This paper evaluates the quality of segmentation achieved by a level set evolution strategy call Tunneling Descent. Level sets often evolve and become stationary at the nearest local minimum of an energy function. A
comparison of the local level set minimum with a global minimum is important for many applications. This is
especially true of ultrasound segmentation where ultrasound speckle can introduce many local minima which trap
the level set. In this paper, we compare the quality of the level set segmentation with the quality of segmentation
achieved by (1) simulated annealing (with three different cooling schedules), and (2) random sampling, and (3)
three experts (manual segmentation). Simulated annealing and random sampling offer global minimization.
In this paper, the quality of the segmentation is compared for 21 clinically-obtained images. The comparison
is carried out using two performance measures: the amount by which the global minimizers can further decrease
the level set energy, and the contour distance between the segmentations themselves. The results show that level
set segmentation is within one ultrasound resolution cell of the global minimum. The results also show that the
level set segmentation is quite close to manual segmentation.
We introduce a new speckle suppression approach for 3-D ultrasound
images. The proposed method seeks to enhance volume visualization of
3-D ultrasound images, and improve the accuracy of volume
determination. Extended from 2-D nonlinear multiscale wavelet
diffusion, the proposed method is developed on the basis of an
integration of the 3-D nonlinear diffusion and 3-D dyadic wavelet
transform techniques. Our approach uses the normalized wavelet
modulus as an edge map to expose the intrinsic speckle/edge
relation. Relying on the statistical analysis of this edge map, the
method is able to classify homogenous speckle regions and edges in a
3-D volume, as well as provide strong speckle suppression and
boundary preservation. Our method is validated using both synthetic
and real 3-D ultrasonic images. Performance improvement over other
filters is quantified by evaluating standard quality indices.
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