基于图像局部熵的混合水平集模型甲状旁腺分割  

Hybrid Level Set Model for Parathyroid Gland Segmentation Based on Local Entropy of Images

作　　者：毛林 赵利强 于明安 魏莹 王颖 Mao Lin;Zhao Liqiang;Yu Ming’an;Wei Ying;Wang Ying(College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China;Interventional Ultrasourid Medicine,China-Japan Friendship Hospital,Beijing 100029,China;Institute of Microelectronics,Chinese Academy of Sciences,Beijing 100029,China;University of Chinese Academy of Sciences,Beijing100049,China)

机构地区：[1]北京化工大学信息科学与技术学院 [2]中日友好医院介入超声医学科 [3]中国科学院微电子研究所 [4]中国科学院大学

出　　处：《光学学报》2019年第12期248-256,共9页Acta Optica Sinica

基　　金：国家自然科学基金(61340056);北京化工大学-中日友好医院生物医学转化工程联合基金(PYBZ1804)

摘　　要：针对甲状旁腺超声图像灰度分布不均匀、甲状旁腺病灶多样化的特点,利用图像全局和局部信息,采用基于图像局部熵的混合水平集模型进行甲状旁腺超声图像分割。针对不同超声图像灰度分布差异大的难题,利用图像局部熵确定全局项权重,提高模型的自适应能力。为避免局部项区域尺度设定大易出现过分割,区域尺度设定小计算效率低的问题,利用两尺度进行曲线演化。实验结果表明,本文提出的混合水平集模型对差异性大的甲状旁腺超声图像具有较强的自适应能力,能使演化曲线自动收敛于目标轮廓,具有更高的分割准确率和计算效率。Aiming at the characteristics of the intensity inhomogeneous and diversiform parathyroid lesions in the ultrasound images of the parathyroid gland, we propose a hybrid level set model for parathyroid gland segmentation based on local entropy of images. The proposed model uses both global and local image information. To address the problem of the inhomogeneous intensity distribution in ultrasound images,local entropy of images is used to determine the weight of the global term to improve the model’s adaptivity. In addition, two scales are adopted to prevent over-segmentation and calculation inefficiency on the large and small scales, respectively. Experimental results show that the proposed model can adapt to different ultrasound images of parathyroid gland, which makes the evolution curve converge to the target contour automatically. In addition, this model has high segmentation accuracy and computational efficiency.

关 键 词：医用光学 甲状旁腺超声图像 水平集 图像局部熵 自适应 两尺度 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]