![](bgb.png)
Figure 14: Contour smoothing for two type of segmenta-
tion, Circle (top) and star (bottom). Smoothing has been
performed using the methods discussed in sections Sec:4.1
and Sec:4.3 and the Absolute roughness index RI
Absolute
is
specified below each image. The window size for RI calcu-
lation is 7% of image size i.e. 7 and roughness threshold κ
, κ
c
for the given experiments was taken as 0.
this method is capable for detecting irregular spikes of
width 1 pixel, However this method can be extended
to detect spikes of multiple pixels by increasing the
window size and the number of neighbors in neigh-
bors set S
ζ
Neighbors
. The method for smoothing holes is
same but in that case we will add a surface point to
the surface instead of removing it in case of a spike.
6 CONCLUSION
In this paper we first discussed the pros and cons of
various metrics that have been commonly used for
the medical image segmentation task. We emphasize
more on the limitations of existing metrics for vol-
umetric segmentation. We then proposed (i) an al-
gorithm that helps to detect all irregular spikes/holes
that exist in the object surface; (ii) a roughness met-
ric that describes how rough of a given object; (iii)
a roughness distance that aims at comparing the sur-
faces between two given objects; (iv) an algorithm
that aims at removing irregular spikes/holes to smooth
the surface. Compare to other volumetric segmen-
tation metrics i.e. Hausdorff distance, our proposed
roughness distance is able to measure the topologi-
cal error whereas roughness metric present the sur-
face roughness. Furthermore, our proposed irregular
spikes/holes detection and surface smoothing can be
applied as a post-processing step in any image seg-
mentation algorithm to improve the accuracy.
ACKNOWLEDGEMENT
This research was supported in part by the Depart-
ment of Radiology, University of Arkansas of Medi-
cal Science UAMS.
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