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4 VALIDATION AND RESULTS
The method has been evaluated for a total of 42 MRI
studies. Automatic initializations have been com-
pared to manual ones: five users interactively initial-
ized the model. To assess the quality of the automatic
method three figures of merit have been evaluated:
average point-to-surface distance, texture error, and
time performance.
The average point-to-surface (PTS) distance is
calculated for the manually and automatically placed
mean model shape relative to the accurately done
manual ground truth segmentation. This was only
possible for a subset of 31 data sets where ground
truth segmentation was available. Figure 4 (top) sum-
marizes PTS measures achieved by users and the au-
tomatic method. It is observed that automatic initial-
izations come close to the manual ones. Please note
that the average discrepancy of 6mm only refers to
rigid initialization of the mean model. In this work
only initialization is investigated – no subsequent de-
formations of the model are applied in attempt to
achieve final segmentations.
In order to evaluate initializations for which no
ground truth was given, the texture difference be-
tween mean model and image data was determined.
Figure 4 (middle) shows the quality of matches for
all (unsegmented) 42 data sets after manual/automatic
initializations with respect to texture difference. It is
again concluded that the automatic method generates
initializations qualitatively comparable to those of the
users. The final results – the initializations – for all
validation data sets are visualized in figure 5 for the
central slices of end-diastole.
While similar in quality figure 4 (bottom) proves
another advantage of the automatization over user in-
teraction – the speed-up. The average initialization of
1 second has been achieved by a Java implementation.
5 CONCLUSIONS
This work has introduced an automatic and robust
method for localization of LV and RV in 4D cardiac
MRI data. The method has been designed with help
of few elementary image processing operators. The
Hough-transformation for circles was adapted to op-
erate on original image gray values instead of gradi-
ent magnitudes which makes the detection of the LV
highly robust. The overall quality of initialization has
been assessed by a user study. Time performance of
the method indicates a high potential for daily clinical
use.
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