Vessel Extraction on Ocular Fundus Images by Using Gabor Filter Bank

被引:0
|
作者
SU Ming-jian [1 ]
ZHANG Xue-jun [1 ,2 ]
WANG Xi-ming [2 ]
Brent J Liu [2 ]
GAO Xin [3 ]
ZHANG Zuo-jun [4 ]
ZHOU Bin [1 ]
机构
[1] School of Computer,Electronics and Information,Guangxi University
[2] Department of Medical Imaging,Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences
[3] Cancer Hospital of Guangxi Medical University
基金
中国国家自然科学基金;
关键词
Gabor filter bank; vessel extraction; ocular fundus; segmentation;
D O I
10.19583/j.1003-4951.2015.01.005
中图分类号
TP391.41 [];
学科分类号
080203 ;
摘要
Fundus diagnosis is an important part of the whole body examination that may provide rich clinical information to doctors for diagnostic reference. Manual fundus vessel extraction is helpful to quantitative measurement of diseases but obviously it is a tough work for physicians. This paper presents an automatic method by using Gabor filter bank to extract the artery and vein separately in the ocular fundus images. After preprocessing steps that include gray-scale transform, gray value inversion and contrast enhancement, the Gabor filter bank is applied to the extraction of the artery and vein in the ocular fundus images. Finally these two different width types of vessels are selected by post-processing methods such as labeling, corrosion, binarization, etc. Evaluation results show an accurate rate of 90% in vein and 82% in artery from 20 cases, that indicates the effectiveness of our proposed segmentation method.
引用
收藏
页码:28 / 36
页数:9
相关论文
共 50 条
  • [1] Blood Vessel Detection in Fundus Images Using Frangi Filter Technique
    Jothi, Adityan
    Jayaram, Shrinivas
    SMART INNOVATIONS IN COMMUNICATION AND COMPUTATIONAL SCIENCES, VOL 2, 2019, 670 : 49 - 57
  • [2] Vessel Extraction in Retinal Images using Automatic Thresholding and Gabor Wavelet
    Ali, Aziah
    Hussain, Aini
    Zaki, Wan Mimi Diyana Wan
    2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2017, : 365 - 368
  • [3] Automatic Framework for Extraction of Red Lesion Using Gabor Filter from Fundus Image
    Singh, Astha
    Srivastava, Shubhi
    Yadav, Anjali
    Dutta, Malay Kishore
    Travieso, Carlos M.
    PROCEEDINGS OF 2ND INTERNATIONAL CONFERENCE ON APPLICATIONS OF INTELLIGENT SYSTEMS (APPIS 2019), 2019,
  • [4] VESSEL ENHANCEMENT OF LOW QUALITY FUNDUS IMAGE USING MATHEMATICAL MORPHOLOGY AND COMBINATION OF GABOR AND MATCHED FILTER
    Lu, Cheng-Yu
    Jing, Bing-Zhong
    Chan, Patrick P. K.
    Xiang, Daoman
    Xie, Wanhua
    Wang, Jianxun
    Yeung, Daniel S.
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR), 2016, : 168 - 173
  • [5] Blood vessel extraction in fundus images using hessian eigenvalues and adaptive thresholding
    P. V. G. D. Prasad Reddy
    Evolutionary Intelligence, 2021, 14 : 577 - 582
  • [6] Blood vessel extraction in fundus images using hessian eigenvalues and adaptive thresholding
    Prasad Reddy, P. V. G. D.
    EVOLUTIONARY INTELLIGENCE, 2021, 14 (02) : 577 - 582
  • [7] Comparative analysis of retinal fundus images with the distant past images using a vessel extraction technique
    Modegi, Toshio (Modegi-T@mail.dnp.co.jp), 1600, Springer Science and Business Media Deutschland GmbH (45):
  • [8] Comparative Analysis of Retinal Fundus Images with the Distant Past Images Using a Vessel Extraction Technique
    Modegi, Toshio
    Takahashi, Yoichi
    Yokoi, Tae
    Moriyama, Muka
    Shimada, Noriaki
    Morita, Ikuo
    Ohno-Matsui, Kyoko
    INNOVATION IN MEDICINE AND HEALTHCARE 2015, 2016, 45 : 565 - 574
  • [9] Segmentation of Blood Vessel Structures in Retinal Fundus Images with Logarithmic Gabor Filters
    Gross, Sebastian
    Klein, Monika
    Schneider, Dorian
    CURRENT MEDICAL IMAGING, 2013, 9 (02) : 138 - 144
  • [10] Image enhancement of medical images using gabor filter bank on hexagonal sampled grids
    Veni, S.
    Narayanankutty, K.A.
    World Academy of Science, Engineering and Technology, 2010, 65 : 816 - 821