Chen Chung-Ming, Chou Yi-Hong, Chen Curtis S K, Cheng Jie-Zhi, Ou Yen-Fu, Yeh Fang-Cheng, Chen Kuei-Wu
Institute of Biomedical Engineering, College of Medicine, National Taiwan University, Taipei, Taiwan.
Ultrasound Med Biol. 2005 Dec;31(12):1647-64. doi: 10.1016/j.ultrasmedbio.2005.09.011.
Segmentation of multiple objects with irregular contours and surrounding sporadic spots is a common practice in ultrasound image analysis. A new region-based approach, called cell-competition algorithm, is proposed for simultaneous segmentation of multiple objects in a sonogram. The algorithm is composed of two essential ideas. One is simultaneous cell-based deformation of regions and the other is cell competition. The cells are generated by two-pass watershed transformations. The cell-competition algorithm has been validated with 13 synthetic images of different contrast-to-noise ratios and 71 breast sonograms. Three assessments have been carried out and the results show that the boundaries derived by the cell-competition algorithm are reasonably comparable to those delineated manually. Moreover, the cell-competition algorithm is robust to the variation of regions-of-interest and a range of thresholds required for the second-pass watershed transformation. The proposed algorithm is also shown to be superior to the region-competition algorithm for both types of images.
在超声图像分析中,对具有不规则轮廓和周围散在斑点的多个物体进行分割是一种常见的操作。本文提出了一种新的基于区域的方法——细胞竞争算法,用于同时分割超声图中的多个物体。该算法由两个基本思想组成。一个是基于细胞的区域同时变形,另一个是细胞竞争。细胞通过两遍分水岭变换生成。细胞竞争算法已通过13幅不同对比度噪声比的合成图像和71幅乳腺超声图进行了验证。进行了三项评估,结果表明,细胞竞争算法得出的边界与手动勾勒的边界具有合理的可比性。此外,细胞竞争算法对感兴趣区域的变化以及两遍分水岭变换所需的一系列阈值具有鲁棒性。对于这两种类型的图像,所提出的算法也优于区域竞争算法。