1. Bashir Dodo (Bashir.dodo@brunel.ac.uk)
Automated Layer Segmentation of Macula SD-OCT Images Using Graph-Cut Method
Bashir I. Dodo, Yongmin Li, Khalid Eltayef and Xiaohui Liu
Bashir Dodo

2. Presentation Outline Motivation
Challenges in Retinal layer segmentation
Image Segmentation
Proposed Method
Result
Conclusion

3. Motivation
The world’s four leading causes of blindness and visual impairment
Cataract -Affects the Lens and is noticed early by Patients
Glaucoma – Disease known for centuries, due to difficulties in its early diagnosis and frequent necessity of life-long treatment.
Age-related macular degeneration (AMD) - Ranks third among the global causes of visual impairment
Diabetic Retinopathy - Due to increase of Diabetics
World Health Organization (WHO)

4. Challenges In Retinal Diagnosis
Some challenges faced in the retinal layers segmentation for diagnosis are:
Causes of the diseases are unknown
Errors of manual segmentation
Variation from one specialist to another
Inconsistence of Retinal Structure
Speckle noise and shadows of blood vessel
Inexistence of universal Segmentation Algorithm

5. Image segmentation
Image segmentation is the process of automating or facilitating the delineation of anatomical structures and other regions of interest(Pham, Xu, & Prince, 2000). Which is based on similarity, differences or proximity (Wertheimer, 1923).
Figure 1: OCT image with boundaries and order of segmentation

6. Proposed Method: Schema
Figure 2: Schematic representation of segmentation method

7. Proposed Method: Image Enhancement
Top – Raw Images corrupt with noise Bottom – Enhanced images
Figure 3: Unprocessed images (top) compared to pre-processed (bottom

8. Proposed Method: Gradient images
Compute vertical Image Gradient
Normalize Image Gradient
Dark-light transition
Get inverse of Image Gradient with *-1+1
Light-dark transition

9. Proposed Method: Building weights

10. Proposed Method: Segmentation process

11. Result Successfully segments 7 retinal layers across 8 boundaries.
Applicable in real-time (~4.25sec per Image)
Improved results accuracy
Figure 4: Segmentation output- 7 layers across 8 boundaries

12. Result…
The resolution of the B-scan images are 512 pixels in depth and 992 pixels across section with 16 bits per pixel. Ground truth images were labelled under the supervision of clinical experts.

13. Conclusion: Significance
The importance of image segmentation in technology cannot be over emphasized as it plays a crucial role in many medical- imaging applications. Specifically, this study will aid in:
Preventing major eye diseases through early diagnosis
Monitoring progress of medication
Easing diagnostic process
Reducing variability in segmentation amongst professionals/ophthalmologists

14. CONCLUSION Comparison with other methods
Reduce rigid nature of segmentation algorithm
Optimizing the flow of the gradient