IEEE Std 3333.2.1-2015 pdf download – IEEE Recommended Practice for Three-Dimensional (3D) Medical Modeling.
3.2 Modeling architecture 3.2.1 Medical image acquisition 3.2.1.1 Background An important ingredient in further improving 3D video-processing technologies is the incorporation of better models of 3D perception. Among these, saliency detection, or the automated discovery of points of high visual interest, conspicuity, or task relevance, is a challenging problem. 3.2.1.2 General requirement In the medical field, the most important problems are treatment planning and virtual practice from advanced imaging modalities. These problems should be solved by employing various methods using a 3D model of the patient, which will improve diagnostic accuracy and allow for the simulation of medical procedures using a controller. Accurate 3D models shall be obtained by 3D reconstruction of serial sectional images of the structures that are derived from computed tomographs (CTs) and magnetic resonance images (MRIs). 3.2.1.3 Acquisition procedure To make 3D patient models, sequential 2D images are necessary and should be acquired from CTs, MRIs, and an optical microscope. Generally, 2D patient images should be acquired from a CT or MRI scanning of the patients’ body by intervals of a few millimeters. Each image has its own strengths and weaknesses according to what has been observed. In the case of CT, bones will be clearly identified, therefore bone- or joint-related diseases will be effectively shown. In the case of MR, cartilage, muscle, and nerves will be clearly shown as well as the bone, therefore MR is shown to be more effective (see Figure 4).
4. Segmentation 4.1 Overview of segmentation 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. 4.2 Segmentation methods 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 little significance for most other problems.