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).