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In particular, they adopt a dynamic segment size control mechanism, which makes the segmentation adapt to the dynamics of the network.
On the other hand, the shape distortion makes the segmentation method less reliable.
This makes the segmentation time very difficult to predict or to control.
SAR images are inherently corrupted by speckle noise, which degrades the quality of SAR data and makes the segmentation of SAR images difficult.
This makes the segmentation time highly dependent on the contents of the image, because the distance which the nodes need to travel can be varying in different images or even in different parts of a single image.
It also makes the segmentation and 3D reconstruction difficult or impossible.
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Moreover, we propose an iterative scheme of RW, making the segmentation more performant.
Besides, a general method which combines shape information into graph cuts is presented, which can make the segmentation more accurate.
The algorithm performance is tested on real images with natural artifacts that make the segmentation process difficult.
To make the segmentation efficient, a duality based optimization approach is utilized to minimize the energy functional.
Moreover, high variations in liver margins, different image characteristics with different CT scanners and atypical liver shapes make the segmentation process even harder.
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