Advances in medical image processing
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. User interaction and system responses must be designed carefully to guide the user
within the com- plex 3D domain. However, automation of the segmentation process
is another challenge in medical image processing. Since
Medical image processing is an exciting and active ﬁeld of research, where disciplines such as Engineering, Computer Science, Physics, Biology and Medicine interdisciplinarily cooperate in order to improve health care. Most frequently, medical images are the basis of diagnostics, treatment planning, and treatment, but medical images are likewise important for medical education, research and epidemiology. Since the discovery of X-rays more than 100 years ago, several imaging modalities have been developed to visualize anatomy, tissue morphology, as well as muscular-skeletal and nervous functionality of the human body. Computed tomography (CT), magnet resonance imaging (MRI) and Ultrasound (US) are commonly known, but others, such as magnetic resonance spectroscopy imaging (MRSI), ﬂuorescence endoscopy, CT angiography or optical surface scanning (OSS) are also applied in clinical routine. The discipline of medical image processing deals with generation and reconstruction, pre-processing and improvement, analysis and quantiﬁcation, as well as visualization and management of all kind of medical images. Frequently used steps in the image processing pipeline are image registration, which aims at aligning image data from different modalities, subjects, or points of time, as well as image segmentation, which aims at localization and delineation of relevant objects in two-dimensional (2D) projections and threedimensional (3D) visualization.