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7 February 2011 Segmentation and visualization of anatomical structures from volumetric medical images
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Abstract
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, and 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
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