[1]王孝义,邢素霞,王瑜,等.基于自适应能量偏移场无边缘主动轮廓模型的乳腺肿块分割与分类方法研究[J].中国医学物理学杂志,2020,37(8):1010-1016.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.014]
　WANG Xiaoyi,XING Suxia,WANG Yu,et al.Breast mass image segmentation and classification based on adaptive energy offset field-CV[J].Chinese Journal of Medical Physics,2020,37(8):1010-1016.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.014]
点击复制

基于自适应能量偏移场无边缘主动轮廓模型的乳腺肿块分割与分类方法研究()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第8期
页码:
1010-1016
栏目:
医学影像物理
出版日期:
2020-08-27

文章信息/Info

Title:
Breast mass image segmentation and classification based on adaptive energy offset field-CV
文章编号:
1005-202X（2020）08-1010-07
作者:
王孝义; 邢素霞; 王瑜; 曹宇; 申楠; 潘子妍
北京工商大学计算机与信息工程学院， 北京 100048
Author(s):
WANG Xiaoyi;  XING Suxia;  WANG Yu;  CAO Yu;  SHEN Nan;  PAN Ziyan
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
关键词:
乳腺肿块; 图像分割; 能量偏移场; CV模型; 支持向量机
Keywords:
Keywords: breast mass image segmentation energy offset field CV model support vector machine
分类号:
R318；TP301.6
DOI:
DOI:10.3969/j.issn.1005-202X.2020.08.014
文献标志码:
A
摘要:
目的：为提高乳腺癌检测的精准度和效率，提出了一种基于自适应能量偏移场无边缘主动轮廓模型（AEOF-CV）的乳腺肿块分割与分类方法。方法：首先采用中值滤波、阈值分割及区域连通进行图像预处理，去除图像噪声；然后使用伽马变换及形态学运算相结合的方法进行图像增强；其次，采用AEOF-CV对弱对比度图像提高分割精度，用于乳腺肿块分割，得到感兴趣区域；最后使用不同提取特征方法，结合支持向量机识别感兴趣区域是否有肿块，并对存在肿块的图像判别肿块的良、恶性。结果：实验利用DDSM数据库中350个图像进行测试，实验结果证明，基于AEOF-CV乳腺肿块分割方法可以得到肿块清晰外部轮廓，具有较好的鲁棒性，误分率可达到0.212 0。无肿块样本识别率达到94.57%，恶性肿块识别率为97.91%，良性肿块识别率为96.96%，总识别率达94.00%。结论：基于AEOF-CV的乳腺肿块分割效果较好，误分率相对CV方法降低19.17%，查准率和查全率达到了0.851 9和0.836 5，全局分析性能较好，是乳腺肿块分割的有效方法，可为后续模式识别提供可靠依据。
Abstract:
Abstract: Objective To propose a method based on adaptive energy offset field-CV (AEOF-CV) for breast mass image segmentation and classification, thereby improving the accuracy and efficiency of breast cancer detection. Methods Firstly, median filtering, threshold segmentation and regional connectivity were used for image preprocessing to remove image noise. Then the image was enhanced by combining gamma transformation and morphological operation. Subsequently, AEOF-CV was used to improve the accuracy of low-contrast image segmentation for realizing breast mass image segmentation and obtaining the regions of interest. Finally, different feature extraction methods were combined with support vector machine for identifying whether there was a mass in the regions of interest and whether the mass was benign or malignant. Results A total of 350 images in DDSM database were tested. The experimental results showed that breast mass image segmentation based on AEOF-CV could obtain a clear external contour of the mass, with good robustness, and the misclassification rate was 0.212 0. The recognition rate for non-mass samples was 94.57%, and the recognition rates for malignant masses and benign masses were 97.91% and 96.96%, respectively. The average recognition rate of the proposed method reached 94.00%. Conclusion Breast mass image segmentation based on AEOF-CV has a good performance, with the misclassification rate reduced by 19.17% as compared with CV method, and the precision and recall rates are up to 0.851 9 and 0.836 5. The proposed method which has a good global analysis performance is an effective method for breast mass image segmentation and can provide a reliable basis for subsequent pattern recognition. Keywords: breast mass image segmentation energy offset field CV model support vector machine

相似文献/References:

