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Computational human phantoms are models of the human body used in computerized analysis. Since the 1960s, the radiological science community has developed and applied these models for ionizing radiation dosimetry studies. These models have become increasingly accurate with respect to the internal structure of the human body.
Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine.
Breast cancer affects 1 in 8 women, and an estimated 27% of people live at least 5 years after being diagnosed with stage IV cancer according to the National Cancer Institute. [1] Mammography is the most commonly used method to screen for cancer, but there are three major drawbacks. [ 2 ]
Most USCT systems aiming for 3D-imaging, either by synthesizing ("stacking") 2D images or by full 3D aperture setups. Another aim is quantitative imaging instead of only qualitative imaging. The idea of Ultrasound computer tomography goes back to the 1950s with analogue compounding setups, [ 5 ] [ 6 ] [ 7 ] in the mid 1970s the first "computed ...
Body center rendered in MeVisLab. MeVisLab features include: [3] [4] [5] Image processing with the MeVis Image Processing Library (ML): The ML is a request-driven, page-based, modular, expandable C++ image processing library supporting up to six image dimensions (x, y, z, color, time, user dimensions).
Image-based meshing is the automated process of creating computer models for computational fluid dynamics (CFD) and finite element analysis (FEA) from 3D image data (such as magnetic resonance imaging (MRI), computed tomography (CT) or microtomography).
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Schematic image showing a woman undergoing a mammogram imaging examination. In medicine, breast imaging is a sub-speciality of diagnostic radiology that involves imaging of the breasts for screening or diagnostic purposes. There are various methods of breast imaging using a variety of technologies as described in detail below.