1. Fundus Image Mosaic Generation for
Large Field of View
Daniyal Usmani, Tanveer A, M. U. Akram, Abdullah Danyal Saeed
BIOMISA Research Group
National University of Sciences & Technology
Pakistan

2. Our Webpage (www.biomisa.org)

3. Introduction

4. Fundus Images
• Fundus Images – Used for the manual and
autonomous detection of eye diseases.
• Bottleneck – Analysis of multiple fundus images

5. Significance of Fundus Image
Stitching

6. • High Resolution Image
• Easy Analysis
• Less probability of Error
• Robust implementation of autonomous
disease detection algorithms

7. Existing Methods
• Pixel based Image Stitching
• Image Stitching using SIFT features
• Time Complexity?
• Accuracy?

8. Methodology

9. To implement a method which stitches
the images more accurately

11. Preprocessing
• A square Gaussian filter of 5 x 5 is applied with
the variance of 0.8 to remove the noise on the
edge of the fundus images

12. Feature Extraction
• Features of the fundus images for particular
patient are extracted

13. Calculating Similarity
• RANSAC algorithm is used to find similarity
between each image
• All possible Inliers are calculated

14. • Combination with maximum Inliers is selected

15. Blending
• The image is first scaled and rotated
accordingly to match the plane of other image

16. • Image Merged

17. Data & Results

18. Data
• One Local available database is used
– 8 Patients (3 Images each)
– 2 Patients(4 Images each)

19. Results

20. Results
• The Results were compared with the SIFT and
SURF features.
From left to Right the feature used are ASIFT, SURF and SIFT

21. Conclusion & Future Work
• A simple yet robust & practically implemented
method
• Helpful in real time analysis
• Accuracy can be further improved by further
preprocessing of the image and adjusting the
light intensity while taking the image

22. Thank you