This document describes the design and development of an early optical disease recognition system using fundus imaging on an FPGA. The system aims to extract exudates from fundus images of eyes to detect diabetic retinopathy. It involves image acquisition, color space conversion, segmentation of the optic disc, masking of the optic disc, extraction of exudates, and area calculation of exudates. The system is implemented using Simulink and also allows for content-based image retrieval for comparison of test images to database images. Future work proposed includes better optic disc segmentation, displaying medication information, and developing handheld ophthalmoscopes.

1. DESIGN AND DEVELOPMENT OF AN
EARLY OPTICAL DISEASE
RECOGNITION SYSTEM USING
FUNDUS IMAGING ON FPGA
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2. NEED FOR THE PROJECT
 Individuals with untreated polygenic disorder are twenty five times
more in danger for vision defect than the final population.
 The longer an individual has had polygenic disorder, the higher the
chance of developing diabetic retinopathy.
 But with regular, correct eye care and treatment at the proper time,
the incidence of severe vision loss are often greatly reduced.
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3. PROBLEM STATEMENT
 The fundus image of the healthy eye has only the optic disk in it.
Whereas the fundus image of an infected eye has optic disk along
with spots with the same intensity level as that of the optic disk.
 These spots are called as exudates OR cotton wool spots and are
characteristic of diabetic retinopathy.
 We aim to extract the characteristics (exudates) obtained from the
fundus image of a person’s eye.
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4. Fig.1 Healthy fundus image Fig.2 Infected fundus image
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5. System level block diagram5
Image
Acquisition
Color Space
Conversion
Segmentation
of Optic Disc
Masking of
Optic Disc
Extraction of
exudates
Area
calculation
CBIR

6. IMAGE ACQUISITION6
Fig 3. Fundus image of infected eye

7. COLOR SPACE CONVERSION7
H S V
where

8. COLOR SPACE CONVERSION8
Y Cb Cr
Y= 0.299R + 0.587G + 0.114B
Cb= B - Y
Cr= R - Y

9. SEGMENTATION OF OPTIC DISC9
Fig. 6 Thresholding operation on fundus image using a single color component(S)

10. Fig.7 Segmented optic disc before erosion and dilation
10 SEGMENTATION OF OPTIC DISC

11. Fig 8. Segmented optic disc after erosion and dilation
11 SEGMENTATION OF OPTIC DISC

12. MASKING
Fig 9. RGB Image Fig 10. Segmented optic disc
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13. Fig 11. Image obtained after masking
MASKING13

14. Fig 12. RED Fig 13. GREEN
Fig 14. BLUE
EXTRACTION
OF COLOR
COMPONENTS
FROM MASKED
IMAGE
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15. Fig 15. EXUDATES EXTRACTED FROM MASKED IMAGE
EXUDATES EXTRACTION
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16. IMPLEMENTATION IN SIMULINK
Fig.16 Area of exudates calculated for a single image implemented in Simulink
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17. Fig 17. Output obtained from Simulink
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18. CONTENT BASED IMAGE RETRIEVAL
 Content-based image retrieval (CBIR) is the application of computer
vision techniques to the image retrieval problem, that is, the problem
of searching for digital images in large databases.
 “Content-based" means that the search analyses the contents of the
image rather than the metadata such as keywords, tags, or
descriptions associated with the image.
 CBIR is desirable because searches that rely purely on metadata are
dependent on annotation quality and completeness.
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19. CONTENT BASED IMAGE RETRIEVAL
Fig. 18 A test image with a database image
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20. CONTENT BASED IMAGE RETRIEVAL
Fig.19 Multiport switch and JTAG programming along with MATLAB function block for comparison
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21. FUTURE WORKS
 Better segmentation of optic disc can be achieved.
 Along with the area, the medicine to be prescribed can also be
displayed.
 Handheld ophthalmoscopes which can take the fundus image
without dilation of the pupil.
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22. - VRUSHAK K(1BG11TE062)
VIKRAM(1BG11TE061)
DINESH N SHENOY(1BG11TE015)
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