| 基于改进DRLSE模型的甲状腺3D超声图像自动分割 |
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| 引用本文： | 冉冬梅,严加勇,崔崤峣,于振坤.基于改进DRLSE模型的甲状腺3D超声图像自动分割[J].软件,2019(4):61-66. |
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| 作者姓名： | 冉冬梅 严加勇 崔崤峣 于振坤 |
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| 作者单位： | 1.上海理工大学医疗器械与食品学院;2.上海健康医学院医疗器械学院;3.中科院苏州生物医学工程技术研究所;4.南京同仁医院 |
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| 基金项目： | "江苏省省级重点研发专项资金项目"资助(BE2017601);上海市浦东新区科技发展基金民生科研专项医疗卫生项目(PKJ2017-Y41) |
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| 摘 要： | 从超声图像中准确分割甲状腺区域是甲状腺疾病手术计划的关键之一。本文一方面,针对甲状腺超声3D图像,提出利用边缘指示函数和面积项系数改进的距离正则化水平集演化(Distance Regularized Level Set Evolution,DRLSE)模型来实现甲状腺区域的有效分割;另一方面,根据3D超声图像相邻帧之间甲状腺变化较小的特点,通过计算已分割图像的质心,作为相邻帧图像分割初始点来实现3D图像的自动分割。实验表明,采用本文改进DRLSE模型分割甲状腺3D超声图像,平均分割精度可以达到90%以上。
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| 关 键 词： | 甲状腺三维超声图像 图像分割 DRLSE模型 边缘指示函数 |
Automatic Segmentation of Thyroid 3D Ultrasound Images Based on An Improved DRLSE Model |
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| RAN Dong-mei,YAN Jia-yong,CUI Xiao-yao,YU Zhen-kun.Automatic Segmentation of Thyroid 3D Ultrasound Images Based on An Improved DRLSE Model[J].Software,2019(4):61-66. |
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| Authors: | RAN Dong-mei YAN Jia-yong CUI Xiao-yao YU Zhen-kun |
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| Affiliation: | (University of Shanghai for Science and Technology, Shanghai 200093, China;School of Medical Instrument, ShanghaiUniversity of Medical &Health Science, Shanghai 201318, China;Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences, Suzhou 215163;Tongren Hospital of Nanjing, Nanjing 211102) |
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| Abstract: | Accurate segmentation of the thyroid region from ultrasound images is one of the keys to a surgical plan for thyroid disease. On the one hand, this paper proposes utilizing an improved edge indicator function and the area regular coefficient to improve the Distance Regularized Level Set Evolution (DRLSE) model to achieve effective segmentation of the thyroid region. On the other hand, according to the characteristics of small thyroid change between adjacent frames of the three-dimensional ultrasound image, the centroid of the segmented image is used as the initial point for the adjacent frame image. Thus the segmentation of the three-dimensional image is performed automatically. Experiments show that the improved DRLSE model can segment the thyroid three-dimensional ultrasound image effectively, and the average segmentation accuracy is about 90%. |
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| Keywords: | Thyroid three-dimensional ultrasound image Edge indication function Image segmentation DRLSE model |
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