1. Detection of Retinal Vessels in
Fundus Images through
Transfer Learning of Tissue Specific
Photon Interaction Statistical Physics
Debdoot Sheet
School of Medical Science and Technology
Indian Institute of Technology Kharagpur, India
debdoot@smst.iitkgp.ernet.in
10 Apr 2013 1ISBI 2013 - ThCT1.5 [Debdoot Sheet]
Authored by: D. Sheet, S. P. K. Karri, S. Conjeti, S. Ghosh, J. Chatterjee and A. K. Ray

2. Contents
• Motivation
• Physics of Retinal Imaging
• Statistical Physics of Tissue-Photon Interaction
• Transfer Learning of TPI
• Vessel Detection in Fundus Images – Framework
• Performance Assessment
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3. Motivation
• Fundus imaging is used for
screening of
– Diabetic retinopathy
– Glaucoma
– Age-related macular degeneration
– Hypertension and Stroke induced
changes
• Challenges include
– Vessels, fovea, optic disc –
localization and segmentation
– Pathology detection
– Image quality assessment
• Vessel detection in white light Fundus
imaging
– Necessary for reporting of pathologies
– Reduces clinician’s dependency on FA
– Solutions include
• Staal et al. (2004)
• Niemeijer et al. (2004)
• Zana et al. (2001)
• Jiang et al. (2003)
• Martinez-Perez et al. (1999)
• Chaudhuri et al. (1989)
• Limitations
― Less Accurate (< 94%)
― High inter-observer variability (κ<0.71)
― Performance less than 2nd
human-observer
(Acc.=95%, κ=0.76)
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4. Physics of Retinal Imaging
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Spatially varying spectral reflectance pattern of the retina
Spatially varying illumination model
PSF of the ophthalmoscope
Spectral transmission of ophthalmoscope
( ) ( ) ( ) ( )[ ] ( )λλλλλ tyxpyxLyxRyxB ,,,,,,,, ∗=

5. Statistical Physics of Tissue-Photon
Interaction
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Photo-current readout
Dark-current noise Shot noise
Readout noise Quantization noise
External quantum efficiency of sensor
Rate of photon induced electron generation
Integration time Amplification factor
QRSDC NANNNTKD ++++= )( ρ
TKNNNN QRSDC ρ<<,,,
TTAKD ′=≅ ρρ

6. Statistical Physics of Tissue-Photon
Interaction
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Spatial response of the collection site
Internal quantum efficiency of sensor
Spectral irradiance pattern incident on the sensor
( ) ( ) ( )∫∫∫=
λ
λλλρ ddydxqyxSyxB
y x
r ,,,

7. Statistical Physics of Tissue-Photon
Interaction
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( ) ( ) [ ]21,
!
,| λλλ
ρ
ρ
ρ
∈
′
∝′
′−
d
eT
Tdf
Td
( )Tdf RR
′,| ρ
( )Tdf GG
′,| ρ
( )Tdf BB
′,| ρ
Distribution for GREEN sensor element
Distribution for BLUE sensor element
Distribution for RED sensor element
[ ] ( )ddET var==′ρ
[ ]{ } [ ] { } { }n
k
kkkkBGR ,,,,,, 21 ∈∈∀Φ=Θ λλ
[ ] [ ] [ ][ ]k
k
dET λλλ ρ =′=Φ Multiscale photon density estimation
TPI Model

8. Learning of TPI
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{ } { }vascular-nonvessel,| ∈∀Θ ωω
Assuming there exists a primal relation in TPI for each tissue type
{ }trainI be a set of images in this relation holds valid and where
these tissues are marked
{ }( )train;,| IIΘωH Response of the learnt model provides
probability of finding vessels

9. Vessel Detection Framework
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Learn TPI Model
Training Image Ground Truth Labels
Test Image
Learn TPI Model
Vessel Detection Probab.
{ }( )train;,| IIΘωH

10. Performance Assessment
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11. Performance Assessment
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12. Performance Assessment
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13. Take Home Message
• Photons interact characteristically with different tissues
– This is manifested through the spatially varying spectral reflectance
model R(∙)
• The stochastic nature of TPI accounts for uncertainties in
observations.
– Learning of TPI statistical physics overcomes these uncertainties.
• Transfer learning is a good framework for solving such 2-level
learning tasks
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