Page 56 - Fister jr., Iztok, and Andrej Brodnik (eds.). StuCoSReC. Proceedings of the 2017 4th Student Computer Science Research Conference. Koper: University of Primorska Press, 2017
P. 56
ure 5: File selector for selecting image registra- the development of such procedure within the new image
tion procedures. registration environment was much faster and reliable.
Figure 6: Graphical user interface after image reg- 5. ACKNOWLEDGMENT
istration.
We thank Peter Rogelj, professor at University of Primorska,
tration results in the graphical user interface. Without any for assistance in the implementation of image registration
additional coding the graphical user interface displayed the environment and for the provided set of low level processing
imported medical images and provided all necessary tools functions that were implemented in past research activities.
for analyzing them. The testing phase was reasonably easier
because of the possibility to apply the registration results di- 6. REFERENCES
rectly on the loaded moving image inside the user interface.
The integrated toolbox helped to speed up the implemen- [1] M. D. Abra`moff, P. J. Magalha˜es, and S. J. Ram.
tation phase of the registration procedure. In the end we Image processing with imageJ, 2004.
only needed to focus on the implementation of three small
components. The first one was to controll the overall regis- [2] B. B. Avants, N. J. Tustison, M. Stauffer, G. Song,
tration workflow, the second was to compute the similarity B. Wu, and J. C. Gee. The Insight ToolKit image
between images and the last component was the optimiza- registration framework. Frontiers in Neuroinformatics,
tion process. To conclude, we shall sumarize our contribu- 8, 2014.
tion presented in the paper. We have implemented a new
image registration environment for Matlab. It offers sup- [3] C. Cocosco, V. Kollokian, R. K. Kwan, G. B. Pike,
port for development of image registration procedures. This and A. C. Evans. BrainWeb : Online Interface to a 3D
environment includes a graphical user interface that simpli- MRI Simulated Brain Database. 3-rd International
fies testing and application of image registration procedures Conference on Functional Mapping of the Human
as well as their development by incorporating a large set of Brain, 5(4):S425, 1997.
lower level processing functions. To provide a common way
for manipulating images and registration results it includes [4] A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet,
a well defined data structure. The main goal of this envi- J. C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings,
ronment is to provide a comprehensive solution to simplify F. Fennessy, M. Sonka, J. Buatti, S. Aylward, J. V.
and to speed-up the development of image registration pro- Miller, S. Pieper, and R. Kikinis. 3D Slicer as an
cedures. We have used this library in a new project in which image computing platform for the Quantitative
we implemented a rigid registration procedure for medical Imaging Network. Magnetic Resonance Imaging,
images. From the described results we can conclude that 30(9):1323–1341, 2012.
[5] U. Kumar, S. Soman, and Jayadeva. Benchmarking
NLopt and state-of-the-art algorithms for continuous
global optimization via IACOR. Swarm and
Evolutionary Computation, 27:116–131, 2016.
[6] C. S. Lent. Learning to Program with MATLAB
Building GUI Tools. John Wiley and Sons Inc., page
310, 2013.
[7] P. Mildenberger, M. Eichelberg, and E. Martin.
Introduction to the DICOM standard, 2002.
[8] D. Mistry, A. Banerjee, and A. Tatu. Image Similarity
based on Joint Entropy (Joint Histogram).
International Conference on Advances in Engineering
and Technology, (1):5, 2013.
[9] G. V. Rossum, P. S. Foundation, U. Swallow,
S. Python, P. Software, and F. License. Python (
programming language ). Flying, pages 1–14, 2011.
[10] W. J. Schroeder and K. M. Martin. The visualization
toolkit. In Visualization Handbook, pages 593–614.
2005.
[11] The Mathworks Inc. MATLAB - MathWorks, 2016.
[12] J. West, J. M. Fitzpatrick, M. Y. Wang, B. M.
Dawant, C. R. Maurer, R. M. Kessler, R. J. Maciunas,
C. Barillot, D. Lemoine, A. Collignon, F. Maes,
P. Suetens, D. Vandermeulen, P. A. van den Elsen,
S. Napel, T. S. Sumanaweera, B. Harkness, P. F.
Hemler, D. L. Hill, D. J. Hawkes, C. Studholme, J. B.
Maintz, M. A. Viergever, G. Malandain, and R. P.
Woods. Comparison and evaluation of retrospective
intermodality image registration techniques.
International Society for Optics and Photonics, pages
332–347, 1996.
