Comparison of time-series registration methods in breast dynamic infrared imaging

• Autorzy: Riyahi-Alam S. , Agostini V. , Molinari F. , Knaflitz M.

Identyfikatory

DOI

10.1515/oere-2015-0003

• Konferencja: Advanced Infrared Technology and Applications - AITA 2013 (12 ; 10-13.09.2013 ; Turin, Italy)
• Języki publikacji: EN

Abstrakty

EN

Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons’ registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons’ registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation.

Słowa kluczowe

EN

breast cancer detection; dynamic area telethermometry; DAT; dynamic infrared imaging; DIRI; time-series infrared images; image registration; temperature modulation spectral analysis

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2015
• Tom: Vol. 23, No. 1
• Strony: 66--75
• Opis fizyczny: Bibliogr. 17 poz., il., tab., wykr.

Twórcy

autor

Riyahi-Alam S.

• Department of Mechanical and Aerospace, Politecnico di Torino, 24 Corso Duca degli Abruzzi, 10129, Trino, 10129, Italy

autor

Agostini V.

• Department of Electronics and Telecommunications, Politecnico di Torino, 24 Corso Duca degli Abruzzi, 10129, Torino, Italy

autor

Molinari F.

• Department of Electronics and Telecommunications, Politecnico di Torino, 24 Corso Duca degli Abruzzi, 10129, Torino, Italy

autor

Knaflitz M.

• Department of Electronics and Telecommunications, Politecnico di Torino, 24 Corso Duca degli Abruzzi, 10129, Torino, Italy

Bibliografia

• 1. A.G. Hauss and M.J. Yaffe, “A categorical course in physics: technical aspects of breast imaging”, 78th Scientific Assembly and Annual Meeting o f Radiological Society o f North America, Chicago, 1994.
• 2. M. Anbar, L. Mileseu, C. Brown, C. Carty, A. Naumov, E. Bachman, K. AlDulaimy, C. Geronimo, and T. Button, “Diagnosis of breast cancer with infrared dynamic area telethermometry (DAT)”, IEEE Eng. Med. Biol. Soc. 2, 1215-1218 (2000).
• 3. R. Joro, AL. Liiaperi, S. Soimakallio, R. Jiirvenpaa, T. Kuukasjiirvi, T. Toivonen, R. Saaristo, and P. Dastidar, “Dynamic infrared imaging in identification of breast cancer tissue with combined image processing and frequency analysis”, Med. Eng. Technol. 32, 325-35 (2008).
• 4. V. Agostini, S. Delsanto, M. Knaflitz, and F. Molinari, “Communications noise estimation in infrared image sequences: A tool for the quantitative evaluation of the effectiveness of registration algorithms”, IEEE T. Bio-Med. Eng. 55, 1917-1920 (2008).
• 5. S. Riyahi-Alam, M. Peroni, G. Baroni, and M. Riboldi, “Regularization in deformable registration of biomedical images based on divergence and curl operators”, Method. Inform. Med. 53,21-28 (2014).
• 6. Y.L. Sant, “An image registration method for infrared measurements”, Quantitative InfraRed Thermography. 2, 207-222 (2005).
• 7. V. Agostini, S. Delsanto, F. Molinari, and M. Knaflitz, “Evaluation of feature-based registration in dynamic infrared imaging for breast cancer diagnosis”, IEEE Eng. Med. Biol. Soc. 1, 953-956 (2006).
• 8. V. Agostini, M. Knatlitz, and F. Molinari, “Motion artifact reduction in breast dynamic infrared imaging”. IEEE T. Bio-Med. Eng. 56, 903-906 (2009).
• 9. L. Ibanez, W Schroeder. L. Ng, and J Cates. The ITK Software Guide. Insight Toolkit Kitware Inc, 2003.
• 10. N. Scales, C. Herry, and M. Frize, “Automated image segmentation for breast analysis using infrared images”, IEEE Eng. Med. Bio. Soc. 3, 1737-40 (2004).
• 11. 3D Slieer. |Internet]. Available from http://www.slicer.org.
• 12. J.V. Hajnal and D.L.G. Hill, Medical Image Registration, CRC Press, pp. 46-50, 2001.
• 13. S. Klein, M. Staring, and J.P. W. Pluim, “Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines”, IEEE T. Image Proces. 16, 2879-2890 (2007).
• 14. T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, “Non-parametric diffeomorphic image registration with the demons algorithm”, Proc. MICCAI10, 319-326 (2007).
• 15. G.E. Christensen and H.J. Johnson, “Consistent image registration”, IEEE T. Med. Imaging 20, 568-582 (2002).
• 16. T.M. Button, H. Li, P. Fisher, R. Rosenblatt, K. Dulaimy, S. Li, B. O’Hea, M. Salvitti, V. Geronimo, C. Geronimo, S. Jambawalikar, P. Carvelli, and R. Weiss, “Dynamic infrared imaging for the detection of malignancy”, Phys. Med. Biol. 49,3105-3116(2004).
• 17. G.E. Christensen, “Consistent linear-elastic transformations for image matching”, Information Processing In Medical Imaging, LNCS 1613, 224-237 (1999).