Quantitative comparison of our method with the other five well-known methods using the Dice similarity coefficient (DSC) and the mean sum of square distance (MSSD) standards from the test images in Figure 5.

<p>The number of iterations = 400. In the first and second segmentations of the MSLCV, the numbers of iterations were 350 and 50, respectively. Text in bold indicates the best performance for a specific image.</p

Calculation time (in seconds) for each method.

<p>The number of iterations = 400. Text in bold indicates the best performance for a specific image.</p

Segmentation of two synthetic images.

<p>Column (a) is initialization, column (b) is the segmentation results without shape constraint and column (c) is the segmentation results with shape constraint. Comparing column (b) with column (c), we can see that the incorporation of the shape constraint maintains an approximately elliptical contour in the final segmentation results in the segmentation of images with information loss or severe noise near the target region.</p

Zero narrow band.

<p>The green curve represents the initial contour , and the two yellow curves and are the edges of the narrow band.</p

HIFU ultrasound images of uterine fibroids with extremely low SNR, low contrast and weak edges; the red arrows show edges that can easily lead to incorrect segmentation.

<p>HIFU ultrasound images of uterine fibroids with extremely low SNR, low contrast and weak edges; the red arrows show edges that can easily lead to incorrect segmentation.</p

Decomposition images of the Gaussian pyramid.

<p> represents for scale of Gaussian pyramid.</p

The process the MSLCV algorithm.

<p>The process the MSLCV algorithm.</p

Comparison of the MSLCV and SLCV with five other well-known methods by applying them to segment 10 typical ultrasound images (A-J) of uterine fibroids for HIFU therapy.

<p>Columns 1 and 2 are the original images and the initial contours. Columns 3 to 9 respectively show the segmentation results for GAC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103334#pone.0103334-Caselles1" target="_blank">[13]</a>, C-V <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103334#pone.0103334-ChanT1" target="_blank">[16]</a>, LCV <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103334#pone.0103334-Lankton1" target="_blank">[19]</a>, RSF <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103334#pone.0103334-Li3" target="_blank">[18]</a>, LGF <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103334#pone.0103334-Wang5" target="_blank">[38]</a>, SLCV and MSLCV. The green curves are manual segmentation results by the specialist as ground truth, and the red curves are the final segmentation contours from these methods.</p

Accuracy and calculation time under different scales (iterations = 300).

<p>Accuracy and calculation time under different scales (iterations = 300).</p

Effects of different localizing radii on the segmentation results.

<p>The first row and the second row present the segmentation results with different localizing radii using the LCV model and the MSLCV model, respectively. The green curves are manual segmentation results by the specialist, the red curves are the results from the experiments, and the yellow circles represent the size of localized regions formed by the localizing radius. The localizing radii of (a) and (d), (b) and (e), and (c) and (f) are 4, 20 and 45, respectively.</p