Automated motion reduction in 3D dynamic infrared imaging is on demand in many applications. Few methods for registering time-series dynamic infrared frames have been proposed. Almost all such methods are feature based algorithms requiring manual intervention. We apply different automated registration methods based on spatial displacement to 11 datasets of Breast Dynamic Infrared Imaging (DIRI) and evaluate the results in terms of both the image similarity and anatomical consistency of the transformation. The aim is to optimize the registration strategy for breast DIRI in order to improve the spectral analysis of temperature modulation; thus facilitating the acquisition procedure in a Dynamic Area Telethermometry framework. The results show that symmetric diffeomorphic demons registration outperforms both warped frames similarity and smoothness of deformation fields; hence proving effective for time-series dynamic infrared registratio

Agostini, Valentina

Knaflitz, Marco

Molinari, Filippo

Riyahi-Alam, S.

English

PORTO Publications Open Repository TOrino

Politecnico di Torino
Porto Institutional Repository
[Proceeding] Evaluation of time-series registration methods in dynamic area
telethermometry for breast cancer detection
Original Citation:
Riyahi-Alam S., Agostini V., Molinari F., Knaflitz M. (2013). Evaluation of time-series registration
methods in dynamic area telethermometry for breast cancer detection. In: Advanced Infrared
Technology and Applications (AITA), Turin, September 10 - 13, 2013. pp. 1-4
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EVALUATION OF TIME-SERIES REGISTRATION METHODS IN DYNAMIC 
AREA TELETHERMOMETRY FOR BREAST CANCER DETECTION 
S. Riyahi-Alam
1
, V. Agostini
2
, F. Molinari
3
, M. Knaflitz
4
 
 
1 
Department of Mechanical and Aerospace, Politecnico di Torino, Torino, 10129, Italy, 
sadegh.riyahi@polito.it 
2 
Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy, 
valentina.agostini@polito.it 
3 
Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy, 
filippo.molinari@polito.it 
4 
Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy, 
marco.knaflitz@polito.it 
 
Automated motion reduction in 3D dynamic infrared imaging is on demand in many applications. Few 
methods for registering time-series dynamic infrared frames have been proposed. Almost all such 
methods are feature based algorithms requiring manual intervention. We apply different automated 
registration methods based on spatial displacement to 11 datasets of Breast Dynamic Infrared Imaging 
(DIRI) and evaluate the results in terms of both the image similarity and anatomical consistency of the 
transformation. The aim is to optimize the registration strategy for breast DIRI in order to improve the 
spectral analysis of temperature modulation; thus facilitating the acquisition procedure in a Dynamic 
Area Telethermometry framework. The results show that symmetric diffeomorphic demons registration 
outperforms both warped frames similarity and smoothness of deformation fields; hence proving 
effective for time-series dynamic infrared registration. 
 
Introduction 
Dynamic Area Telethermometry (DAT) can 
be useful in detecting breast cancer. The 
basic assumption is that normal tissues show 
temperature modulation that is different 
from cancerous tissue [1]. The surface 
temperature modulation caused by cancer, 
depends on specific aspects such as 
angiogenesis, local deoxygenation, and 
production of nitric monoxide [1]. Such 
physiological temperature modulation occurs 
at some specific frequencies [1]; hence, the 
spectral analysis of the time variations of the 
local temperature could allow for spotting 
cancerous lesions.  
During a DAT examination, several frames 
of the patients’ breasts are acquired. Prior to 
spectral analysis, the patient movement must 
be eliminated and the sequential frames must 
be registered. The movements disarrange the 
time-temperature series of each pixel, thus 
originating thermal artifacts that might be 
interpreted as a false positive. 
Very few methods were proposed for automated 
dynamic infrared images registrations, all were 
concentrated on manual feature-based motion 
reduction [2],[3]. In a previous work from our 
team [3], a marker-based registration was applied 
on breast dynamic thermograms. However, there 
were some drawbacks (e.g. difficulty in manually 
placing the markers as well as deciding the 
optimal number of markers, additional prior 
marker detection algorithms, limitation on 
choosing types of registration parameters). 
The purpose of this paper is to perform different 
types of linear/non-linear intensity based 
registration methods (implemented in ITK [4]) on 
the healthy subjects, in order to improve the 
accuracy of the spectral analysis of temperature 
modulation. This should ease the patient 
acquisition procedure in a DAT framework with 
respect to the marker-based method.  
To evaluate the performance of each registration 
method, we exploited breast boundary overlap 
along with Normalized Mutual Information 
(NMI), likewise final deformation field (DF) is 
demonstrated for visualizing misalignment errors. 
Materials and Methods 
 
