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IEEE 3333.2.1-2015 pdf free

IEEE 3333.2.1-2015 pdf free.IEEE Recommended Practice for Three-Dimensional (3D) Medical Modeling.
A medical 3D model using the phase information of segmentation and feature will be able to reconstruct a patient’s body. Medical 3D reconstruction shall be completed with surface- and volume-rendering data.
Segmentation in medical imaging is generally considered a difficult problem, mainly because of the sheer size of the datasets coupled with the complexity and variability of the anatomic organs. The situation is worsened by the shortcomings of imaging modalities, such as sampling artifacts, noise, low contrast, etc.,that may cause the boundaries of anatomical structures to be indistinct and disconnected. Thus, the main challenge of segmentation algorithms is to accurately extract the boundary of the organ or ROI and separate it from the rest of the dataset.
Numerous segmentation algorithms are found in the literature. Due to the nature of the problem of segmentation, most of these algorithms are specific to a particular problem and thus have lttle significance for most other problems.
This standard will try to cover all the algorithms that have a generalized scope and that are the basis of most current segmentation techniques. In addition, we will concentrate only on 3D volumes and thus present each algorithm with respect to its application on 3D volumes.
To make 3D models from 2D images, it was necessary to segment the area of interest in the 2D image. 3D models were made by stacking serial 2D images, therefore segmentation was also done consecutively.Generally, segmentation was done on horizontal images, but in some cases, segmentation was done on coronal or sagittal images that were made by stacking horizontal images and then cutting in the coronal or sagital directions (see Figure 6).
In the medical imaging field, a variety of algorithms for the automation of segmentation have been developed [B1], [B3], [B4], [B5]. However, some anatomical structures cannot be segmented automatically, such as detailed muscles, because the borders of neighboring structures were not clearly identified on 2D images.
Segmentation could be done efficiently using commercial software that handles medical images [B6], [B10]. These software packages have many functions, including semiautomatic segmentation, when optimally used.IEEE 3333.2.1 pdf free download.

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