Zusammenfassung
The use of the watershed algorithm for image segmentation is widespread
because it is able to produce a complete division of the image. However,
it is susceptible to over-segmentation and in medical image segmentation,
this meant that that we do not have good representations of the anatomy.
We address this issue by thresholding the gradient magnitude image
and performing post-segmentation merging on the initial segmentation
map. The automated thresholding technique is based on the histogram
of the gradient magnitude map while the post-segmentation merging
is based on the similarity in textural features (namely angular second
moment, contrast, entropy and inverse difference moment) belonging
to two neighboring partitions. When applied to the segmentation of
various facial anatomical structures from magnetic resonance (MR)
images, the proposed method achieved an overlap index of 92.6\% compared
to manual contour tracings. It is able to merge more than 80\% of
the initial partitions, which indicates that a large amount of over-segmentation
has been reduced. Results produced using watershed algorithm with
and without the proposed and proposed post-segmentation merging are
presented for comparisons.
Nutzer