Perceptual Organization based
method in vessel extraction from
real retina images
Revised on Sept 17,2004
Frank Tao
Motivation & Objectives:
• Retina vessel map segmentation is very important to
•
•
medical applications, such as diabetic retinopathy, aging
related retina analysis etc.
Available effective solutions will either cause high
computational cost or need users intervention
Our objectives:
– Develop an efficient, accurate automatic solution based on
perceptual organization principle: perceptual curve partition &
grouping
• Edge trace partition
• Generic edge token grouping
Review
• Available researches can be grouped into
following classes based on a review paper:
– Pattern recognition
– Matched filter related methods (MFR)
– Regional growing
– Vessel tracking
– Artificial intelligent
– Others
Review
-continuing
• All the available systems can also be re-grouped into following classes
based on the different features they are trying to search for:
– Linear segment structure
• MFR related methods
• Morphology models: snake, water shade
• Regional growing
• Some tracking methods
– Center line and/or edge
• Zhou matched filter edge tracking
• Quebec parallel matching edge tracking
• Sobel edge detection and tracking
• Others
– Others
• Artificial intelligence
– Fuzzy c mean
– others
Pro and cons of current systems
• Line segment structure based : MFR, Pattern recognition, Artificial
intelligence etc.
– Advantages
• Automatic system
• Good noise suppression and vessel segmentation
• Continues vessel map including junction structures
– Disadvantages
• High computational cost
• Center line and/or edge based : Vessel tracking
– Advantages
• Computational efficiency
• Good noise suppression and vessel segmentation
– Disadvantages
• Non automatic
• Non continues map with poor junction detection and breaking of vessel
segments
Our proposed system
• System design:
– Robust vessel feature extraction based on Perceptual
Organization
– Effective vessel junction and breaking fixing and extracting using
limited numbers of guided matched filters
• Targets:
–
–
–
–
Fully automatic
Very low computational cost
Good noise suppression and vessel segmentation
Continues vessel map including junctions and low intensity
vessel segments
Perceptual Organization based method
for vessel segment extraction
• GAO’s Curve Partitioning Methods
– Image processing with edge map obtained from an Edge Tracker
software which detected and extracted all the edge traces based
on the following rules:
•
•
•
•
Intensity similarity
Shortest distance
Direction similarity
Noise removal principle
– Linearity
– length
– Curve Partitioning and Grouping
Perceptual Partition & Grouping
Gibson’s Observation:
The qualities of a simple line observed by Gibson:
(a) “Left slant… Zero slant… Right Slant”
(b) “Convex…straight…concave”
Perceptual Partition & Grouping
Psychological experiments of 2-D curve partitioning:
1) best mark those locations at which distinctive curve segments are
“glued” together;
2) best allow the reconstruction of the complete curves;
3) best allow a viewer to distinguish a given curve from the others.
GCS Partition Model
• Analytic descriptors of curves: f (x,a) = 0
•
where x denotes an image point, a is a vector of
parameters.
