Retinal Health Investigation by Segmentation or Major and Minor Blood Vessels in Fundus Images for Diabetes Patients

被引:1
|
作者
Poonguzhali, S. [1 ]
Chakravarthi, Rekha [1 ]
机构
[1] Sathyabama Inst Sci & Technol, Chennai, Tamil Nadu, India
关键词
Diabetic retinopathy; Extraction and classification; Morphological operation; Prediction of eye health; Retinal disease; FEATURE-EXTRACTION; OPTIC DISK;
D O I
10.7860/JCDR/2019/41221.13207
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Introduction: The Major and Minor blood vessel and the optic disc are the important factors for finding the presence of retinal disease like diabetic retinopathy. Aim: To segment the blood vessel using Major vessel extraction and Minor vessel classification methods. Materials and Methods: The retinal blood vessels are analysed from the images, for determining the changes in the blood vessels depending on the vessel branching pattern, width and density. High pass filter is used for easily segmenting the major vessels and also to make the retinal nerves lighter and the eye background dark to avoid fovea. A morphological operation like the top hat is performed for zooming and shrinking the nerves to adjust to the outer structure of the eye. The Minor vessels are obtained by using the derivative of Gaussian filter without any crack or without any breaks in the structure of the retinal nerves which is used to determine the presence of blood clot in the eye. Gabor Filter is used to strengthening the pixels of the image. Conclusion: The segmented outputs of the Major vessel and Minor vessels are together added to get the resultant image of the eyes. Thus extracted and classified vessels can be sent to an intelligent artificial neural network for comparison with threshold values for diagnosing the problems due to high glucose level, cardiac disorders, etc.
引用
收藏
页数:4
相关论文
共 50 条
  • [21] Particle Swarm Optimization Approach for the Segmentation of Retinal Vessels from Fundus Images
    Khomri, Bilal
    Christodoulidis, Argyrios
    Djerou, Leila
    Babahenini, Mohamed Chaouki
    Cheriet, Farida
    [J]. IMAGE ANALYSIS AND RECOGNITION, ICIAR 2017, 2017, 10317 : 551 - 558
  • [22] Automated analysis of the distributions and geometries of blood vessels on retinal fundus images
    Hatanaka, Y
    Hara, T
    Fujita, H
    Aoyama, M
    Uchida, H
    Yamamoto, T
    [J]. MEDICAL IMAGING 2004: IMAGE PROCESSING, PTS 1-3, 2004, 5370 : 1621 - 1628
  • [23] Blood Vessels Quantification to Detect Glaucoma Using Retinal Fundus Images
    Khan, Fauzia
    Sharif, Sana
    Khan, F. M. Ali
    Ul Haq, Ihtisham
    [J]. ELEVENTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2018), 2019, 11041
  • [24] New algorithm for detecting smaller retinal blood vessels in fundus images
    LeAnder, Robert
    Bidari, Praveen I.
    Mohammed, Tauseef A.
    Das, Moumita
    Umbaugh, Scott E.
    [J]. MEDICAL IMAGING 2010: COMPUTER - AIDED DIAGNOSIS, 2010, 7624
  • [25] Blood vessels segmentation method for retinal fundus images based on adaptive principal curvature and image derivative operators
    Thanh, Dang N.H.
    Sergey, Dvoenko
    Surya Prasath, V.B.
    Hai, Nguyen Hoang
    [J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2019, 42 (2/W12): : 211 - 218
  • [26] Automatic segmentation of blood vessels from retinal fundus images through image processing and data mining techniques
    Geetharamani R.
    Balasubramanian L.
    [J]. Sadhana, 2015, 40 (6) : 1715 - 1736
  • [27] BLOOD VESSELS SEGMENTATION METHOD FOR RETINAL FUNDUS IMAGES BASED ON ADAPTIVE PRINCIPAL CURVATURE AND IMAGE DERIVATIVE OPERATORS
    Thanh, Dang N. H.
    Sergey, Dvoenko
    Prasath, V. B. Surya
    Nguyen Hoang Hai
    [J]. INTERNATIONAL WORKSHOP ON PHOTOGRAMMETRIC AND COMPUTER VISION TECHNIQUES FOR VIDEO SURVEILLANCE, BIOMETRICS AND BIOMEDICINE, 2019, 42-2 (W12): : 211 - 218
  • [28] Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks
    Sule, Olubunmi
    Viriri, Serestina
    [J]. JOURNAL OF DIGITAL IMAGING, 2023, 36 (02) : 414 - 432
  • [29] Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks
    Olubunmi Sule
    Serestina Viriri
    [J]. Journal of Digital Imaging, 2023, 36 : 414 - 432
  • [30] Automatic segmentation of blood vessels from retinal fundus images through image processing and data mining techniques
    Geetharamani, R.
    Balasubramanian, Lakshmi
    [J]. SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2015, 40 (06): : 1715 - 1736