International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004 
and non-invasively analyse individual anatomy (Glombitza, 
1998; Meinzer, 2002). The image data consists of contrasting 
agent enhanced CT images. Segmental classification is 
calculated by means of an analysis of portal vein structure. It 
defines volumes of the liver tissue components that are assigned 
to the different branches of the vessel tree. 
The integration of computer-aided operation planning in this 
field requires that all steps of the analysis process are embedded 
in a framework that enables the reception of image data and 
forwarding of results. The individual procedural steps are: 
— registration of the different CT-phases (venous, 
arterial, bilary), 
— segmentation of liver and other organs to be visualised 
in the operation planning proposal. These organs are 
referred to as the anatomical landmarks. 
— segmentation of the vessel system, 
— differentiation of the vessel tree, 
— calculation of a resection proposal and visualisation of 
the results, 
— volumetric analysis of the graft size and the remaining 
liver, 
— presentation during the preoperative meeting with 
radiologists, surgeons, internists, anesthetist, 
— visualisation during the surgical intervention. 
The system receives the image data with the aid of the CHILI® 
radiological system. CHILI is capable of communicating with 
the imaging device and stores all data in its own patient and 
image database. Furthermore, it encrypts forwarded and stored 
data sets. 
2.1 Registration of different CT-volumes 
Typically different images of the liver are acquired. Contrast 
agent is used to examine the anatomy of the four vessel 
systems — portal venous, hepatic, arterial and bile duct. For that 
reason images are taken in various points in time. As result 
three different CT-volumes are made which have to be fused to 
get a standardized coordinate system for all vessels. At the 
moment an affine approach is used which leads to very good 
correspondence (Bóttger, 2003; Zitova, 2003). 
  
Registration of two CT- images of different phases (grey, light) 
2.2 Segmentation of the Anatomical Landmarks 
The next step is the segmentation of the image data in order to 
tag the liver. Other anatomical landmarks can also be identified 
for better orientation in the visualisation. Various manual and 
semi-automatic algorithms used for this purpose were integrated 
into the segmentation model and are stable for use in the 
clinical routine. This module provides basic interactions such as 
region growing, merge, cut, threshold, active contours, undo, 
propagation. The framework provides the opportunity for 
generating a graphical user interface for the image processing 
function. Therefore, new functions are easy to introduce. These 
algorithms can be used as two-dimensional tools to identify 
landmarks in cach slice. To reduce the segmentation procedure 
time, many can also be applied to the whole volume (Kunert, 
2004). 
In order to facilitate the interaction between the end user and 
the segmentation module, a set of interaction patterns was 
incorporated and standard parameter values was used. 
[Interaction patterns determine the ways in which the parameter 
values of an algorithm are obtained. For example, they can be 
obtained from the input image with a mouse click. 
  
GUI of the segmentation tool 
2.3 Segmentation of the Vessel Tree 
A module divided in two parts performs vessel segmentation. 
The first one shows the input data and segmented landmarks. 
The user can interactively change the image's level/window 
values until only vessels are displayed. This sets a gray value. 
The portal vein system is used to calculate the resection 
strategy by selecting a starting point in the portal vein system's 
stem. The gray value and the starting point define the 
parameters of the second module part. A modified algorithm for 
vessel tree location according to Selle (Selle, 2000b) generates 
a symbolic description of the vessel tree (Schóbinger, 2002). 
  
Visualisation of the liver and the venous system 
The algorithm then passes through the entire data set searching 
for connected vessel structures. On the basis of a 
skeletonization a symbolic description is generated which 
includes the vessel-diameter at each position. A. three- 
dimensional reconstruction of the resulting vessel structure is 
shown, which the user can rotate and zoom to analyse the 
result. If the result does not correspond to the expected vein 
   
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