Sadegh Riyahi Alam1, Valentina Agostini2, Filippo Molinari2, Marco Knaflitz2

1Department of Mechanics and Aerospace, Politecnico di Torino, Torino, 10129, Italy 2Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy

AITA 2013 – Conference of Advanced Infrared Technology and Applications

12th, September 2013

Introduction

Dynamic Area Telethermometry (DAT) Framework for breast cancer detection

Problems and aims of the work

Methodology and Implementation

Implemented DAT specific time-series registration methods

DAT specific registration evaluation methods

Experimental datasets and optimized registration parameters

Results and discussions

Qualitative and quantitative evaluation of the results

Inter-subject/Intra-subject statistical analysis

Conclusions and Future work

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Intro: DAT framework for Breast Cancer Detection

Time-Series registration

Dynamic Telethermometry Area Segmentation

t2 t1

t3 t4 t5

Power Spectral Density

Final Spectral Image

Problems Movement artifact: The movements disarrange the time-temperature series of each

pixel, thus originating thermal artifacts that might be interpreted as a false positive.

Time

t1 t2 t4 t3

temperature

Marker-based image registration (Agostini, Delsanto, Molinari and Knaflitz, 2006 p. 953)

Difficulty in manually placing the markers Optimal number of markers Additional prior marker detection algorithms Limitation on choosing types of registration parameters

t1 t2 t4 t3

Acquisition Infrared 4

Aims of the work

Improve the accuracy of the further spectral analysis of temperature modulation in a DAT framework. (markers interfere the spectral analysis procedure)

Facilitate the patient acquisition procedure.

Performing different Automated Marker-less Intensity based image registration methods on DAT data.

To obtain the best suited registration method

To optimize the registration parameters

Marker-based spectral image Marker-less spectral image 5

Method: Time-Series 3D Image Registration One image (Fixed) is aligned on another image (Moving) by fitting the

transformation that optimizes the Cost function including similarity measure between the two images.

The solution: Deformation Field A vector field represents the transformation

parameters.

Displacement difference of each corresponding pixels on fixed and moving frames.

Deployed for registration evaluation.

(Ibanez, Schroeder. Ng and Cates, 2003 ITK software guide)

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DAT specific Registration methods Affine linear registration

Rotation, translation, shear, scale.

Compensate global linear geometric deformations.

Parametric BSpline non-linear registration

N times differentiable Polynomial parametric Bspline function.

Number of Grid points influences the multi level registration.

Compensate localized non-linear displacements.

Non parametric diffeomorphic Demons non-linear registration

Obtain velocity field using gradient symmetric forces.

Gaussian smooth kernel as additional regularizer.

Fits for time-series sequential registration.

Transformation Similarity Measure

Optimization Level/ Resolution

Interpolation

Affine Mutual Information

Gradient Descent - Linear

BSpline Mutual Information

LBFGS Multi/Multi Linear

Demons Geometric Mean Square

Iterative Single /Multi Nearest Neighbor

Local forces proportional to fixed frame gradient

Gaussian smooth kernel Warped frame Moving frame

N tim

es

(Vercauteren, Pennec, Perchant and Ayache. 2008 p.754)

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Evaluation methods Image difference residual, Root Mean Square difference of Normalized

Mutual Information (NMI) between the frames

The larger the better (out of 1)

Symmetric alignment of breast boundaries assessment (DAT Specific)

1. Sum of absolute difference of Center Of Mass (C.O.M) distance on all the warped frames.

2. Sum of absolute differences of the strong gradients divided by the number of voxels. (Canny edge detection)

Smaller the better (Close to ZERO)

Jacobian determinant of Deformation Field. i.e. Singularities in deformation fields.

The overall percentage of negative jacobian values on all the sequential Deformation Fields

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Dataset and optimized DAT registration parameters High speed thermo camera AIM LWIR QWIP 256, acquired for 10 s.

Frame rate of 50 frames/s.

Each sequence consisted of 476 thermal images of 256 × 256 pixels, down quantized to 14 bits.

Field of view was 38×38cm, element spacing of 1.5x1.5mm.

We acquired 4 subjects arbitrary

Subject1: 4 cases

Subject2: One case

Subject3 and Subject4: Each three cases

Label Iteration number Max step length Number of Bspline grid

points

Reg. Time (min)

Affine 200 0.1 - 45

BSpline [20, 50] 10 (15,30) 15

Demons 40 - - 8

11 dynamic breast cases

Qualitative and Quantitative analysis Inter-subject/Intra-subject evaluation strategy

Intel Core 2Duo 2.27 GHz CPU, 3GB RAM

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Results: Qualitative Evaluation: Warped frames before and after • Movement of warped frames on Case 1 for each method

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Visualized Deformation Field evaluation • Movement of DF overlaid on warped frames for each method: Case1

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Quantitative evaluation: Breast Boundary Overlap: before and after (Case1 and Case2)

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

BSpline on Case1

1 476

Frames 0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

Demons on Case1

1 476

Frames 0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

0,18

Affine on Case1

1 476

Frames

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

BSpline on Case2

1 476

Frames 0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

Affine on Case2

1 476

Frames 0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

Demons on Case2

1 476

Frames

Before Registration

After Registration

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Quantitative evaluation: NMI: before and after (Case1 and Case2)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

BSpline on Case1

1 476

Frames 0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Demons on Case1

1 476

Frames 0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Affine on Case1

1 476

Frames

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

BSpline on Case2

1 476

Frames 0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Demons on Case2

1 476

Frames 0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Affine on Case2

1 476

Frames

Before Registration

After Registration

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Inter-subject/Intra-subject evaluation

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

0,1

Affine BSpline Demons

Inte

r-su

bje

ct B

reas

t O

verl

ap E

rro

rs

0

0,02

0,04

0,06

0,08

0,1

0,12

Affine BSpline Demons

Intr

a-s

ub

ject

Bre

ast O

verl

ap E

rro

rs (S

ub

1)

0

1

2

3

4

5

6

7

8

9

10

Affine BSpline Demons

Ne

gati

ve Ja

cob

ian

Val

ue

s (%

)

0

2

4

6

8

10

12

14

16

18

20

Affine Bspline Demons

Neg

ati

ve Ja

cob

ian

Val

ues

(%)

0,55

0,6

0,65

0,7

0,75

0,8

0,85

0,9

0,95

Affine BSpline Demons

Intr

a-S

ub

ject

NM

I (Su

b1

)

0,55

0,6

0,65

0,7

0,75

0,8

0,85

0,9

0,95

Affine BSpline Demons

Inte

r-Su

bje

ct N

MI

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Conclusion

Respiratory motion can be a significant effect when applying breast infrared thermography. Motion artifacts.

Marker-based registration.

We implemented time-series registration techniques in ITK, i.e. affine, Bspline and Demons registration methods and applying to thermograms cases we acquired then validating the methods by inter/intra repeatability evaluation approaches.

Demons method acts as the best method for time-series motion reduction in DAT due to homologous symmetric forces, excelled comparing to other methods.

Future work: Application performance optimization. i.e. From CPU to GPU and DAT registration parameter optimization framework.

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References

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.

L. Ibanez, W Schroeder. L. Ng, J Cates. The ITK software guide. Insight Toolkit Kitware Inc: 2003.

T. Vercauteren, X. Pennec, A. Perchant, N. Ayache. Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach. Medical Image Computing and Computer-Assisted Intervention , Vol. 5241, pp. 754-761, 2008.

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Thanks for your attention!!!

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