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    Identification of Diabetic Retinopathy Using The Retinal Images

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    Ramitha Devi
    This document describes a study aiming to identify diabetic retinopathy from retinal images efficiently by detecting exudates, a key feature of the disease. The researchers used k-means clustering to segment exudates and a naive bayes classifier to identify exudates versus non-exudates. They preprocessed images, extracted texture and color features from segmented regions, and classified pixels with over 98% success at detecting exudates. The methodology aims to reduce processing time compared to prior work while maintaining high accuracy in automatic exudate detection from retinal images for diabetic retinopathy screening.

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    Identification of Diabetic Retinopathy Using The Retinal Images

    Uploaded by

    Ramitha Devi
    36 views23 pages
    This document describes a study aiming to identify diabetic retinopathy from retinal images efficiently by detecting exudates, a key feature of the disease. The researchers used k-means clustering to segment exudates and a naive bayes classifier to identify exudates versus non-exudates. They preprocessed images, extracted texture and color features from segmented regions, and classified pixels with over 98% success at detecting exudates. The methodology aims to reduce processing time compared to prior work while maintaining high accuracy in automatic exudate detection from retinal images for diabetic retinopathy screening.

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    This document describes a study aiming to identify diabetic retinopathy from retinal images efficiently by detecting exudates, a key feature of the disease. The researchers used k-means clustering to segment exudates and a naive bayes classifier to identify exudates versus non-exudates. They preprocessed images, extracted texture and color features from segmented regions, and classified pixels with over 98% success at detecting exudates. The methodology aims to reduce processing time compared to prior work while maintaining high accuracy in automatic exudate detection from retinal images for diabetic retinopathy screening.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    36 views23 pages

    Identification of Diabetic Retinopathy Using The Retinal Images

    Uploaded by

    Ramitha Devi
    This document describes a study aiming to identify diabetic retinopathy from retinal images efficiently by detecting exudates, a key feature of the disease. The researchers used k-means clustering to segment exudates and a naive bayes classifier to identify exudates versus non-exudates. They preprocessed images, extracted texture and color features from segmented regions, and classified pixels with over 98% success at detecting exudates. The methodology aims to reduce processing time compared to prior work while maintaining high accuracy in automatic exudate detection from retinal images for diabetic retinopathy screening.

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    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 23

    IDENTIFICATION OF DIABETIC

    RETINOPATHY USING THE RETINAL


    IMAGES

    Department of ECE

    -by
    M.Ayisha sithika
    M.Basima banu
    K.Gayathri
    Batch number :111
    Under the supervision of
    Miss
    L.C.MEENA(Asst.PROFESSOR) , 1
    AIM
     To identify diabetic retinopathy using the retinal images in an
    efficient manner.
     Exudates is one of the features used to identify the
    diabetic retinopathy .

    OBJECTIVE

     Exudates ,a very important and mostly occurring feature


    of retinopathy is identified using k-means and naives bayes
    classifier.

    2
    INTRODUCTION-EYE
    INTRODUCTION-DIABETIC
    RETINOPAHTY
    DIABETIC RETINOPATHY
    • DR is an eye disease which has been caused due to high blood
    sugar level.

    TYPES OF DR

    1) Non-proliferative diabetic retinopathy


    2) Diabetic maculopathy
    3) Proliferative diabetic retinopathy
    Normal Defective
    EXUDATES

    • Primary sign of diabetic retinopathy


    • It is lipids and proteins leaks from damaged
    blood vessels.

    FUNDUS IMAGE OF EYE

    • FUNDUS OF EYE: The back portion of the interior of


    the eyeball, visible through the pupil by use of the
    ophthalmoscope.

    • FUNDUS IMAGE: Fundus photography is performed by a


    fundus camera, which basically consists of a specialized
    low power microscope with an attached camera.
    LITREATURE SURVEY
    1.Akara Sopharak , Bunyarit Uyyanonvara and Sarah Barman[6],
    “Automatic Exudate Detection from Non-dilated Diabetic
    Retinopathy-Retinal Images Using Fuzzy C-means Clustering”.

    ADVANTAGES:
     The low contrast retinal image- intensity increased
    and a
    number of edge pixels were extracted.

    DISADVANTAGES:
     More time consuming.
    —
    7
    2.T. Walter, J. Klein, P. Massin, and A. Erginary[2],“A
    contribution of image processing to the diagnosis of
    diabetic retinopathy thy,detection of exudates in colour
    fundus images of the human retina".

    ADVANTAGES:
     Time consumption reduced as it uses mathematical
    is morphology
    techniques .
    DISADVANTAGES:
     The paper ignored some types of errors on the border
    of the segmented exudates in their reported performances.
     Time consumption is reduced but not to great extent. 8
    NOVELTY USED

    • In our project we are using k-means clustering algorithm with


    naive bayes classifier.