In time-series registration method, different 
frames inside a 3D thermogram are aligned, 
where the first frame is chosen as fixed and 
all the rest sequential frames as the moving 
image, while applying optimal parameters. 
In breast thermal images, intensities directly 
correspond to temperature, hence the 
similarity metrics must minimize the 
geometric displacement of the pixels, instead 
of the pixel intensities, otherwise leads to 
losing information.  
Prior to experiments, the breast regions are 
segmented, using a semi-automated 
algorithm developed by N. Scales et al [5], 
while unsharp masking is applied to negate 
the noise and oversmoothness of 
interpolation. 
 
Intensity based DAT registration methods 
 
First, affine registration (L1) with the Mattes 
Mutual Information is applied as similarity 
metric. The registration parameters are listed 
in Table.1. 
Evaluating the linear registration is 
significant, since in many applications the 
sequences are not aligned in phase and 
single level linear registration could be 
satisfactory. Therefore choosing a suitable 
linear registration, satisfies evaluation 
criteria even prior to a deformable 
registration and reduces the complexity. 
Next, two different non-linear registrations 
are applied: Parametric multi-level/multi-
resolution Bspline method (NL1) [6] and 
non-parametric diffeomorphic demons 
(NL2) [7]. In a multi-level/multi-resolution 
registration, the non-linear characterized 
Bspline registration is followed by the linear 
registration to roughly align the frames, 
while the non-linear registration is divided to 
coarse and fine resolution with respect to 
number of bspline grid points. Cost 
functions and registration parameters for 
each method are presented in Table 1, 2. 
In the non-parametric demons, by using 
gradient symmetric forces, the DF is 
obtained and it is warped onto the moving image, 
by minimizing the following energy functional: 
       
 
  
            
 
   
           
 
  
        
          represents geometric pixel disparity 
metric of fixed and moving image,             
considers a correspondence error between 
transformation c and deformation field s as 
Gaussian noise, hence, a Gaussian smooth kernel 
is applied on DF as a regularizer along with 
smoothness degree of deformation        leading 
to well-posed framework as well as a smoother 
vector field.   
 ,   
 ,   
  maintain the trade-off 
between the three terms in the cost function. In the 
demons algorithm, pixels representing the same 
homologous point on an object have the same 
intensity on both the fixed and moving images, 
hence well suited for the time-series thermogram 
registration. Significant parameters are listed in 
Table 2. 
 
Label Trans. Sim. Opt. Lvl./ 
Res. 
L1 Affine 
a
MMI 
b
GD - 
NL1 Bspline MMI 
c
LBFGS Multi/
Multi 
NL2 Demon 
d
GMS Iterative Single 
/Multi 
Table 1. Linear and deformable registrations. 
a
Mattes Mutual 
Information, 
b
Gradient Descent, 
c
Low-memory conjugate 
gradient descent. 
d
Geometric-Mean Squared Error. 
 
Label Iteration 
number 
Max 
step 
length 
Bspline 
grid 
points 
Reg. 
Time 
(min) 
L1 200 0.1 - 45 
NL1 
a
[20, 50] 10 
b
(15,30) 15 
NL2 40 - - 15 
Table 2. Registration parameters. Registrations are 
performed with Intel Core 2Duo 2.27 GHz CPU, 3GB RAM. 
a
[linear level, non-linear level], 
b
(coarse stage, fine stage). 
 
DAT Registration evaluation methods 
 
As a DAT specific evaluation, “symmetric 
alignment of breast boundaries assessment” of 
every frame is implemented. Next, image 
difference residual is calculated via NMI for all 
the sequentially warped thermograms.  
Finally, a qualitative evaluation of DF is 
performed to analyze how physically logic 
the transformation is computed. 
 
