An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images
An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images
复制标题
利用邻近信息进行超声图像中乳腺肿瘤分割的自适应模糊 C 均值方法
DOI：
10.1002/mp.12350
复制
发表时间：
2017-07-01
期刊：
影响因子：
3.8
通讯作者：
Mutic, Sasa
中科院分区：
文献类型：
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作者：
Feng, Yuan;Dong, Fenglin;Mutic, Sasa
目的：超声成像技术已广泛应用于乳腺肿瘤的诊断和介入治疗.肿瘤的自动勾画是关键的第一步，特别是对于计算机辅助诊断（CAD）和US引导的乳腺手术。然而，US图像的固有属性，如低对比度和模糊的边界提出了挑战的自动分割的乳腺肿瘤。因此，本研究的目的是提出一种分割算法，可以在US图像中的乳腺肿瘤轮廓。方法：利用每个像素的邻域信息，采用基于Hausdorff距离的模糊c均值（FCM）方法。通过比较两个图像之间的互信息，自适应地更新相邻区域的大小。聚类过程的目标函数通过结合欧几里得距离和自适应计算的Hausdorff距离进行更新。通过与三位专家的人工分割结果进行比较，对分割结果进行评价。结果还与具有空间约束的基于核诱导距离的FCM、没有自适应区域选择的方法以及传统FCM进行了比较。对30幅患者图像的分割结果表明，自适应方法的敏感度、特异度、Jaccard相似度和Dice系数分别为93.60 ± 5.33%，97.83 ± 2.17%，86.38 ± 5.80%和92.58 ± 3.68%。平均对称表面距离（ASSD）、均方根对称距离（RMSD）和最大对称表面距离（MSSD）的基于区域的度量分别为0.03 +/- 0.04 mm、0.04 +/- 0.03 mm和1.18 +/- 1.01 mm。除灵敏度外，其他指标均优于非自适应算法和传统FCM。只有三个区域为基础的指标优于核诱导的距离为基础的FCM与空间constrains.Conclusion：包括像素的邻居信息自适应分割US图像提高了分割性能。结果表明，该方法在乳腺肿瘤CAD和其他US引导程序中的潜在应用。(C)2017年美国医学物理学家协会
Purpose: Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images.Methods: To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM.Results: Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 5.33%, 97.83 +/- 2.17%, 86.38 +/- 5.80%, and 92.58 +/- 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 +/- 0.04 mm, 0.04 +/- 0.03 mm, and 1.18 +/- 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints.Conclusion: Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures. (C) 2017 American Association of Physicists in Medicine