Design of Ultrafast Imaging System for Thyroid Nodule Detection

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract A complete solution to estimate the volume of the thyroid gland directly from ultrasound (US) images is proposed in this paper. 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 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, Ultrasound imaging does not require ionizing radiation and is relatively inexpensive. Ultrasound imaging is thus one of the most commonly used auxiliary tools in clinical diagnosis. 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. Simulation results of the thyroid show that the region segmentation can be automatically achieved and the volume of thyroid nodule can be precisely measured.