Categories of breast infrared subjects 
 
The infrared image sequences were acquired 
with an AIM256Q camera for 10s and the 
frame rate of 50 frames/s; hence, each 
sequence consisted of 476 thermal images of 
256 × 256 pixels, down quantized to 14 bits. 
The field of view was 38×38cm, element 
spacing of approximately 1.5x1.5mm. To 
perform repeatability evaluation, we 
acquired 4 subjects, each arbitrary acquired 
several times. Subject1 was acquired 4 
times, subject2 only once, subject3 and 
subject4 each three times, hence we totally 
have 11 dynamic breast cases for the 
experiments. 
 
Results and Discussion 
 
Inter-subject case-independent results 
We applied each registration method to all 
the subjects and evaluate the performance, 
based on the evaluation metrics provided. 
For each method, distribution of errors with 
respect to all cases is presented in Fig.1(a). 
Intra-subject case-dependent approach 
We evaluated the registration result of each 
method with respect to cases within each 
subject. Therefore we assessed the within-
subject distribution of the errors for each 
method. Fig.1(b) shows statistical 
distribution errors of NMI for all the method 
on only Subject1. 
Repeatability evaluation approach 
Base on breast boundary overlap error, we 
evaluate how well the dynamic frames of 
each case are aligned. Hence, distribution of 
the errors belonging to all the methods 
performed on each individual case is 
assessed, helping to know which case has 
the least movement and better recovery 
inside a subject. Fig.1(c) shows the errors 
distributed on each case. 
 
 
 
Fig.1. (a)Distribution of NMI error performed for each 
method as inter-subject evaluation. (Larger better) (b)NMI 
distribution as intra-subject evaluation for only Subject1.  
(c)Repeatability evaluation of every subject by breast 
boundary overlaps error criteria. (Smaller better) 
 
By referring to the results, for both inter-subject 
and intra-subject evaluation, Demons registration 
yielded both higher NMI and smaller breast 
boundary overlap error quantitatively (Fig.1). 
Fig.1(c) shows that there is some variation in 
subject1, with Case4 being relatively easy to 
register and Case1 the most difficult. In order to 
qualitatively evaluate the methods, Fig.2 illustrates 
the sequential final DF obtained for subject1-case1 
overlaid on the warped frames. DF in the demons 
method is the smoothest comparing to other 
methods, due to the fact that considering the 
temperature in a point at a given time, total 
derivative of temperature with respect to time is 
proportional to velocity of skin portion [3]. 
0,55 
0,65 
0,75 
0,85 
0,95 
L1 NL1 NL2 
In
te
r-
Su
b
je
ct
 N
M
I (
o
f 
1
) 
(a) 
0,55 
0,65 
0,75 
0,85 
0,95 
L1 NL1 NL2 
In
tr
a-
Su
b
je
ct
 N
M
I 
Su
b
1
(o
f 
1
) 
(b) 
0,01 
0,03 
0,05 
0,07 
0,09 
0,11 
Su
b
1
C
1
 
Su
b
1
C
2
 
Su
b
1
C
3
 
Su
b
1
C
4
 
Su
b
2
C
1
 
Su
b
3
C
1
 
Su
b
3
C
2
 
Su
b
3
C
3
 
Su
b
4
C
1
 
Su
b
4
C
2
 
Su
b
4
C
3
 
B
re
as
t 
B
o
u
n
d
ar
y(
m
m
) 
(C) 
Demons method, calculates the 2D velocity 
in the image, approximating image motion 
from sequential time-ordered images, which 
also guarantees the similarity of warped 
frames. Demons, assumes motion 
displacement but no intensity error, and uses 
Gaussian kernel to smooth the deformation 
field after each iteration, therefore, the 
corresponding points have the same 
intensity. The main criteria are dependent 
upon the local geometric variations of the 
images as well as the registration 
parameters. 
Finally, we compare the different methods. 
Table 3 presents average error for both NMI 
and breast boundary alignment, assigned for 
each method with respect to inter/intra 
subject evaluation for all the subjects. As 
concluded, demons algorithm shows better 
warped frames alignments. 
 
