The Implementation of an Infrared-Ray Image
Acquisition and Quantitative Analysis System
of Blood Vessel on Human Extremity
Presenter: Feng-Chiao Chung
Adviser: Dr. Pei-Jarn Chen
2007/08/23
1
Outline








Introduction
Literatures Review
Motivation & Purpose
Materials & Methods
Results & Discussion
Conclusions
Future Prospect
References
2
Introduction

Diabetes in Taiwan

Among the population over 65 years old


Ten major causes of death in 2006


20% of they are diabetic
http://www.doh.gov.tw/
What is diabetes？



Type I
Type II
Complication


Pathological change
Infection disease
3
Introduction

Complication of Diabetes

Pathological change




Nerve
Heart vessel
Kidney and eyes capillary
Infection of the foot


Cellulitis
Osteomyelitis
Source：http://www.dmfoot.idv.tw
4
Introduction

Diabetes foot


Amputation
Inspection

Computed Tomography（ CT ）


CT Image
Contrast Medium
Digital Subtraction Angiography
（ DSA ）
DSA Image
Source：http://www.24drs.com、http://www.invasivecardiology.com
5
Literatures Review

John G., “Medical Instrumentation Application and
Design”, 1998
Hb
HbO
6
Literatures Review

Junichi Hashimoto, “Finger Vein Authentication
Technology and its Future”, 2006 Symposium on
VLSI Circuits Digest of Technical Papers
Block Diagram of Finger Vein Authentication
Cross-sectional Profile of Finger Vein Image
7
Literatures Review

Chih-Chieh Wu, “Design of a Portable Microcirculation
Photography System and Image Analysis”, Chung Yuan
University Electrical Engineering Institute, M.S. Thesis,
2003
Mean
S.D.
T1=33°C
112
23.3
T2=19°C
60
15.4
8
Motivation & Purpose

To implement an infrared-ray image
acquisition and quantitative analysis system



Acquire clear infrared-ray images
Provide quantitative parameters
Apply this system to clinical examination
9
Materials & Methods
System Architecture
10
Hardware

Camera Module







Camera- UI-1220
752*480
bmp
Lens- IDS-30
Filter- 850 nm
Light Source- IR LED
Sony XC EI30
NB




1.66 GHz
512 MB DDR RAM
Windows XP
Matlab
Source: http://www.idsvision.com.tw
11
Image Acquisition Module
Finger vein acquisition module
Focus distance: 12.5 cm
Toe vein acquisition module
12
Materials & Methods
Finger Vein
Toe Vein
Original Image
752*480
13
System Flow Chart
14
Pre-processing
Original Image
Binary
Image
Enhancement
Median
Filter
Morphology
( close )
Average
Filter
Remove
Small Regions
15
System Flow Chart
16
Invariant Moment

Maintain invariant properties through
the following operations:




Rotation
Translation
Scaling
Applications:


Shape Matching
Pattern Recognition
17
Invariant Moment
(M. K. Hu, 1962 )

Moment of two dimension
m pq   x p y q f x, y 
x

y
Central moments
L
L

 
μ pq   X i - X Yi -Y f  X, Y 
p
q
i 1 j 1

m10
m01
x
&y
m00
m00
Normalized central moments
η pq 
μ pq
 μ00 γ
, where γ 
pq
1
2
18
Invariant Moment
19
Euclidian distance
Threshold = 0.02
Example
20
System Flow Chart
21
Quantitative Parameters Analysis

Fractal Brownian Motion


A method to count fractal dimension
Ratio of Vessel Diameter

Between the daughter vessel diameter
22
Graphical User Interface
23
Graphical User Interface
24
Graphical User Interface
25
System Calibration

Optical System Calibration


Modulation Transfer Function, MTF
Geometric Distortion
Original Image

distortion
Phantoms Validation
26
Results & Discussion

System Calibration






Geometric Distortion
Magnification
Phantom Validation
Invariant Moments
Fractal
Ratio of Vessel Diameter

