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基于多域先验的乳腺超声图像协同分割

邵昊阳 张英涛 鲜敏 李致勋 唐降龙

邵昊阳, 张英涛, 鲜敏, 李致勋, 唐降龙. 基于多域先验的乳腺超声图像协同分割. 自动化学报, 2016, 42(4): 580-592. doi: 10.16383/j.aas.2016.c150199
引用本文: 邵昊阳, 张英涛, 鲜敏, 李致勋, 唐降龙. 基于多域先验的乳腺超声图像协同分割. 自动化学报, 2016, 42(4): 580-592. doi: 10.16383/j.aas.2016.c150199
SHAO Hao-Yang, ZHANG Ying-Tao, XIAN Min, LI Zhi-Xun, TANG Xiang-Long. Breast Ultrasound Image Co-segmentation by Means of Multiple-domain Knowledge. ACTA AUTOMATICA SINICA, 2016, 42(4): 580-592. doi: 10.16383/j.aas.2016.c150199
Citation: SHAO Hao-Yang, ZHANG Ying-Tao, XIAN Min, LI Zhi-Xun, TANG Xiang-Long. Breast Ultrasound Image Co-segmentation by Means of Multiple-domain Knowledge. ACTA AUTOMATICA SINICA, 2016, 42(4): 580-592. doi: 10.16383/j.aas.2016.c150199

基于多域先验的乳腺超声图像协同分割

doi: 10.16383/j.aas.2016.c150199
基金项目: 

国家自然科学基金 61370162

详细信息
    作者简介:

    邵昊阳, 哈尔滨工业大学计算机科学与技术学院硕士研究生.主要研究方向为医学图像处理和模式识别.E-mail:541998476@qq.com

    鲜敏, 哈尔滨工业大学计算机科学与技术学院和美国犹他州立大学计算机科学系博士研究生.主要研究方向为图像处理, 模式识别, 人工智能.E-mail:min.xian@aggiemail.usu.ed

    李致勋, 南昌大学信息工程学院讲师, 哈尔滨工业大学计算机科学与技术学院博士研究生.主要研究方向为医学图像处理与模式识别.E-mail:zhixun.li@163.com

    唐降龙, 哈尔滨工业大学计算机科学与技术学院教授.主要研究方向为人工智能与信息处理.E-mail:tangxl@hit.edu.cn

    通讯作者:

    张英涛, 哈尔滨工业大学计算机科学与技术学院副教授.主要研究方向为模式识别与医学图像处理.本文通信作者.E-mail:yingtao@hit.edu.cn

Breast Ultrasound Image Co-segmentation by Means of Multiple-domain Knowledge

Funds: 

National Natural Science Foundation of China 61370162

More Information
    Author Bio:

    Master student at the School of Computer Science and Technology, Harbin Institute of Technology. His research interest covers medical imaging and pattern recognition

    Ph. D. candidate at the School of Computer Science and Technology, Harbin Institute of Technology and the Department of Computer Science, Utah State University. His research interest covers medical imaging, pattern recognition, and artificial intelligence

    Lecturer at the School of Information Engineering, Nanchang University and Ph. D. candidate at the School of Computer Science and Technology, Harbin Institute of Technology. His research interest covers medical image processing and pattern recognition

    Professor at the School of Computer Science and Technology, Harbin Institute of Technology. His research interest covers artificial intelligence and information processing

    Corresponding author: ZHANG Ying-Tao Associate professor at the School of Computer Science and technology, Harbin Institute of Technology. Her research interest covers pattern recognition and medical imaging. Corresponding author of this paper
  • 摘要: 乳腺超声(Breast ultrasound, BUS)图像具有较低的信噪比、 较低的对比度以及较模糊的边缘, 其分割是一项富有挑战性的工作. 本文提出了一种多域协同分割模型, 该模型通过结合空域与频域先验, 并引入协同分割的思想来实现对乳腺超声序列的分割. 模型在空域中得到肿瘤的姿态、 位置和强度信息, 在频域中通过使用相位一致性与零交叉检测得到肿瘤的边缘信息, 最后利用协同分割的思想构建起全局能量项, 有效地利用了图像序列信息.实验结果表明, 与传统的乳腺超声图像分割方法相比, 本文提出的分割模型能够很好地处理低对比度低回声图像以及单帧分割模型不能有效分割的图像, 分割结果具有更高的准确性.
  • 图  1  多域协同分割模型

    Fig.  1  Co-segmentation model based on multiple-domain

    图  2  本文提出算法的部分中间结果

    Fig.  2  Part of intermediate results of our proposed method

    图  3  使用不同参数¸ 时的分割结果

    Fig.  3  Segmentation results corresponding to di®erent ¸

    图  4  4 含较多与肿瘤区域相似的正常组织的超声序列分割结果

    Fig.  4  Segmentation results of normal breast ultrasound sequence similar to tumor regions

    图  5  低对比度、边界模糊的超声序列的分割结果

    Fig.  5  Segmentation results of breast ultrasound sequence with low contrast and blurry boundaries

    图  6  单帧不易分割的超声序列的分割结果

    Fig.  6  Segmentation results of breast ultrasound sequence hard to segment for single frame

    图  7  单帧不易分割的超声序列的分割结果

    Fig.  7  Segmentation results of breast ultrasound sequence hard to segment for single frame

    图  8  4类方法的ROC 曲线

    Fig.  8  The curves of ROC of four methods

    表  1  4类方法在所有乳腺超声图像上的整体分割结果

    Table  1  Segmentation results of four methods on all breast ultrasound images

    面积误差评价标准(%)边界误差评价标准(%)
    TPRFPRSIRAHDAMD
    文献[28]的模型55.2962.8342.46148.5555.31
    文献[29]的模型53.1439.8943.7899.9839.65
    文献[4]的模型82.6121.7571.7752.9416.63
    本文模型82.5817.5473.6342.4113.34
    下载: 导出CSV

    表  2  4 类分割方法的AUC 值

    Table  2  Results of AUC of four segmentation methods

    文献[28]的模型文献[29]的模型文献[4]的模型本文模型
    AUC0.4520.5950.8510.872
    下载: 导出CSV
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  • 收稿日期:  2015-04-20
  • 录用日期:  2015-11-17
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