Label Intra-Subject Inter-Subject 
 Avg 
NMI 
Error 
Avg 
Breast 
Boundary 
Avg 
NMI 
Error 
Avg 
Breast 
Boundary 
L1 0,68 0,13 0,69 0,14 
NL1 0,75 0,043 0,75 0,044 
NL2 0,85 0,0013 0,84 0,0011 
Table 3. Method comparison. Average errors of each 
method with respect to evaluation metrics in terms of 
inter/intra subject variability. 
 
 
 
 
 
Conclusion 
 
Respiratory motion can be a significant effect 
when applying breast infrared thermography. We 
implemented time-series registration techniques in 
ITK, validating the methods by inter/intra 
repeatability evaluation approaches. Based on the 
results, demons registration due to homologous 
symmetric forces enforcing the pixel geometric 
disparities to be shortened on all the frames, 
excelled in terms of both frame similarity and 
smooth DF, comparing to other methods. 
Obtaining optimal DAT registration parameters is 
considered as a future work. 
 
References 
1. M. Anbar, L. Milescu, C. Brown, C. Carty, et al. Diagnosis of 
Breast Cancer with Infrared Dynamic Area Telethermometry 
(DAT). In proceeding of: Engineering in Medicine and Biology 
Society. Vol.2, pp.1215-1218, 2000. 
2. Y.L. Sant. An image registration method for infrared 
measurements. Quantitative InfraRed Thermography. Vol. 2(2), 
pp. 207-222, 2005. 
3. V. Agostini, S. Delsanto, F. Molinari, M. Knaflitz. Evaluation 
of feature-based registration in dynamic infrared imaging for 
breast cancer diagnosis. Conf Proc IEEE Eng Med Biol Soc. 
Vol. 1, pp. 953-956, 2006. 
4. L. Ibanez, W Schroeder. L. Ng, J Cates. The ITK software 
guide. Insight Toolkit Kitware Inc: 2003. 
5. N. Scales, C. Herry, M. Frize. Automated Image Segmentation 
for Breast Analysis using Infrared Images. Conf Proc IEEE Eng 
Med Biol Soc. Vol. 3, pp. 1737-40, 2004. 
6. S. Klein, M. Staring, J. P. W. Pluim. Evaluation of 
Optimization Methods for Nonrigid Medical Image 
Registration using Mutual Information and B-Splines, IEEE 
Transaction On Image Processing, Vol. 16, pp. 2879-2890, 
2007. 
7. T. Vercauteren, X. Pennec, et al. Non-parametric diffeomorphic 
image registration with the demons algorithm. Med Image 
Comput Comput Assist Interv. Vol. 10, pp. 319-326, 2007. 
Fig. 2. DF overlaid on warped images belonging to current frame of subject1-case1, in order to show the smoothness of 
final DF yielded for each method. 


An image registration method for infrared measurements.

Automated Image Segmentation for Breast Analysis using Infrared Images. Conf Proc

Diagnosis of Breast Cancer with Infrared Dynamic Area Telethermometry (DAT).

Evaluation of feature-based registration in dynamic infrared imaging for breast cancer diagnosis.

Evaluation of Optimization Methods for Nonrigid Medical Image Registration using Mutual Information and B-Splines,

Non-parametric diffeomorphic image registration with the demons algorithm.

The ITK software guide. Insight Toolkit Kitware Inc:

Evaluation of time-series registration methods in dynamic area telethermometry for breast cancer detection

Automated motion reduction in 3D dynamic infrared imaging is on demand in many applications. Few methods for registering time-series dynamic infrared frames have been proposed. Almost all such methods are feature based algorithms requiring manual intervention. We apply different automated registration methods based on spatial displacement to 11 datasets of Breast Dynamic Infrared Imaging (DIRI) and evaluate the results in terms of both the image similarity and anatomical consistency of the transformation. The aim is to optimize the registration strategy for breast DIRI in order to improve the spectral analysis of temperature modulation; thus facilitating the acquisition procedure in a Dynamic Area Telethermometry framework. The results show that symmetric diffeomorphic demons registration outperforms both warped frames similarity and smoothness of deformation fields; hence proving effective for time-series dynamic infrared registration

RIYAHI ALAM, MOHAMAD SADEGH

AGOSTINI, VALENTINA

MOLINARI, FILIPPO

KNAFLITZ, Marco

PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)