Compared with man-made selection
27
Conclusion





A low cost camera system with infrared lens and LED light source is
feasible to acquire blood vessel image.
Invariant moment is feasible to recognize the image acquire from the
same position or not.
Fractal can provide the information about vessel distribution.
Ratio of vessel can be calculated by this system accurately.
It’s necessary to improve:
 The optical field design.
 The image pre-processing.
 With lens of shorter focal length to reduce the system size.
28
Future Prospect

To implement an analysis system applied in
clinic

To collect more images with variant people

To find out more feasibly quantitative
parameters applied in clinic on diabetes
29
References
1. 行政院衛生署(Department of Health, Executive Yuan, Taiwan, R.O.C)
http://www.doh.gov.tw/
2. John G., “Medical Instrumentation Application and Design”, 1998.
3. Junichi Hashimoto, “Finger Vein Authentication Technology and its
Future” , 2006 Symposium on VLSI Circuits Digest of Technical Papers.
4. Jan Flusser and Tomas Suk, “Pattern Recognition By Affine Moment
Invariants,” Pattern Recognition, vol. 26, no. 1, pp. 167-174, 1993
5. A. Conci and C. B. Proenca, “A fractal image analysis system for fabric
inspection based on a box-counting method,” Computer Networks and ISDN
Systems, vol.30, no.20-21, pp.1887-1895, 1998.
6. Naomi Tsafnat, Guy Tsafnat and Tim D. Lambert, “A Three-Dimensional
Fractal Model of Tumour Vasculature,” Proceedings of the 26th Annual
International Conference of the IEEE EMBS San Francisco, CA, USA。
September 1-5, 2004.
7. J.T. Hast, T.Prykari, E.Alarousu, R.A Myllyla, and A.V. Priezzhev, ‘Flow
velocity profile measurement of scattering liquid using Doppler optical
coherence tomography”, in: Optical Diagnostics and Sensing in Biomedical
III; Alexander V. Priezzhev, Gerard L. Cote – Eds. Proc. SPIE 4965, pp. 6672(2003)
30
Demo Time
31
Thank you for your attention
32
(a)
(b)
Infection of the foot
Source: A. J. M. Boulton, The Foot in Diabetes
33
Pixel Number
Pixel Number
Adaptive Histogram Equalization
Gray Value
Original Image
Gray Value
Adaptive Histogram Equalization
34
Median Filter & Average Filter

Median Filter


Remove pepper and salt noise
Average Filter

Provide image smoothing and noise removal
After median filter processing
After average filter processing
35
Binary & Close

Binary


Convert gray-level to binary
Close

Dilation first and then erosion
Binary Image
After average filter processing
After close processing
36
Binary Image 200*200
Moments：
0.47494
0.017346
0.042705
0.0005229 -8.66E-09
5.46E-05
-2.38E-06
37
(a) Rotation
(b) Original
Moments：
(a)
0.47351
0.017464
0.042948 0.00057488
(b)
0.47494
0.017346
0.042705
Euclidian distance : 0.0014562
1.74E-07
6.13E-05
-5.67E-07
0.0005229 -8.66E-09
5.46E-05
-2.38E-06
38
(a) Translation
(b) Original
Moments：
(a)
0.47369
0.017257
0.042515
0.0005292 -2.16E-08
5.48E-05
-6.27E-07
(b)
0.47494
0.017346
0.042705
0.0005229 -8.66E-09
5.46E-05
-2.38E-06
Euclidian distance : 0.0012675
39
(a) Broken
(b) Original
Moments：
(a)
0.48029
0.017565
0.044098 0.00064849 -8.87E-07
5.88E-05
-1.84E-06
(b)
0.47494
0.017346
0.042705
5.46E-05
-2.38E-06
0.0005229 -8.66E-09
Euclidian distance : 0.00553342
40
Fractal Dimension
(Benoit Mandelbrot, 1967)

