Transcript ppt - IIIT Hyderabad

ROBUST OPTIC DISK SEGMENTATION
FROM COLOUR RETINAL IMAGES
Gopal Datt Joshi, Rohit Gautam, Jayanthi Sivaswamy
IIIT Hyderabad
CVIT, IIIT Hyderabad, Hyderabad, India
S. R. Krishnadas
Aravind Eye Hospital, Madurai, India
Optic Disk (OD) Segmentation
• Changes in OD are indicative for
Glaucoma
• Direct ophthalmoscope is used to
assess changes
– a subjective OD parameterisation
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• Retinal image analysis is a valuable
aid for the objective assessment
• OD segmentation is a fundamental
task
Challenges
 irregular disk shape
Smooth region
transition
 blood vessel occlusions
 ill defined boundaries
Vessels
 atrophy presence
 regions around OD with similar
image characteristics
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Optic disk
boundary
Atrophy
region
 inter and intra image
variations
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
Fixed shape assumption…
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
Sensitive to local gradients
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To handle:
• Impose shape prior
(Fixed or Learned)
• Evolution on selective curve points
Sensitive to contour initialisation
Region based
Active Contour
• Chan Vese (C-V)
Active contour
• Yandong et al. (2006):
• Joshi et al. (2010)
Advantages & Limitations >> next
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
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– Joshi et al. (2010): with no shape constraint
C-V model: Limitations
Example of smooth
region transition
Erroneous segmentations
where the object cannot be
easily distinguished in terms of
global statistics
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Example of high
atrophy
C-V model
expert
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
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 to differentiate the OD region from atrophy regions
Basic Chan Vese (C-V) Model
• Consider a vector valued image
in the image domain
where
• The C-V model defines the energy functional as:
contour ‘C’
outside
inside
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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
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
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• to bring robustness against distraction near OD region
Localisation of C-V model
The redefined energy for a point ‘x’
outside
region
where, h+ and h- are two constants that approximate region intensities inside
and outside a contour, near the point x
defines a local image domain around a point x
with in the radius of r
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x and y denotes two points in the image I.
This energy is minimum when a point is exactly on the
boundary
inside
region
Multi-feature Channels Integration
outside
Integration of multiple feature channels
to the model as:
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inside
Red colour space
Original Colour Image
 gives a better discriminating representation of image regions
Texture Space- 2
Texture Space- 1
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 make model robust to the distractions found near the OD boundaries
Energy minimisation over all Points
• The integral of
over all points ‘x’ is minimised to
obtain entire object boundary, defined as:
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• An equivalent level-set formulation is defined for
curve evolution*
* Refer to the article for the details of level-set formulation.
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
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• To only assess the strength of individual model
Results
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Input
Initialisation
GVF
C-V model
Expert Marking
Proposed model
Method’s Result
Inter Observer Variability
This is due to
 level of clinical experience
 comfort level with the marking tool
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The proposed method has better
consensus with the average marking
White: Proposed method; Other: Experts
Evaluation Metrics
• Region-based: pixel wise segmentation accuracy
• Boundary-based: boundary localisation accuracy
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• Let Cg be the boundary marked by the expert and Co be the
boundary obtained by the method
Results on 8 Difficult Images
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Average signed boundary Distance
Under segmentation
Over-segmentation
Results on 138 images
F-score
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Average Boundary Distance
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
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 suitable for various OD shapes
 no shape prior used
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Thanks 
Gopal Datt Joshi
PhD Student
CVIT, IIIT Hyderabad
email: [email protected]
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
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• 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