Improving genericity and performances of medical systems based on image analysis

This paper deals with the improvement of the genericity of the well-known ITK medical image processing library. Such a library is the core of any medical system/software based on the medical image analysis (e.g. computer aided diagnosis, surgery planning...). This proposed improvement consists in, without algorithm rewriting, extending ITK iterators (leading to an ITK++framework) in order to constrain algorithms to user-specified image areas. We experimentally evaluate this work by considering the practical case of liver vessel segmentation from CT-scan images, where it is pertinent to constrain processing's to the liver area: this reduces the number of voxels to process. Experimental results clearly prove the interest of this work: for example, the anisotropic filtering of this area is performed in only 16 seconds with our proposed solution, while 52 seconds are required using the native limited ITK framework. Moreover, we also show that the code resulting from the proposed improvement remains easy to manage. A major advantage of the proposed solution is that the native ITK library is not modified because the improvement consists in some add-ons: this facilitates the further evaluation of the pertinence of the proposed design while preserving the native ITK framework.