Properties



Fractional Dimension
Self-similarity
Unconcerned with the
scale
log N s 
D
log 1 / s 
log 4 
log 3
log 16 

log 9 
 1.262
D
Koch Curve
41
Fractal Brownian Motion
(C. C. Chen, 1989)
D  3 H
0  H  1 , H（Hurst Coefficient）

Hurst Coefficient

To compute average gray-level


H↑, image is smoother
H↓, image is coarser
42
Fractal Brownian Motion

Define
r 
x2  x12  y2  y12
I r  Ix 2, y2  Ix1, y1
r : distance
I r : difference of gray - level from r
43
Fractal Brownian Motion

Hurst Coefficient
log EI r   H log r   log k

Simplification (E. L. Chen, 1998)
M 1 M  k 1








I
x
,
y

I
x
,
y

k
/
M
M

k




x 0 y 0


M

1
M

k

1


   Ix, y   Ix  k, y  / MM  k 


y

0
x

0
/4
d i k   
M  k 1 M  k 1


2







I
x
,
y

I
x

k
,
y

k
/
M

k




y 0
x 0
 M  k 1 M  k 1

2









I
x
,
M

y

I
x

k
,
M

y

k
/
M

k




 y 0 x 0

44
Ratio of Vessel Diameter
(M. Zamir, 1999)

In normal vasculature


most blood vessels (98%) bifurcate at each
junction
small number (2%) trifurcate
d1
d2
d1 and d 2 both are daughter v essel diameter, d1  d 2
Generally, 0.25 < α < 1
45
gray-level
Polynomial Curve Fit
pixel
46
Gaussian Curve Fitting with
Polynomial
1
y  f ( x | , ) 
e
 2
( x )2
2 2
x     , when y ' '  0
μ= 5
σ= 2
σ
μ
σ
47
Results of Gaussian Curve Fit
p( x)  p1 x n  p2 x n1  p3 x n2  ...  pn x  pn1
0.1995
0.1192
0.121
σ
x = 0 : 0.01 : 10
μ=5
σ=2
RMSE = 0.0002
n=9
2.98
σ
μ
7.01
48
RMSE
pixel
Order
x = 0:0.01:10
μ=5
σ=2
Order
49
Results of Geometric Distortion
Test
Capture
ΔX
Max. error = 0.266%
ΔY
Max. error = 0.417%
50
Results of Magnification

Focused distance


Conversion Factor


11cm ~ 17cm
0.02 ~ 0.0384 (mm/pixel)
Focus distance: 12.5 cm

40 pixel in the image
= 1mm in real distance
51
Results of Phantom Validation
Specification of Tubes
編號
外徑（mm）
1
3
1.8
0.6
2
3.5
3.1
0.2
3
5
3
1
1
內徑（mm） 管壁厚度（mm）
2
In the air
3
1
2
In the water
3
52
Results of Phantom Validation

Light - IR LED



Low power
Scatter angle
Tissue of chicken

With IR LED
Differ from human
With lamp
53
Tube No.3
Tube with water
External of Tube
In the air
Behind the chicken
54
Results of Invariant Moments

Sample
28 組
手指血管影像
20 組
腳趾血管影像
8組
Effects

Binary threshold


1.Threshold=76
Image Quality
Illumination
2.Threshold=76
ED：0.0248119 > 0.02
每組有2張影像 共56張
正確
24
錯誤
4
正確率
85.71%
55
n=6
RMSE = 1.1404
gray-level
Results of Polynomial Curve
Fit
66.25
79.12
pixel
Width
＝79.12 – 66.25
＝12.87
≒13 pixels
56
Results of Polynomial Curve
Fit

63
Calculated value
26 pixels
Mean & S.D. of select
24 ± 2 pixels
Effect


Sample
Position of user click
Error Range

0 ~ 5 pixels
Single part
Sample
100
Average error 9.01±7.39%
5 specific parts
57
Results of Fractal

Image


Coarse, h↓
Smooth, h ↑
58