[1]陈海斌,甄 鑫,周凌宏.基于先验的随机游走算法在医学图像分割中的应用[J].中国医学物理学杂志,2015,32(02):174.[doi:10.3969/j.issn.1005-202X.2015.02.005]
[2]许红玉,蔡坦坦,叶良凯,等.分水岭算法在CT图像分割中的应用[J].中国医学物理学杂志,2014,31(06):5272.[doi:10.3969/j.issn.1005-202X.2014.06.012]
[3]周露,张书旭,余辉,等.PET-CT图像配准的预处理研究[J].中国医学物理学杂志,2013,30(05):4392.[doi:10.3969/j.issn.1005-202X.2013.05.012]
[4]赖胜圣,刘虔铖,张刚平.基于模糊C均值自动随机游走算法在脑肿瘤分割中的应用[J].中国医学物理学杂志,2015,32(05):707.[doi:doi:10.3969/j.issn.1005-202X.2015.05.021]
[5]刘国才,胡泽田,朱苏雨,等.头颈部肿瘤PET与MRI融合放疗靶区自适应区域生长勾画[J].中国医学物理学杂志,2016,33(3):222.[doi:10.3969/j.issn.1005-202X.2016.03.002]
　[J].Chinese Journal of Medical Physics,2016,33(8):222.[doi:10.3969/j.issn.1005-202X.2016.03.002]
[6]贾高杰,邱崧,蔡茗名,等.三维点云重构和体显示在医学辅助诊断中的应用[J].中国医学物理学杂志,2016,33(6):593.[doi:10.3969/j.issn.1005-202X.2016.06.012]
　[J].Chinese Journal of Medical Physics,2016,33(8):593.[doi:10.3969/j.issn.1005-202X.2016.06.012]
[7]张泽凡,张东.基于交叉熵和GVF-Snake的子宫肌瘤高强度聚焦超声图像自动分割算法[J].中国医学物理学杂志,2016,33(8):776.[doi:10.3969/j.issn.1005-202X.2016.08.005]
　[J].Chinese Journal of Medical Physics,2016,33(8):776.[doi:10.3969/j.issn.1005-202X.2016.08.005]
[8]胡立伟,白凯,钟玉敏,等. 基于不同图像分割法构建3D打印右室双出口模型[J].中国医学物理学杂志,2016,33(12):1272.[doi:10.3969/j.issn.1005-202X.2016.12.020]
　[J].Chinese Journal of Medical Physics,2016,33(8):1272.[doi:10.3969/j.issn.1005-202X.2016.12.020]
[9]孟爽,王辉,谢蓄芬,等.超像素有偏观测模糊聚类的乳腺超声图像分割[J].中国医学物理学杂志,2017,34(7):693.[doi:10.3969/j.issn.1005-202X.2017.07.009]
　[J].Chinese Journal of Medical Physics,2017,34(8):693.[doi:10.3969/j.issn.1005-202X.2017.07.009]
[10]张玉芳,关天民,刘光孟,等. 基于CT数据的医学图像处理系统设计[J].中国医学物理学杂志,2019,36(9):1055.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.012]
　ZHANG Yufang,GUAN Tianmin,LIU Guangmeng,et al. Design of medical image processing system based on CT data[J].Chinese Journal of Medical Physics,2019,36(8):1055.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.012]
[11]梁楠,赵政辉,周依,等.基于滑动块的深度卷积神经网络乳腺X线摄影图像肿块分割算法[J].中国医学物理学杂志,2020,37(12):1513.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.008]
　LIANG Nan,ZHAO Zhenghui,et al.An algorithm of mass segmentation in mammogram by using deep convolutional neural network based on sliding patch[J].Chinese Journal of Medical Physics,2020,37(8):1513.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.008]

备注/Memo

备注/Memo:
【收稿日期】2020-01-15 【基金项目】国家自然科学基金（61671028, 61473009）；北京市教委科研计划面上项目（KM201510011010） 【作者简介】王孝义，硕士研究生，研究方向：医学图像处理，E-mail: 1181371672@qq.com 【通信作者】邢素霞，副教授，博士，研究方向：图像处理与嵌入式系统开发，E-mail: xingsuxia@163.com
更新日期/Last Update: 2020-08-27