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IIIT Hyderabad ROBUST OPTIC DISK SEGMENTATION FROM COLOUR RETINAL IMAGES Gopal Datt Joshi, Rohit Gautam, Jayanthi Sivaswamy CVIT, IIIT Hyderabad, Hyderabad,

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Presentation on theme: "IIIT Hyderabad ROBUST OPTIC DISK SEGMENTATION FROM COLOUR RETINAL IMAGES Gopal Datt Joshi, Rohit Gautam, Jayanthi Sivaswamy CVIT, IIIT Hyderabad, Hyderabad,"— Presentation transcript:

1 IIIT Hyderabad ROBUST OPTIC DISK SEGMENTATION FROM COLOUR RETINAL IMAGES Gopal Datt Joshi, Rohit Gautam, Jayanthi Sivaswamy CVIT, IIIT Hyderabad, Hyderabad, India S. R. Krishnadas Aravind Eye Hospital, Madurai, India

2 IIIT Hyderabad Optic Disk (OD) Segmentation Changes in OD are indicative for Glaucoma Direct ophthalmoscope is used to assess changes –a subjective OD parameterisation Retinal image analysis is a valuable aid for the objective assessment OD segmentation is a fundamental task

3 IIIT Hyderabad Challenges Optic disk boundary Smooth region transition Atrophy region Vessels  irregular disk shape  blood vessel occlusions  ill defined boundaries  atrophy presence  regions around OD with similar image characteristics  inter and intra image variations

4 IIIT Hyderabad OD Segmentation State of the Art Template Matching Based on  Shape  Intensity Lalonde et al. 2001 Chrastek et al. 2002 Abdel-Ghafar et al. 2007 Pallawala et al. 2004 Gradient based Active Contour Snakes Mendels et al. 1999 Lowell et al. 2004 Li et al. 2003, 2004 Wong et al. 2008 Novo et al. 2009 Juan et al. 2007 Region based Active Contour Region based Active Contour Chan Vese (C-V) Active contour Yandong et al. (2006): Joshi et al. (2010) Fixed shape assumption…  Sensitive to local gradients To handle: Impose shape prior (Fixed or Learned) Evolution on selective curve points  Sensitive to contour initialisation Advantages & Limitations >> next

5 IIIT Hyderabad Region-based Active Contour Advantages:  lower sensitivity to contour initialisation and noise  feasibility of segmentation of color images even in the absence of gradient-defined boundaries  better ability to capture concavities of objects Attempts with Chan Vese (C-V) model –Yandong et al. (2006): with a circularity constraint –Joshi et al. (2010): with no shape constraint

6 IIIT Hyderabad C-V model: Limitations expert C-V model Erroneous segmentations where the object cannot be easily distinguished in terms of global statistics Example of smooth region transition Example of high atrophy

7 IIIT Hyderabad Objective –To achieve consistent and robust segmentation Strategy  by integrating local statistics  to improve sensitivity to the slowly varying gradient boundaries  by integrating information from multiple image feature channels  to differentiate the OD region from atrophy regions

8 IIIT Hyderabad Basic Chan Vese (C-V) Model Consider a vector valued image where in the image domain The C-V model defines the energy functional as: c+ and c- are two constants to approximate the image intensity inside and outside of the contour C. λ and µ are weights for the fitting and the regularizing terms, respectively inside outside contour ‘C’

9 IIIT Hyderabad Proposed Model Underlying assumption in C-V model –image consists of statistically homogeneous regions  lacks in handling inhomogeneous objects The basic idea is –local instead of global statistics to extend the scope of the model –information from multiple feature channel to bring robustness against distraction near OD region

10 IIIT Hyderabad defines a local image domain around a point x with in the radius of r Localisation of C-V model The redefined energy for a point ‘x’ outside region inside region where, h + and h - are two constants that approximate region intensities inside and outside a contour, near the point x x and y denotes two points in the image I. This energy is minimum when a point is exactly on the boundary

11 IIIT Hyderabad Multi-feature Channels Integration Integration of multiple feature channels to the model as: outside inside

12 IIIT Hyderabad Red colour space Texture Space- 2 Texture Space- 1 Original Colour Image  gives a better discriminating representation of image regions  make model robust to the distractions found near the OD boundaries

13 IIIT Hyderabad Energy minimisation over all Points The integral of over all points ‘x’ is minimised to obtain entire object boundary, defined as: An equivalent level-set formulation is defined for curve evolution* * Refer to the article for the details of level-set formulation.

14 IIIT Hyderabad Experimentations Dataset: 138 images of size (2896 x 1944 pixels) –collected from Aravind Eye Hospital, Madurai Ground Truth: from 3 eye experts –we derive an average boundary to compensate inter-observer variability Comparison:  With two known active contour models Gradient vector flow (GVF) Basic C-V model  Contour initialisation and pre-processing are kept same To only assess the strength of individual model

15 IIIT Hyderabad Results InputInitialisationGVFC-V model Proposed model Expert MarkingMethod’s Result

16 IIIT Hyderabad Inter Observer Variability White: Proposed method; Other: Experts This is due to  level of clinical experience  comfort level with the marking tool The proposed method has better consensus with the average marking

17 IIIT Hyderabad Evaluation Metrics Region-based: pixel wise segmentation accuracy Boundary-based: boundary localisation accuracy Let C g be the boundary marked by the expert and C o be the boundary obtained by the method

18 IIIT Hyderabad Results on 8 Difficult Images Average signed boundary Distance Over-segmentationUnder segmentation

19 IIIT Hyderabad Results on 138 images F-score Average Boundary Distance

20 IIIT Hyderabad Conclusion  We presented a novel, active contour model to achieve robust OD segmentation  Contributions:  the scope of C-V model is extended  by localising energy functional  robust active contour model  by integrating multiple image feature channels  suitable for various OD shapes  no shape prior used

21 IIIT Hyderabad Thanks Gopal Datt Joshi PhD Student CVIT, IIIT Hyderabad email:

22 IIIT Hyderabad ROI selection and Contour initialisation The red colour plane of retinal image was chosen –it gives good definition of OD region The vessel points are identified and masked using standard vessel segmentation technique We perform localisation and initialisation steps together by performing circular Hough transform on the gradient map Identified circle radius and center point are used to get initialisation for the contour

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