmentation method and a hybrid volume rendering technique. First, the segmentation using the
level set method was conducted through a surface evolution framework based on the geometric variation principle. This
approach addresses the topological changes in the deformable surface by using the geometric integral measures and level
set theory. These integral measures contain a robust alignment term, an active region term, and a mean curvature term.
By using the level set method with a new hybrid speed function derived from the geometric integral measures, the
accurate deformable surface can be extracted from a volumetric medical data set. Second, we employed a hybrid volume
rendering approach to visualize the extracted deformable structures. Our method combines indirect and direct volume
rendering techniques. Segmented objects within the data set are rendered locally by surface rendering on an object-by-object
basis. Globally, all the results of subsequent object rendering are obtained by direct volume rendering (DVR).
Then the two rendered results are finally combined in a merging step. This is especially useful when inner structures
should be visualized together with semi-transparent outer parts. This merging step is similar to the focus-plus-context
approach known from information visualization. Finally, we verified the accuracy and robustness of the proposed
segmentation method for various medical volume images. The volume rendering results of segmented 3D objects show
that our proposed method can accurately extract and visualize human organs from various multimodality medical volume
images.
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Jonghyun Park, Soonyoung Park, Wanhyun Cho, Sunworl Kim, Gisoo Kim, Gukdong Ahn, Myungeun Lee, Junsik Lim, "Segmentation and visualization of anatomical structures from volumetric medical images," Proc. SPIE 7877, Image Processing: Machine Vision Applications IV, 78770U (7 February 2011); https://doi.org/10.1117/12.872684