04 August 2020POLITECNICO DI TORINORepository ISTITUZIONALEEvaluation of time-series registration methods in dynamic area telethermometry for breast cancer detection / Riyahi-AlamS.; Agostini V.; Molinari F.; Knaflitz M.. - (2013), pp. 1-4. ((Intervento presentato al convegno Advanced InfraredTechnology and Applications (AITA) tenutosi a Turin nel September 10 - 13, 2013.OriginalEvaluation of time-series registration methods in dynamic area telethermometry for breast cancerdetectionPublisher:PublishedDOI:Terms of use:openAccessPublisher copyright(Article begins on next page)This article is made available under terms and conditions as specified in the  corresponding bibliographic description inthe repositoryAvailability:This version is available at: 11583/2508922 since:EVALUATION OF TIME-SERIES REGISTRATION METHODS IN DYNAMIC AREA TELETHERMOMETRY FOR BREAST CANCER DETECTION S. Riyahi-Alam1, V. Agostini2, F. Molinari3, M. Knaflitz4  1 Department of Mechanical and Aerospace, Politecnico di Torino, Torino, 10129, Italy, sadegh.riyahi@polito.it 2 Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy, valentina.agostini@polito.it 3 Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy, filippo.molinari@polito.it 4 Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy, marco.knaflitz@polito.it  Automated motion reduction in 3D dynamic infrared imaging is on demand in many applications. Few methods for registering time-series dynamic infrared frames have been proposed. Almost all such methods are feature based algorithms requiring manual intervention. We apply different automated registration methods based on spatial displacement to 11 datasets of Breast Dynamic Infrared Imaging (DIRI) and evaluate the results in terms of both the image similarity and anatomical consistency of the transformation. The aim is to optimize the registration strategy for breast DIRI in order to improve the spectral analysis of temperature modulation; thus facilitating the acquisition procedure in a Dynamic Area Telethermometry framework. The results show that symmetric diffeomorphic demons registration outperforms both warped frames similarity and smoothness of deformation fields; hence proving effective for time-series dynamic infrared registration.  Introduction Dynamic Area Telethermometry (DAT) can be useful in detecting breast cancer. The basic assumption is that normal tissues show temperature modulation that is different from cancerous tissue [1]. The surface temperature modulation caused by cancer, depends on specific aspects such as angiogenesis, local deoxygenation, and production of nitric monoxide [1]. Such physiological temperature modulation occurs at some specific frequencies [1]; hence, the spectral analysis of the time variations of the local temperature could allow for spotting cancerous lesions.  During a DAT examination, several frames of the patients’ breasts are acquired. Prior to spectral analysis, the patient movement must be eliminated and the sequential frames must be registered. The movements disarrange the time-temperature series of each pixel, thus originating thermal artifacts that might be interpreted as a false positive. Very few methods were proposed for automated dynamic infrared images registrations, all were concentrated on manual feature-based motion reduction [2],[3]. In a previous work from our team [3], a marker-based registration was applied on breast dynamic thermograms. However, there were some drawbacks (e.g. difficulty in manually placing the markers as well as deciding the optimal number of markers, additional prior marker detection algorithms, limitation on choosing types of registration parameters). The purpose of this paper is to perform different types of linear/non-linear intensity based registration methods (implemented in ITK [4]) on the healthy subjects, in order to improve the accuracy of the spectral analysis of temperature modulation. This should ease the patient acquisition procedure in a DAT framework with respect to the marker-based method.  To evaluate the performance of each registration method, we exploited breast boundary overlap along with Normalized Mutual Information (NMI), likewise final deformation field (DF) is demonstrated for visualizing misalignment errors. Materials and Methods  In time-series registration method, different frames inside a 3D thermogram are aligned, where the first frame is chosen as fixed and all the rest sequential frames as the moving image, while applying optimal parameters. In breast thermal images, intensities directly correspond to temperature, hence the similarity metrics must minimize the geometric displacement of the pixels, instead of the pixel intensities, otherwise leads to losing information.  