Proc. SPIE. 8669, Medical Imaging 2013: Image Processing
KEYWORDS: Image processing algorithms and systems, Principal component analysis, Data modeling, Image segmentation, Control systems, Medical imaging, Dysprosium, Algorithm development, Fuzzy logic, Spectral models
We present a novel method to incorporate prior knowledge into normalized cuts. The prior is incorporated into
the cost function by maximizing the similarity of the prior to one partition and the dissimilarity to the other. This
simple formulation can also be extended to multiple priors to allow the modeling of the shape variations. A shape
model obtained by PCA on a training set can be easily integrated into the new framework. This is in contrast
to other methods which usually incorporate the prior knowledge by hard constraints during optimization. The
eigenvalue problem inferred by spectral relaxation is not sparse, but can still be solved efficiently. We apply this
method to toy and real data and compare it with other normalized cut based segmentation algorithms and graph
cuts. We demonstrate that our method gives promising results and can still give a good segmentation even when
the prior is not accurate.
Fiber tracking algorithms yield valuable information for neurosurgery as well as automated diagnostic approaches.
However, they have not yet arrived in the daily clinical practice. In this paper we present an open source
integration of the global tractography algorithm proposed by Reisert et.al.1 into the open source Medical Imaging
Interaction Toolkit (MITK) developed and maintained by the Division of Medical and Biological Informatics at
the German Cancer Research Center (DKFZ). The integration of this algorithm into a standardized and open
development environment like MITK enriches accessibility of tractography algorithms for the science community
and is an important step towards bringing neuronal tractography closer to a clinical application. The MITK
diffusion imaging application, downloadable from www.mitk.org, combines all the steps necessary for a successful
tractography: preprocessing, reconstruction of the images, the actual tracking, live monitoring of intermediate
results, postprocessing and visualization of the final tracking results. This paper presents typical tracking results
and demonstrates the steps for pre- and post-processing of the images.