    • Fuzzy c-means algorithm, as consumes time, so k-means


    is used to reduce time.

    • Naive bayes,a type of classifier is used to increase


    the accuracy and sensitivity of the detection.

    9
    WORK ACCOMPLISHED

    BLOCK DIAGRAM:

    INPUT
    PRE- FEATURE
    RETINAL SEGMENTATION
    PROCESSING EXTRACTION
    IMAGE

    EXUDATES

    CLASSIFICATION

    NON-
    EXUDATES
    STEP 1:PRE-PROCESSING

    RGB to HIS
    image

    Median
    filtering

    CLAHE

    HSI to RGB
    image
    STEP 2:IMAGE SEGMENTATION

    RGB to a*b alone


    l*a*b colour using k-means five clusters
    space clustering

    Colour
    optic disc is labels every
    segmented
    localized pixel
    images
    STEP 3:FEATURE EXTRACTION

    • On the basis of colour and texture


    orientation, features are extracted using
    GLCM.

    STEP 4: CLASSIFICATION

    • The final step is classification of given input as


    exudates (or) non-exudates by naive bayes
    classifier.
    PRE-PROCESSED OUTPUT

    Input retinal image

    HSI Components

    H component S Component I Component


    Filtered I component CLAHE image Pre-processed image
    SEGMENTATION OUTPUT
    LAB colour space images

    a. L channel b. A channel c. B channel


    Image Labeled By Cluster Index
    CLUSTER FORMATION

    cluster1 cluster2 cluster3

    cluster4 cluster5

    Cluster Output
    EXECUTION OF FINAL OUTPUT
    CONCLUSION
    • The selected features clustered by k-means clustering and
    classified into exudates and non –exudates using naive
    bayes classifier.
    • Using this approach, the exudates are detected with 98%
    success rate.

    FUTURE WORK

    • Detection of Micro-aneurysm and also maculopathy be


    predicted and performance can be compared.
    REFERENCES
    1 Wynne Hsu, P M D S Pallawala, Mong Li Lee, KahGuan Au Eong(2001),”The Role
    of Domain Knowledge in the Detection of Retinal Hard Exudates”, IEEE
    Conference on Computer Vision and Pattern Recognition (CVPR), Kauai Marriott,
    Hawaii, vol.12,pp. 533-548.

    2 T. Walter, J.Klein, P.Massin and A.Erginary(2002), “A Contribution of image


    processing to the diagnosis of Diabetic Retinopathy detection of exudates in color
    fundus images of the human retina”, IEEE Trans. On Med. images, vol. 21, no.
    10,
    pp. 1236-1243.

    3 Pizer. S.M(2003),“The Medical Image Display and analysis group at the university
    of North Carolina:Reminiscences and philosophy ”, IEEE Trans On Medical
    Imaging, vol. 22, no. 1, pp. 2-10.

    4 Fleming. AD, Philips. S, Goatman. KA, Williams. GJ, Olson.JA, sharp.


    PF(2007),“Automated detection of exudates for Diabetic Retinopathy Screening”,
    Journal of Phys. Med. Bio., vol. 52, no. 24, pp. 7385-7396.

    5 Alireza Osareh, Bita Shadgar, and Richard Markham(2009), “A Computational-


    Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy
    Images”,IEEE Transactions on Information Technology in Biomedicine,vol. 13,
    no. 4,pp.535-545.International Diabetic Federation (IDF), 2009a, Latest diabetes
    • [6] Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman(2009), “Automatic Exudate
    Detection from Non-dilated Diabetic Retinopathy retinal images using Fuzzy Cleans
    Clustering” Journal of Sensors, vol.9, No. 3, pp 2148- 2161.

    • [7] Saiprasad Ravishankar, Arpit Jain, Anurag Mittal(2009),“Automated feature extraction for
    early detection of Diabetic Retinopathy in fundus images”,IEEE Conference on Computer
    vision and pattern Recognition, pp. 210-217.

    • [8] Doaa Youssef, Nahed Solouma, Amr El-dib, Mai Mabrouk(2010),“New Feature-Based
    Detection of Blood Vessels and Exudates in Color Fundus Images” IEEE conference on Image
    Processing Theory, Tools and Applications, vol.16,pp.294-299

    • [9] Guoliang Fang, Nan Yang, Huchuan Lu and Kaisong Li(2010),“Automatic


    Segmentation of Hard Exudates in fundus images based on Boosted Soft Segmentation”,
    International Conference on Intelligent Control and Information Processing, vol.13,pp. 633-
    638.

    • [10] Plissiti.M.E., Nikar.C, Charchanti.A(2011),“Automated detection of cell nuclei in


    pap smear images using morphological reconstruction and clustering” IEEE Trans. On
    Insformation Technology in Biomedicine, vol. 2, pp. 233-241.
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