StuCoSReC Proceedings of the 2017 4th Student Computer Science Research Conference 56
Ljubljana, Slovenia, 11 October
tion procedures. registration environment was much faster and reliable.
Figure 6: Graphical user interface after image reg- 5. ACKNOWLEDGMENT
istration.
We thank Peter Rogelj, professor at University of Primorska,
tration results in the graphical user interface. Without any for assistance in the implementation of image registration
additional coding the graphical user interface displayed the environment and for the provided set of low level processing
imported medical images and provided all necessary tools functions that were implemented in past research activities.
for analyzing them. The testing phase was reasonably easier
because of the possibility to apply the registration results di- 6. REFERENCES
rectly on the loaded moving image inside the user interface.
The integrated toolbox helped to speed up the implemen- [1] M. D. Abra`moff, P. J. Magalha˜es, and S. J. Ram.
tation phase of the registration procedure. In the end we Image processing with imageJ, 2004.
only needed to focus on the implementation of three small
components. The first one was to controll the overall regis- [2] B. B. Avants, N. J. Tustison, M. Stauffer, G. Song,
tration workflow, the second was to compute the similarity B. Wu, and J. C. Gee. The Insight ToolKit image
between images and the last component was the optimiza- registration framework. Frontiers in Neuroinformatics,
tion process. To conclude, we shall sumarize our contribu- 8, 2014.
tion presented in the paper. We have implemented a new
image registration environment for Matlab. It offers sup- [3] C. Cocosco, V. Kollokian, R. K. Kwan, G. B. Pike,
port for development of image registration procedures. This and A. C. Evans. BrainWeb : Online Interface to a 3D
environment includes a graphical user interface that simpli- MRI Simulated Brain Database. 3-rd International
fies testing and application of image registration procedures Conference on Functional Mapping of the Human
as well as their development by incorporating a large set of Brain, 5(4):S425, 1997.
lower level processing functions. To provide a common way
for manipulating images and registration results it includes [4] A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet,
a well defined data structure. The main goal of this envi- J. C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings,
ronment is to provide a comprehensive solution to simplify F. Fennessy, M. Sonka, J. Buatti, S. Aylward, J. V.
and to speed-up the development of image registration pro- Miller, S. Pieper, and R. Kikinis. 3D Slicer as an
cedures. We have used this library in a new project in which image computing platform for the Quantitative
we implemented a rigid registration procedure for medical Imaging Network. Magnetic Resonance Imaging,
images. From the described results we can conclude that 30(9):1323–1341, 2012.
[5] U. Kumar, S. Soman, and Jayadeva. Benchmarking
NLopt and state-of-the-art algorithms for continuous
global optimization via IACOR. Swarm and
Evolutionary Computation, 27:116–131, 2016.
[6] C. S. Lent. Learning to Program with MATLAB
Building GUI Tools. John Wiley and Sons Inc., page
310, 2013.
[7] P. Mildenberger, M. Eichelberg, and E. Martin.
Introduction to the DICOM standard, 2002.
[8] D. Mistry, A. Banerjee, and A. Tatu. Image Similarity
based on Joint Entropy (Joint Histogram).
International Conference on Advances in Engineering
and Technology, (1):5, 2013.
[9] G. V. Rossum, P. S. Foundation, U. Swallow,
S. Python, P. Software, and F. License. Python (
programming language ). Flying, pages 1–14, 2011.
[10] W. J. Schroeder and K. M. Martin. The visualization
toolkit. In Visualization Handbook, pages 593–614.
2005.
[11] The Mathworks Inc. MATLAB - MathWorks, 2016.
[12] J. West, J. M. Fitzpatrick, M. Y. Wang, B. M.
Dawant, C. R. Maurer, R. M. Kessler, R. J. Maciunas,
C. Barillot, D. Lemoine, A. Collignon, F. Maes,
P. Suetens, D. Vandermeulen, P. A. van den Elsen,
S. Napel, T. S. Sumanaweera, B. Harkness, P. F.
Hemler, D. L. Hill, D. J. Hawkes, C. Studholme, J. B.
Maintz, M. A. Viergever, G. Malandain, and R. P.
Woods. Comparison and evaluation of retrospective
intermodality image registration techniques.
International Society for Optics and Photonics, pages
332–347, 1996.
StuCoSReC Proceedings of the 2017 4th Student Computer Science Research Conference 56
Ljubljana, Slovenia, 11 October