Prior to experiments, the breast regions are segmented, using a semi-automated algorithm developed by N. Scales et al [5], while unsharp masking is applied to negate the noise and oversmoothness of interpolation.  Intensity based DAT registration methods  First, affine registration (L1) with the Mattes Mutual Information is applied as similarity metric. The registration parameters are listed in Table.1. Evaluating the linear registration is significant, since in many applications the sequences are not aligned in phase and single level linear registration could be satisfactory. Therefore choosing a suitable linear registration, satisfies evaluation criteria even prior to a deformable registration and reduces the complexity. Next, two different non-linear registrations are applied: Parametric multi-level/multi-resolution Bspline method (NL1) [6] and non-parametric diffeomorphic demons (NL2) [7]. In a multi-level/multi-resolution registration, the non-linear characterized Bspline registration is followed by the linear registration to roughly align the frames, while the non-linear registration is divided to coarse and fine resolution with respect to number of bspline grid points. Cost functions and registration parameters for each method are presented in Table 1, 2. In the non-parametric demons, by using gradient symmetric forces, the DF is obtained and it is warped onto the moving image, by minimizing the following energy functional:                                                           represents geometric pixel disparity metric of fixed and moving image,             considers a correspondence error between transformation c and deformation field s as Gaussian noise, hence, a Gaussian smooth kernel is applied on DF as a regularizer along with smoothness degree of deformation        leading to well-posed framework as well as a smoother vector field.    ,    ,     maintain the trade-off between the three terms in the cost function. In the demons algorithm, pixels representing the same homologous point on an object have the same intensity on both the fixed and moving images, hence well suited for the time-series thermogram registration. Significant parameters are listed in Table 2.  Label Trans. Sim. Opt. Lvl./ Res. L1 Affine aMMI bGD - NL1 Bspline MMI cLBFGS Multi/Multi NL2 Demon dGMS Iterative Single /Multi Table 1. Linear and deformable registrations. aMattes Mutual Information, bGradient Descent, cLow-memory conjugate gradient descent. dGeometric-Mean Squared Error.  Label Iteration number Max step length Bspline grid points Reg. Time (min) L1 200 0.1 - 45 NL1 a[20, 50] 10 b(15,30) 15 NL2 40 - - 15 Table 2. Registration parameters. Registrations are performed with Intel Core 2Duo 2.27 GHz CPU, 3GB RAM. a[linear level, non-linear level], b(coarse stage, fine stage).  DAT Registration evaluation methods  As a DAT specific evaluation, “symmetric alignment of breast boundaries assessment” of every frame is implemented. Next, image difference residual is calculated via NMI for all the sequentially warped thermograms.  Finally, a qualitative evaluation of DF is performed to analyze how physically logic the transformation is computed.  Categories of breast infrared subjects  The infrared image sequences were acquired with an AIM256Q camera for 10s and the frame rate of 50 frames/s; hence, each sequence consisted of 476 thermal images of 256 × 256 pixels, down quantized to 14 bits. The field of view was 38×38cm, element spacing of approximately 1.5x1.5mm. To perform repeatability evaluation, we acquired 4 subjects, each arbitrary acquired several times. Subject1 was acquired 4 times, subject2 only once, subject3 and subject4 each three times, hence we totally have 11 dynamic breast cases for the experiments.  Results and Discussion  Inter-subject case-independent results We applied each registration method to all the subjects and evaluate the performance, based on the evaluation metrics provided. For each method, distribution of errors with respect to all cases is presented in Fig.1(a). Intra-subject case-dependent approach We evaluated the registration result of each method with respect to cases within each subject. Therefore we assessed the within-subject distribution of the errors for each method. Fig.1(b) shows statistical distribution errors of NMI for all the method on only Subject1. Repeatability evaluation approach Base on breast boundary overlap error, we evaluate how well the dynamic frames of each case are aligned. Hence, distribution of the errors belonging to all the methods performed on each individual case is assessed, helping to know which case has the least movement and better recovery inside a subject. Fig.1(c) shows the errors distributed on each case.    