Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 640, Taiwan.
IEEE Trans Biomed Eng. 2010 Jun;57(6):1348-57. doi: 10.1109/TBME.2010.2041003. Epub 2010 Feb 17.
Physicians usually diagnose the pathology of the thyroid gland by its volume. However, even if the thyroid glands are found and the shapes are hand-marked from ultrasound (US) images, most physicians still depend on computed tomography (CT) images, which are expensive to obtain, for precise measurements of the volume of the thyroid gland. This approach relies heavily on the experience of the physicians and is very time consuming. Patients are exposed to high radiation when obtaining CT images. In contrast, US imaging does not require ionizing radiation and is relatively inexpensive. US imaging is thus one of the most commonly used auxiliary tools in clinical diagnosis. The present study proposes a complete solution to estimate the volume of the thyroid gland directly from US images. The radial basis function neural network is used to classify blocks of the thyroid gland. The integral region is acquired by applying a specific-region-growing method to potential points of interest. The parameters for evaluating the thyroid volume are estimated using a particle swarm optimization algorithm. Experimental results of the thyroid region segmentation and volume estimation in US images show that the proposed approach is very promising.
医生通常通过甲状腺的体积来诊断其病理。然而，即使通过超声（US）图像找到了甲状腺并对其形状进行了手动标记，大多数医生仍然依赖于获取成本高昂的计算机断层扫描（CT）图像来对甲状腺的体积进行精确测量。这种方法严重依赖于医生的经验，而且非常耗时。患者在获取 CT 图像时会受到高剂量的辐射。相比之下，US 成像不需要电离辐射，而且相对便宜。因此，US 成像成为临床诊断中最常用的辅助工具之一。本研究提出了一种从 US 图像中直接估计甲状腺体积的完整解决方案。该方法使用径向基函数神经网络对甲状腺块进行分类。通过对感兴趣的潜在点应用特定区域生长方法来获取积分区域。使用粒子群优化算法估计用于评估甲状腺体积的参数。US 图像中甲状腺区域分割和体积估计的实验结果表明，该方法非常有前途。
