Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy.
Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy.
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HIFU 治疗中子宫肌瘤超声图像的多尺度和形状约束局部区域主动轮廓分割。
DOI：
10.1371/journal.pone.0103334
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Zhao J
中科院分区：
文献类型：
--
作者：
Liao X;Yuan Z;Zheng Q;Yin Q;Zhang D;Zhao J
针对高强度聚焦超声（High intensity Focused Ultrasound， HIFU）超声图像中存在的强度不均匀性严重、边界模糊等问题，提出了一种精确高效的基于多尺度和形状约束的局部区域的活动轮廓模型（MSLCV），以实现子宫肌瘤HIFU超声图像中目标区域的准确高效分割。我们在基于局部区域的活动轮廓中引入了一种新的形状约束，对活动轮廓进行约束，从而获得所需的精确分割，避免边界泄漏和过度收缩。基于局部区域的活动轮廓建模适用于超声图像，但对于子宫肌瘤的HIFU超声图像仍不能获得满意的分割效果。通过在区域水平集框架中加入形状约束，改进了基于局部区域的活动轮廓模型，提高了分割精度。为了克服初始化的敏感性，提出了一些改进措施，并提出了一种多尺度分割方法来提高分割效率。为了获得更好的分割效果，设计了自适应定位半径大小选择函数。实验结果表明，MSLCV模型的精度和效率明显高于常规方法。通过实验对MSLCV模型进行了定量验证，DSC （Dice similarity coefficient）均值为0.94,MSSD （mean sum of square distance）均值为25.16。此外，采用多尺度分割方法，MSLCV模型的平均分割时间降至局部区域活动轮廓模型（LCV模型）的1/8左右。针对HIFU治疗中子宫肌瘤超声（UFUS）图像的半自动分割，设计了一种精确高效的多尺度、形状受限的局部区域主动轮廓模型。与其他方法相比，该方法提供了更准确、更高效的分割结果，与专家手工分割的结果非常接近。
To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids. We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results. Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model). An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.
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影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
影响因子：
19.5
作者：
RONFARD, R
通讯作者：
RONFARD, R
DOI：
10.1109/iccv.2011.6126468
发表时间：
2011-11
期刊：
Proceedings. IEEE International Conference on Computer Vision
影响因子：
--
作者：
Appia V;Yezzi A
通讯作者：
Yezzi A
影响因子：
78.5
作者：
Kennedy, JE
通讯作者：
Kennedy, JE
DOI：
10.1109/tip.2011.2146190
发表时间：
2011-07
期刊：
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
影响因子：
--
作者：
Li C;Huang R;Ding Z;Gatenby JC;Metaxas DN;Gore JC
通讯作者：
Gore JC