Fig.1. (a)Distribution of NMI error performed for each method as inter-subject evaluation. (Larger better) (b)NMI distribution as intra-subject evaluation for only Subject1.  (c)Repeatability evaluation of every subject by breast boundary overlaps error criteria. (Smaller better)  By referring to the results, for both inter-subject and intra-subject evaluation, Demons registration yielded both higher NMI and smaller breast boundary overlap error quantitatively (Fig.1). Fig.1(c) shows that there is some variation in subject1, with Case4 being relatively easy to register and Case1 the most difficult. In order to qualitatively evaluate the methods, Fig.2 illustrates the sequential final DF obtained for subject1-case1 overlaid on the warped frames. DF in the demons method is the smoothest comparing to other methods, due to the fact that considering the temperature in a point at a given time, total derivative of temperature with respect to time is proportional to velocity of skin portion [3]. 0,55 0,65 0,75 0,85 0,95 L1 NL1 NL2 Inter-Subject NMI (of 1) (a) 0,55 0,65 0,75 0,85 0,95 L1 NL1 NL2 Intra-Subject NMI Sub1(of 1) (b) 0,01 0,03 0,05 0,07 0,09 0,11 Sub1C1 Sub1C2 Sub1C3 Sub1C4 Sub2C1 Sub3C1 Sub3C2 Sub3C3 Sub4C1 Sub4C2 Sub4C3 Breast Boundary(mm) (C) Demons method, calculates the 2D velocity in the image, approximating image motion from sequential time-ordered images, which also guarantees the similarity of warped frames. Demons, assumes motion displacement but no intensity error, and uses Gaussian kernel to smooth the deformation field after each iteration, therefore, the corresponding points have the same intensity. The main criteria are dependent upon the local geometric variations of the images as well as the registration parameters. Finally, we compare the different methods. Table 3 presents average error for both NMI and breast boundary alignment, assigned for each method with respect to inter/intra subject evaluation for all the subjects. As concluded, demons algorithm shows better warped frames alignments.  Label Intra-Subject Inter-Subject  Avg NMI Error Avg Breast Boundary Avg NMI Error Avg Breast Boundary L1 0,68 0,13 0,69 0,14 NL1 0,75 0,043 0,75 0,044 NL2 0,85 0,0013 0,84 0,0011 Table 3. Method comparison. Average errors of each method with respect to evaluation metrics in terms of inter/intra subject variability.      Conclusion  Respiratory motion can be a significant effect when applying breast infrared thermography. We implemented time-series registration techniques in ITK, validating the methods by inter/intra repeatability evaluation approaches. Based on the results, demons registration due to homologous symmetric forces enforcing the pixel geometric disparities to be shortened on all the frames, excelled in terms of both frame similarity and smooth DF, comparing to other methods. Obtaining optimal DAT registration parameters is considered as a future work.  References 1. M. Anbar, L. Milescu, C. Brown, C. Carty, et al. Diagnosis of Breast Cancer with Infrared Dynamic Area Telethermometry (DAT). In proceeding of: Engineering in Medicine and Biology Society. Vol.2, pp.1215-1218, 2000. 2. Y.L. Sant. An image registration method for infrared measurements. Quantitative InfraRed Thermography. Vol. 2(2), pp. 207-222, 2005. 3. V. Agostini, S. Delsanto, F. Molinari, M. Knaflitz. Evaluation of feature-based registration in dynamic infrared imaging for breast cancer diagnosis. Conf Proc IEEE Eng Med Biol Soc. Vol. 1, pp. 953-956, 2006. 4. L. Ibanez, W Schroeder. L. Ng, J Cates. The ITK software guide. Insight Toolkit Kitware Inc: 2003. 5. N. Scales, C. Herry, M. Frize. Automated Image Segmentation for Breast Analysis using Infrared Images. Conf Proc IEEE Eng Med Biol Soc. Vol. 3, pp. 1737-40, 2004. 6. S. Klein, M. Staring, J. P. W. Pluim. Evaluation of Optimization Methods for Nonrigid Medical Image Registration using Mutual Information and B-Splines, IEEE Transaction On Image Processing, Vol. 16, pp. 2879-2890, 2007. 7. T. Vercauteren, X. Pennec, et al. Non-parametric diffeomorphic image registration with the demons algorithm. Med Image Comput Comput Assist Interv. Vol. 10, pp. 319-326, 2007. Fig. 2. DF overlaid on warped images belonging to current frame of subject1-case1, in order to show the smoothness of final DF yielded for each method. 

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Evaluation of time-series registration methods in dynamic area telethermometry for breast cancer detection

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