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Automated Layer Segmentation of Macula SD-OCT Images Using Graph-Cut Method

Automated Layer Segmentation of Macula SD-OCT Images Using Graph-Cut Method. Bashir I. Dodo, Yongmin Li, Khalid Eltayef and Xiaohui Liu. Bashir Dodo (Bashir.dodo@brunel.ac.uk). Presentation Outline. Motivation Challenges in Retinal layer segmentation Image Segmentation

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Automated Layer Segmentation of Macula SD-OCT Images Using Graph-Cut Method

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

  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

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