A generic curve segment : GCS = { x | p (x) }
where x is an edge point, p (x) indicates the point
satisfies the property p. This property p can be
represented by the following function:
p (x) = { f (x), j (y), f’ (x), j’ (y)}
Where y = f (x) is a curve, x = j (y) is its inverse function,
j’ (y) are their first derivatives
f’ (x) and
GCS Partition Model
A set of generic curve
segments (GCS)
Definition of GCS, M+ is
monotonic increase and
vice verse
GCS
f(x)
(y)
f’(x)
’(y)
CS1
M+
M+
M+
M-
CS2
M-
M-
M+
M-
CS3
M+
M+
M-
M+
CS4
M-
M-
M-
M+
LS1
M-
M-
c
c
LS2
M+
M+
c
c
LS3
c
N/A
0

LS4
N/A
c

0
GCS Grouping
Definition of CPPs and Curve Grouping Rules:
Extra CPPs (dark
dots) introduced to
increase the sensitivity
of junction detection
Rule #
Definitions
G1
(CPP1, CS1, CS2)
G2
(CPP2, CS2, CS3)
G3
(CPP3, CS3, CS4)
G4
(CPP4, CS4, CS1)
G5
(CPP5, CS1, CS3)
G6
(CPP6, CS2, CS4)
G7
(CPP7, CS, LS)
G8
(CPP8, LSi, LSj)
Retinal Image Based Knowledge
• Vessel map definition
– Junctions and Endings
• Junctions: Branching junctions (including Y junction and T junction);
Crossing junctions
• Endings
– Vessel Segments
• Perceptual Partitioning and Grouping of the edge trace map
– Original CPP detection: Aligned CPP, Junction CPP, Ending CPP
– Virtual CPP creation through two-sides-parallel-scanning
• Two Sides Parallel Scanning: stretched out from both side of the detected CPP, using the gradient of
–
the original pixel to do a parallel scanning, try to find matching pair pixels with reverse gradient within
a pre-defined vessel width
Associated parallel GCS grouping based on original and virtual CPPs
• How to find out the Vessel segments in the edge trace map?
– Extracting vessel segments through connecting all the directly linked associated
parallel GCS pairs
Vessel Map Definition
Original Retina Image
Vessel Map definition
CPP and related structure
Junction CPP and related structure
Non-Junction CPP and related
structure
CPP detection and Virtual CPP creation
Original CPP
Virtual CPP creation via TwoSide-Parallel-Scanning
Associated parallel GCS grouping
and Vessel segment extraction
Associated parallel
GCS grouping
Vessel segment
extraction
Original edge trace
map
Vessel junction, breaking detection and
extraction using guided matched filters
• Assume vessel segment has:
– Gaussian shaped gradient profile perpendicular to it’s length
direction
– Piecewise linear structure
– Vessel width very close thus can be treated as same
• Assume the junction, vessel breaking structure:
– Vessel breakings: Sit between any two detected vessel segments
– Vessel junctions: intersection, crossing or overlapping of
different vessel branches
• Using the direction information from detected vessel
segments to build up matched filter and convolving it
over the junction and vessel breaking areas to detect
then extract junctions, breakings
System Architecture
Pre-Processing
Vessel Map
Extraction
Gaussian
Blurring
Original CPP
detection and
GCS partition
Extract Edge
Traces
Virtual CPP
creation via two
sides parallel
scanning, GCS
further partition
Junction &
breaking
detection with
guided
matched
filters
Noise
removal
Associated
parallel GCS
pair grouping
and Vessel
segment
extraction
System Architecture
• Extract edge traces from retina image:
– Smooth image by Gaussian blurring
– Apply the edge tracker to extract edge traces
– Remove short and non-linear noise traces
• Vessel map extraction:
– Original CPP detection and GCS partitioning
– Virtual CPP creation through two-side-parallel-scanning and GCS
further partitioning
– Associated parallel GCS grouping and vessel segment extraction
– Vessel junction and breaking fixing with limited, guided Matched
Filters
System evaluation
• General performance:
– Automation:
• No user provided start or ending point needed for our system
– Fast: Very efficient system:
• It only takes 2 seconds (average time) for step 1 and 3
seconds for step 2 (average time)
– Accuracy:
• Avoid human created noise VS from global MF enhancement
– Continues vessel map structure:
• Junctions and breakings were correctly detected or fixed
Result comparison with A.Hoover’s system
• Two standard sets of manual drawing retina
vessel map from two experts
– A.Hoover (normal one)
– V. Kouznetsova (rich vessel map)
• By compare with the rich manual drawing vessel
•
map, our system obtained high positive rate
while the negative rate remain lower than AH
system
Our system proved to be good at detecting even
low intensity vessel map
Image 0163- negative rate (green)
Original Retina Image
A.Hoover’s standard
vessel map
Our System
Matched Filter Enhance
Image
A.Hoover’s standard
vessel map
A.Hoover’s System
V.K’s standard vessel
map
Our System
V.K’s standard vessel
map
A.Hoover’s System
Image 0163 Positive rate (brown)
Original Retina Image
A.Hoover’s standard
vessel map
Our System
Matched Filter Enhance
Image
A.Hoover’s standard
vessel map
A.Hoover’s System
V.K’s standard vessel
map
Our System
V.K’s standard vessel
map
A.Hoover’s System
Negative Rates
Positive Rate
Summary
• Perceptual Curve Partitioning method provides an robust
way in handling vessel map extraction
– The proposed system has achieved the following targets:
• Automation
• Efficiency
• High Accuracy even for low intensity retina vessel map
• Limitation:
– For some abnormal retina images, like some strong bright patches in
the background, this system will receive some false detected vessel
segments.
