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一种基于模糊主动轮廓的鲁棒局部分割方法

孙文燕 董恩清 曹祝楼 郑强

孙文燕, 董恩清, 曹祝楼, 郑强. 一种基于模糊主动轮廓的鲁棒局部分割方法. 自动化学报, 2017, 43(4): 611-621. doi: 10.16383/j.aas.2017.c160260
引用本文: 孙文燕, 董恩清, 曹祝楼, 郑强. 一种基于模糊主动轮廓的鲁棒局部分割方法. 自动化学报, 2017, 43(4): 611-621. doi: 10.16383/j.aas.2017.c160260
SUN Wen-Yan, DONG En-Qing, CAO Zhu-Lou, ZHENG Qiang. A Robust Local Segmentation Method Based on Fuzzy-energy Based Active Contour. ACTA AUTOMATICA SINICA, 2017, 43(4): 611-621. doi: 10.16383/j.aas.2017.c160260
Citation: SUN Wen-Yan, DONG En-Qing, CAO Zhu-Lou, ZHENG Qiang. A Robust Local Segmentation Method Based on Fuzzy-energy Based Active Contour. ACTA AUTOMATICA SINICA, 2017, 43(4): 611-621. doi: 10.16383/j.aas.2017.c160260

一种基于模糊主动轮廓的鲁棒局部分割方法

doi: 10.16383/j.aas.2017.c160260
基金项目: 

国家自然科学基金 81371635

国家自然科学基金 81671848

山东省重点研发计划项目 2016GGX101017

教育部高等学校博士学科点专项科研基金 2012013111 0062

详细信息
    作者简介:

    孙文燕   山东大学 (威海) 机电与信息工程学院博士研究生.2005年获得山东大学硕士学位.主要研究方向为医学图像处理.E-mail:sunwenyan80@163.com

    曹祝楼   山东大学 (威海) 数学与统计学院讲师.2015年获得山东大学博士学位.主要研究方向为图像处理.E-mail:zlouc@sdu.edu.cn

    郑强  烟台大学计算机与控制工程学院讲师.2013年获得山东大学博士学位.主要研究方向为医学图像处理.E-mail:zhengqiang@ytu.edu.cn

    通讯作者:

    董恩清  山东大学 (威海) 机电与信息工程学院教授.主要研究方向为无线传感器网络, 医学图像处理.E-mail:enqdong@sdu.edu.cn

A Robust Local Segmentation Method Based on Fuzzy-energy Based Active Contour

Funds: 

National Natural Science Foundation of China 81371635

National Natural Science Foundation of China 81671848

Key Research and Development Project of Shandong Province 2016GGX101017

Research Fund for the Doctoral Program of Higher Education of China 2012013111 0062

More Information
    Author Bio:

      Ph. D. candidate at the School of Mechanical, Electrical and Information Engineering, Shandong University (Weihai). She received her master degree from Shandong University in 2005. Her main research interest is medical image processing

      Lecturer at the School of Mathematics and Statistics, Shandong University (Weihai). He received his Ph. D. degree from Shandong University in 2015. His main research interest is image processing

      Lecturer at the School of Computer and Control Engineering, Yantai University. He received his Ph. D. degree from Shandong University in 2013. His main research interest is medical image processing

    Corresponding author: DONG En-Qing   Professor at the School of Mechanical, Electrical and Information Engineering, Shandong University (Weihai). His research interest covers wireless sensor network and medical image processing. Corresponding author of this paper
  • 摘要: 针对局部分割方法对初始轮廓敏感的问题,本文提出一种基于模糊主动轮廓的鲁棒局部分割方法.该方法利用图像的局部信息,定义一种新的平均模糊能量函数.通过对演化曲线进行形态学膨胀和腐蚀运算构建窄带,并在窄带范围内求解模糊能量函数的最小值来实现局部分割.为防止演化曲线陷入局部极小值,在迭代过程中加入对比度约束判断条件,进一步提高了分割方法对初始轮廓的鲁棒性.对合成图像和医学图像的分割实验结果表明,与已有的几种局部分割方法相比,本文方法在分割精度和鲁棒性等方面都有较大提高.
  • 图  1  窄带构建及邻域示意图

    Fig.  1  The sketch map of narrow band and neighborhood

    图  2  不同$rad$取值时的局部分割结果

    Fig.  2  The local segmentation results with difierent $rad$

    图  3  $rad$ 取值与迭代次数的关系

    Fig.  3  The relationship of $rad$ and iterations number

    图  4  无对比度约束时几种初始轮廓及其分割结果

    Fig.  4  Several initial contours and corresponding segmentation results without contrast constraint

    图  5  图 4 (d) 中伪边缘区域像素点的灰度值、隶属度、均值差及 $\beta$ = 1:8时分割结果

    Fig.  5  The intensity, membership and difierence of pixels near the pseudo edge in Fig. 4 (d) and segmentation result when $\beta$ = 1:8

    图  6  不同 $\beta$ 值对分割结果的影响

    Fig.  6  The influence of difierent $\beta$ on segmentation results

    图  7  合成图像的局部分割结果对比

    Fig.  7  The comparison of local segmentation results on a synthetic image

    图  8  磁共振脑图像尾状核的分割结果对比

    Fig.  8  The comparison of caudate nucleus segmentation results on a MR brain image

    图  9  磁共振脑图像侧脑室前角的分割结果对比

    Fig.  9  The comparison of anterior horn of lateral ventricle segmentation results on a MR brain image

    图  10  不同初始轮廓下对合成图像1的分割结果对比

    Fig.  10  The comparison of segmentation results on synthetic image 1 with difierent initial contours

    图  11  不同初始轮廓下对合成图像2的分割结果对比

    Fig.  11  The comparison of segmentation results on synthetic image 2 with difierent initial contours

    图  12  不同初始轮廓下对磁共振脑图像的肿瘤分割

    Fig.  12  The comparison of tumor segmentation results on a MR brain image with difierent initial contours

    图  13  对弱边缘图像的分割结果对比

    Fig.  13  The comparison of segmentation results on images with weak boundary

    表  1  图 7~9分割结果的$JS $系数比较

    Table  1  Comparison of $JS $ for the segmentation results of Figs. 7~9

    Method LRAC SBGF-RLS BSBO-RLS 本文方法
    Fig. 7 0.7916 0.4823 0.9641 $\boldsymbol{0.9654 } $
    Fig. 8 0.5639 0.0562 0.7120 $\boldsymbol{0.8889} $
    Fig. 9 0.7651 0.0013 0.8505 $\boldsymbol{0.8562} $
    下载: 导出CSV

    表  2  图 7~9分割结果的$DC $系数比较

    Table  2  Comparison of $ DC $ for the segmentation results of Figs. 7~9

    Method LRAC SBGF-RLS BSBO-RLS 本文方法
    Fig. 7 0.8837 0.6508 0.9817 $\boldsymbol{0.9824} $
    Fig. 8 0.7212 0.1065 0.8318 $\boldsymbol{0.9412} $
    Fig. 9 0.8669 0.0027 0.9192 $\boldsymbol{0.9225} $
    下载: 导出CSV
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