高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

一种混合特征高效融合的视网膜血管分割方法

蔡轶珩 高旭蓉 邱长炎 崔益泽

蔡轶珩, 高旭蓉, 邱长炎, 崔益泽. 一种混合特征高效融合的视网膜血管分割方法[J]. 电子与信息学报, 2017, 39(8): 1956-1963. doi: 10.11999/JEIT161290
引用本文: 蔡轶珩, 高旭蓉, 邱长炎, 崔益泽. 一种混合特征高效融合的视网膜血管分割方法[J]. 电子与信息学报, 2017, 39(8): 1956-1963. doi: 10.11999/JEIT161290
CAI Yiheng, GAO Xurong, QIU Changyan, CUI Yize. Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1956-1963. doi: 10.11999/JEIT161290
Citation: CAI Yiheng, GAO Xurong, QIU Changyan, CUI Yize. Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1956-1963. doi: 10.11999/JEIT161290

一种混合特征高效融合的视网膜血管分割方法

doi: 10.11999/JEIT161290
基金项目: 

国家自然科学基金(61201360)

Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion

Funds: 

The National Natural Science Foundation of China (61201360)

  • 摘要: 将机器学习运用到视网膜血管分割当中已成为一种趋势,然而选取什么特征作为血管与非血管的特征仍为众所思考的问题。该文利用将血管像素与非血管像素看作二分类的原理,提出一种混合的5D特征作为血管像素与非血管像素的表达,从而能够简单快速地将视网膜血管从背景中分割开来。其中5D特征向量包括CLAHE (Contrast Limited Adaptive Histgram Equalization),高斯匹配滤波,Hesse矩阵变换,形态学底帽变换,B-COSFIRE(Bar-selective Combination Of Shifted FIlter REsponses),通过将融合特征输入SVM(支持向量机)分类器训练得到所需的模型。通过在DRIVE和STARE数据库进行实验分析,利用Se, Sp, Acc, Ppv, Npv, F1-measure等常规评价指标来检测分割效果,其中平均准确率分别达到0.9573和0.9575,结果显示该融合方法比单独使用B-COSFIRE或者其他目前所提出的融合特征方法更准确有效。
  • 扬琴, 赵景秀. 基于主成分分析的血管分割算法[J]. 电子技术设计与应用, 2016, 3: 66-70.
    YANG Qin and ZHAO Jingxiu. Blood vessel segmentation algorithm based on principal components analysis[J]. Electronics Design Application, 2016, 45(3): 66-70.
    SINGH N P and SRIVASTAVA R. Retinal blood vessels segmentation by using Gumbel probability distributiaon function based matched filter[J]. Comput Methods Progr Biomed, 2016, 129(C): 40-50. doi: 10.1016/j.cmpb.2016.03. 001.
    FRAZ M M, BASIT A, and BARMAN S A. Application of morphological bit planes in retinal blood vessel extration[J]. Digit Imaging. 2013, 26(2): 274-286. doi: 10.1007/s10278-012- 9513-3.
    FRAZ M M, REMAGNINO P, HOPPE A, et al. Blood vessel segmentation methodologies in retinalimages-A survey[J]. Computer Methods and Programs Biomedicine, 2012, 108(1): 407-433. doi: 10.1016/j.cmpb.2012.03009.
    吴奎, 蔡冬梅, 贾鹏, 等. 基于2D Gabor小波与组合线检测算子的视网膜血管分割[J]. 科学技术与工程, 2016, 16(12): 106-112.
    WU Kui, CAI Dongmei, JIA Peng, et al. Retinal vessel segmentation based on 2D Gabor wavelet and combined line operators[J]. Science Technology and Engineering, 2016, 16(12): 106-112.
    于辉, 王小鹏. 基于HESSIAN增强和形态学尺度空间的视网膜血管分割[J]. 计算机应用与软件, 2016, 33(8): 200-205.
    YU Hui and WANG Xiaopeng. Retinal vessels segmentation based on Hessian enhancement and morphological scale space[J]. Computer Applications and Software, 2016, 33(8): 200-205.
    WAHEED Z, AKRAM M U, WAHEED A, et al. Person identification using vascular and non-vascular retinal feature[J]. Computers and Electrical Engineering, 2016, 53: 359-371. doi: 10.1016/j.compeleceng.2016.03.010.
    RICCI E and PERFETTI R. Retinal blood vessel segmentation using line operators and support vector classification[J]. IEEE Transactions on Medical Imaging, 2007, 26(10): 1357-1365. doi: 10.1109/TMI.2007.8985551.
    FRAZ M M, REMAGNINO P, HOPPE A, et al. An ensemble classification-based approach applied to retinal blood vessel segmentation[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(9): 2538-2548. doi: 10.1109/TBME. 2012.2205687.
    AZZOPARDI G, STRISCIUGLIO N, VENTO M, et al. Trainable COSFIRE filters for vessel delineation with application to retinal images[J]. Medical Image Analysis, 2015, 19(1): 46-57. doi: 10.1016/j.media.2014.08.002.
    STRISCIUGLIO N, AZZOPARDI G, VENTO M, et al. Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters[J]. Machine Vision and Applications, 2016, 27(8): 1137-1149. doi: 10.1007 //s00138-016-0781-7.
    ASLANI S and SARNEL H. A new supervised retinal vessel segmentation method based on robust hybrid feature[J]. Biomedical Singal Processing and Control, 2016, 30: 1-12. doi: 10.1016/j.bspc.2016.05006.
  • 加载中
计量
  • 文章访问数:  1342
  • HTML全文浏览量:  220
  • PDF下载量:  351
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-11-28
  • 修回日期:  2017-04-14
  • 刊出日期:  2017-08-19

目录

    /

    返回文章
    返回