• Future works:
– Further verification method could be applied to minimize the negative
detection rate
– Investigate how to combine more domain heuristics of retina images
into the perceptual edge tracking mechanism for improving our
implementation
Acknowledgement
The authors gratefully acknowledge that this
research received funding support from both
NSERC and Deep Vision Inc. Deep Vision Inc.
also provided the authors with their edge tracker
software which was used for producing original
edge trace data.
References:
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
Ferris FL, "How effective are treatments for diabetic retinopathy?", JAMA 269, 1993, pp.1290-1291.
L. Pedersen, M. Grunkin, B. Ersbøll, K. Madsen, M. Larsen, N. Christoffersen, U. Skands, “Quantitative measurement of changes in
retinal vessel diameter in ocular fundus images”, Patt. Recog. Lett., 21, 1215-1223, 2000.
Khoobehi, B., Peyman, G.A., Vo, K.D., “Relationship between Blood Velocity and Retinal Vessel Diameter”, ARVO Abstract, Invest.
Opthalmol. Vis. Sci., 33, 4, 804, 1992.
M. Lalonde, L. Gagnon, M.-C. Boucher, “Non-recursive paired tracking for vessel extraction from retinal images”, Proceedings of the
Conference Vision Interface 2000, 61-68, 2000.
Luo Gang, Opas Chutatape*, and Shankar M. Krishnan,"Detection and Measurement of Retinal Vessels in Fundus Images Using
Amplitude Modified Second-Order Gaussian Filter,"IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 2, FEBRUARY
2002.
QI-GANG GAO and A.K.C. WONG, Curve Detection Based On Perceptual Organization, Pattern Recognition, Vol. 26, No. 7, pp.1039-1046,
1993.
A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched
filter response,” IEEE Trans. Med. Imag., Vol. 19, No. 3, pp. 203–210, 2000.
F. Zana and J.-C. Klein. A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform. IEEE
Trans. Medical Imaging, 18(5):419-428, 1999.
S. Chaudhuri, S. Chatterjee, N. Katz, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched
filters,” IEEE Trans. Med. Imag., Vol. 3, pp. 263–269, Sept. 1989.
O. Chutatape, L. Zheng, and S. M. Krishnan, “Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters,” in
Proc.20th Annu Conf. IEEE Engineering in Medicine and Biology Society, 1998, pp. 3144–3149.
Can, H. Shen, J. Turner, H. Tanenbaum, and B. Roysam, “Rapid automated tracing and feature extraction from retinal fundus images
using direct exploratory algorithms,” IEEE Trans. Inform. Technol. Biomed.,Vol. 3, pp. 125–138, June 1999.
V. Rakotomalala, L. Macaire, J-G. Postaire, and M. Valette. Identification of retinal vessels by color image analysis . Machine Graphics
and Vision, 7:725-742, 1998.
L. Gagnon, M. Lalonde, M. Beaulieu, M.-C. Boucher,Procedure to Detect Anatomical Structures in Optical Fundus Images,Proc SPIE Vol
4322 Med Imaging:Img Processing 2001 1218-25.
D. H. Ballard, “Generalizing the Hough Transform To Detect Arbitrary Shapes”, Pattern Recognition 13, p111-122, 1981.