Zhang Dong, Xu Menglong, Quan Long, Yang Yan, Qin Qianqing, Zhu Wenbin
School of Physics and Technology, Wuhan University, Wuhan, 430072, People's Republic of China.
Phys Med Biol. 2015 Mar 7;60(5):1807-30. doi: 10.1088/0031-9155/60/5/1807. Epub 2015 Feb 6.
It is crucial in high intensity focused ultrasound (HIFU) therapy to detect the tumor precisely with less manual intervention for enhancing the therapy efficiency. Ultrasound image segmentation becomes a difficult task due to signal attenuation, speckle effect and shadows. This paper presents an unsupervised approach based on texture and boundary encoding customized for ultrasound image segmentation in HIFU therapy. The approach oversegments the ultrasound image into some small regions, which are merged by using the principle of minimum description length (MDL) afterwards. Small regions belonging to the same tumor are clustered as they preserve similar texture features. The mergence is completed by obtaining the shortest coding length from encoding textures and boundaries of these regions in the clustering process. The tumor region is finally selected from merged regions by a proposed algorithm without manual interaction. The performance of the method is tested on 50 uterine fibroid ultrasound images from HIFU guiding transducers. The segmentations are compared with manual delineations to verify its feasibility. The quantitative evaluation with HIFU images shows that the mean true positive of the approach is 93.53%, the mean false positive is 4.06%, the mean similarity is 89.92%, the mean norm Hausdorff distance is 3.62% and the mean norm maximum average distance is 0.57%. The experiments validate that the proposed method can achieve favorable segmentation without manual initialization and effectively handle the poor quality of the ultrasound guidance image in HIFU therapy, which indicates that the approach is applicable in HIFU therapy.
在高强度聚焦超声（HIFU）治疗中，以较少的人工干预精确检测肿瘤对于提高治疗效率至关重要。由于信号衰减、斑点效应和阴影，超声图像分割成为一项艰巨的任务。本文提出了一种基于纹理和边界编码的无监督方法，专为HIFU治疗中的超声图像分割而定制。该方法将超声图像过度分割成一些小区域，然后使用最小描述长度（MDL）原理进行合并。属于同一肿瘤的小区域由于保留了相似的纹理特征而被聚类。通过在聚类过程中对这些区域的纹理和边界进行编码获得最短编码长度来完成合并。最终通过一种无需人工交互的算法从合并区域中选择肿瘤区域。该方法的性能在50张来自HIFU引导换能器的子宫肌瘤超声图像上进行了测试。将分割结果与人工勾勒结果进行比较以验证其可行性。对HIFU图像的定量评估表明，该方法的平均真阳性率为93.53%，平均假阳性率为4.06%，平均相似度为89.92%，平均归一化豪斯多夫距离为3.62%，平均归一化最大平均距离为0.57%。实验验证了所提出的方法无需人工初始化即可实现良好的分割，并能有效处理HIFU治疗中超声引导图像质量较差的问题，这表明该方法适用于HIFU治疗。
