Medical imaging indeed has many future applications also in the field of advanced medical surgery, also successful implementation of it would enable surgery to remain the industry's patterning technology of choice for years to come. However, much work remains to be done in order to determine whether or not it will ever be ready for outsourcing. Furthermore, the time scale during which medical imaging has to prove itself is somewhat uncertain. Several years ago, it was assumed that this software would be needed by around 2005 in order to implement virtual surgery. Currently, medical consensus is that ordinary surgery will have to do the job, even though it will be difficult to do so.
One of the most important steps in medical image analysis is segmentation, i.e., the process of partitioning the contents of an image into objects of interest and background in order to facilitate further analysis and information extraction. Segmentation is needed in diagnostics, therapy monitoring, surgery planning, and several other medical applications. To manually segment the structures of interest in medical datasets is a very tedious and error-prone procedure, while fully automatic segmentation is, despite decades of research, still seen as an unsolved problem. Therefore, many methods are semi-automatic, i.e., the segmentation algorithms are provided with high-level knowledge from the user by some means of interaction.
A successful semi-automatic method combines the outstanding capabilities of the human to recognize and locate objects in images with the computer's ability to quickly perform tedious and time-consuming tasks such as counting and exact object delineation. The interactive part is highly dependent on the user interface. Interfaces that rely on 2D interaction have many drawbacks when the data is 3D, since it is not straight-forward how to map 2D interaction into 3D space.
Image segmentation is the process of separating objects of interest from each other and from the background and is one of the most important topics in image analysis. Since segmentation often is an initial step, all further analysis and information extraction will depend on the result of the segmentation. In medical applications, segmentation is needed for basic tasks such as volume and area measurements and more complex tasks such as 3D model extraction for surgical planning and image-guided therapy. There are several things that make medical image segmentation hard, e.g., similar intensity patterns for different tissues (low contrast) and bad image quality due to noise from the imaging system. Another problem is the high shape variability of organs making it hard to incorporate a priori information
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