Detection of Hard Exudates in Retinopathy Images

被引:4
|
作者
Verma, Satya Bhushan [1 ]
Yadav, Abhay Kumar [1 ]
机构
[1] Cent Univ, BBA Univ, Dept Comp Sci, Lucknow, Uttar Pradesh, India
关键词
Hard Exudates; Retinopath; Fundus Image; Cotton Wool Spot; Retina;
D O I
10.14201/ADCAIJ2019844148
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The tissue layer located at the back of the eye is known as retina which converts the incoming light into nerve signals and those signals are sent to the brain for understanding. The damage onto the retina is termed as retinopathy and that may lead to vision weakening or vision loss. The hard exudates are small white or yellowish white deposits with their edges being clear and sharp. In the proposed methods we take color image of retina then extract the green channel of that image then apply top hat transformation and bottom hat transformation on that image. The DIARETDB1 and High-Resolution Fundus (HRF) databases are used for performance evaluation of the proposed method. The proposed technique achieves accuracy 97%, sensitivity 95%, and specificity 96% and it takes average 5.6135 second for detection of hard exudates in an image.
引用
收藏
页码:41 / 48
页数:8
相关论文
共 50 条
  • [31] Detection of Hard Exudates in Retinal Images Using a Radial Basis Function Classifier
    Garcia, Maria
    Sanchez, Clara I.
    Poza, Jesus
    Lopez, Maria I.
    Hornero, Roberto
    ANNALS OF BIOMEDICAL ENGINEERING, 2009, 37 (07) : 1448 - 1463
  • [32] Exudates in Detection and Classification of Diabetic Retinopathy
    Vanithamani, R.
    Christina, R. Renee
    PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2016), 2018, 614 : 252 - 261
  • [33] Segmentation and Detection of Diabetic Retinopathy Exudates
    Elbalaoui, Abderrahmane
    Boutaounte, Mehdi
    Faouzi, Hassan
    Fakir, Mohamed
    Merbouha, Abdelkrim
    2014 INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS (ICMCS), 2014, : 171 - 178
  • [34] A SEQUENTIAL LEARNING METHOD FOR DETECTION AND CLASSIFICATION OF EXUDATES IN RETINAL IMAGES TO ASSESS DIABETIC RETINOPATHY
    Ponnibala, M.
    Vijayachitra, S.
    JOURNAL OF BIOLOGICAL SYSTEMS, 2014, 22 (03) : 413 - 428
  • [35] Morphology-Based Exudates Detection from Color Fundus Images in Diabetic Retinopathy
    Akter, Morium
    Uddin, Mohammad Shorif
    Khan, Mahmudul Hasan
    2014 1ST INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATION & COMMUNICATION TECHNOLOGY (ICEEICT 2014), 2014,
  • [36] Identification of hard exudates in retinal images.
    Dhiravidachelvi, E.
    Rajamani, V
    Janakiraman, P. A.
    BIOMEDICAL RESEARCH-INDIA, 2017, 28 : S336 - S343
  • [37] Hybrid Approach for Detection of Hard Exudates
    Kekre, H. B.
    Sarode, Tanuja K.
    Parkar, Tarannum
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2013, 4 (03) : 250 - 255
  • [38] Automatic Detection of Hard Exudates Shadow Region within Retinal Layers of OCT Images
    Singh, Maninder
    Gupta, Vishal
    Singh, Pramod Kumar
    Gupta, Rajeev
    Kumar, Basant
    Alenezi, Fayadh
    Alhudhaif, Adi
    Althubiti, Sara A.
    Polat, Kemal
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [39] Comparison of Logistic Regression and Neural Network Classifiers in the Detection of Hard Exudates in Retinal Images
    Garcia, Maria
    Valverde, Carmen
    Lopez, Maria I.
    Poza, Jesus
    Hornero, Roberto
    2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2013, : 5891 - 5894
  • [40] Automatic Detection of Hard Exudates Shadow Region within Retinal Layers of OCT Images
    Singh, Maninder
    Gupta, Vishal
    Singh, Pramod Kumar
    Gupta, Rajeev
    Kumar, Basant
    Alenezi, Fayadh
    Alhudhaif, Adi
    Althubiti, Sara A.
    Polat, Kemal
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022