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The definition of structures and the extraction of organ’s shape are essential parts of medical imaging applications. These might be applications like diagnostic imaging, image guided surgery or radiation therapy. The aim of the volume definition process is to delineate a specific shape of an organ on a digital image as accurate as possible especially for 3D rendering, radiation therapy, and surgery planning. This can be done, either by manual user interaction or applying imaging processing techniques for the automatic detection of specific structures in the image using segmentation techniques. Segmentation is the process that separates an image into its important features (primitives) so that each of them can be addressed separately. This converts the planar pixel of the image into a distinguishable number of individual organs or tumour that can be clearly identified and manipulated. The segmentation process might involve complicate structures and in this case usually only an expert can perform the task of the identification manually on a slice-by-slice base. Humans can perform this task using complex analysis of shape, intensity, position, texture, and proximity to surrounding structures. In this work we present a set of tools that are implemented on several computer based medical application. Central focus of this work, are techniques used to improve time and interaction needed for a user when defining one or more structures based on segmentation techniques. These techniques involve interpolation methods for the manual volume definition and methods for the semi-automatic organ shape extraction.

Segmentation Techniques of Anatomical Structures with Application in Radiotherapy Treatment Planning