基于双边滤波-距离正则化水平集演化算法的甲状腺超声图像分割

Ultrasound image segmentation of thyroid gland based on bilateral filters-distance regularized level set evolution algorithm

超声图像有斑点噪声,且对比度低、边界模糊,所以甲状腺超声图像分割较为困难。针对此问题,本研究提出一种结合双边滤波(bilateral filters,BF)和改进边缘指示函数的距离正则化水平集演化(distance regularized level set evolution,DRLSE)模型的分割算法。先对甲状腺超声图像进行双边滤波,然后采用改进的DRLSE模型对甲状腺进行分割。通过与采用另外两种边缘指示函数的DRLSE模型对比,本研究提出的BF-DRLSE模型能减少斑点噪声对分割过程的影响,并在明显减少曲线演化运行时间和迭代次数的情况下有效分割甲状腺。

Diagnosis of thyroid disease often requires segmentation of the thyroid gland from the ultrasound image.Because of speckle noise,low contrast,blurred boundary,it is difficult to segment the thyroid ultrasound image.To solve the problem,a segmentation algorithm that combines Bilateral Filters(BF)and improved Distance Regularized Level Set Evolution(DRLSE)model with improved edge indication function was proposed.The thyroid ultrasound image was bilaterally filtered first,and then the thyroid was segmented using the improved DRLSE model.Comparing with DRLSE model experiments using two other kinds of edge indicator functions,the BF-DRLSE model proposed in this paper can reduce the effect of speckle noise on the segmentation process and effectively segment the thyroid under the condition that the curve evolution running time and iteration times are obviously reduced.