Publications

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A. Adibi, Karami, A., Heylen, R., and Scheunders, P., “Optical Solutions for Improving Spatial Resolution of Hyperspectral Sensors”, in 8th workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing, Los Angeles, USA, 2016.
M. A. Akhter, Heylen, R., and Scheunders, P., “Geometric matched filter for hyperspectral partial unmixing”, in IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse, 2014.
M. A. Akhter, Heylen, R., and Scheunders, P., “Hyperspectral unmixing with projection onto convex sets using distance geometry”, in IEEE IGARSS 2015, International Geoscience and Remote Sensing Symposium, Milan, Italy, July 26-31, 2015, pp. 5059-5062.
M. A. Akhter, Mahmood, Z., and Scheunders, P., “Hyperspectral image subpixel mapping using Getis index”, in IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013, 2013.
L. F. Alves Pereira, Janssens, E., Van Dael, M., Verboven, P., Nicolai, B., Cavalcanti, G. D. C., Tsang, I. J., and Sijbers, J., “Fast X-ray Computed Tomography via Image Completion”, in 6th Conference on Industrial Computed Tomography(iCT), Wels, Austria, 2016, pp. 1-5.
L. F. Alves Pereira, Roelandts, T., and Sijbers, J., “Inline 3D X-ray Inspection of Food using Discrete Tomography”, in InsideFood Symposium, Leuven, Belgium, 2013.PDF icon Download paper (292.06 KB)
L. F. Alves Pereira, De Beenhouwer, J., Kastner, J., and Sijbers, J., “Extreme Sparse X-ray Computed Laminography Via Convolutional Neural Networks”, in ICTAI 2020, 2020.PDF icon Download paper (2.5 MB)
L. F. Alves Pereira, De Beenhouwer, J., and Sijbers, J., “Sparse-view Medical Tomosynthesis via Mixed Scale Dense Convolutional Framelet Networks”, in IEEE International Symposium on Biomedical Imaging (ISBI), 2023, pp. 880-884.
L. F. Alves Pereira, Dabravolski, A., Tsang, I. R., Cavalcanti, G. D. C., and Sijbers, J., “Conveyor belt X-ray CT using Domain Constrained Discrete Tomography”, in Sibgrapi conference on Graphics, Patterns and Images, 2014, pp. 290 - 297.PDF icon Download paper (3.45 MB)
L. F. Alves Pereira, Van Nieuwenhove, V., De Beenhouwer, J., and Sijbers, J., “A Deep Convolutional Framelet Network based on Tight Steerable Wavelet: application to sparse-view medical tomosynthesis”, in International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021.
L. F. Alves Pereira, De Beenhouwer, J., and Sijbers, J., “The Deep Steerable Convolutional Framelet Network for Suppressing Directional Artifacts in X-ray Tomosynthesis”, in 31st European Signal Processing Conference, EUSIPCO, 2023.
V. Andrejchenko, Zahiri, Z., Heylen, R., and Scheunders, P., “A spectral mixing model accounting for multiple reflections and shadow”, in IGARSS 2019, International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 286-289.
V. Andrejchenko, Heylen, R., Liao, W., Philips, W., and Scheunders, P., “MRF-based decision fusion for hyperspectral image classification”, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018.
V. Andrejchenko, Heylen, R., Scheunders, P., Philips, W., and Liao, W., “Classification of hyperspectral images with very small training size using sparse unmixing”, in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Inze, D., and Scheunders, P., “Analysis of Plant Stress Response Using Hyperspectral Imaging and Kernel Ridge Regression”, in 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, Singapore, 2022, vol. 829.
M. Shahrimie Asaari, Mishra, P., Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., “Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging”, in 8th WHISPERS - Evolution in Remote Sensing, Los Angeles, USA, 2016.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Wuyts, N., and Scheunders, P., “Detection of plant responses to drought using close-range hyperspectral imaging in a high-throughput phenotyping platform”, in IEEE Whispers 2018, Workshop on Hyperspectral Image and Signal Processing, Amsterdam, 23-26 September , 2018.
B. Auer, De Beenhouwer, J., Zeraatkar, N., Kuo, P. H., Furenlid, L. R., and King, M. A., “Preliminary investigation of attenuation and scatter correction strategies for a next-generation SPECT system dedicated to quantitative clinical brain imaging”, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Manchester, UK, 2019.
B. Auer, Zeraatkar, N., De Beenhouwer, J., Kalluri, K., Kuo, P. H., Furenlid, L. R., and King, M. A., “Investigation of a Monte Carlo simulation and an analytic-based approach for modeling the system response for clinical I-123 brain SPECT imaging”, in 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, vol. 11072, pp. 187 – 190.
B. Auer, De Beenhouwer, J., Fromme, T. J., Kalluri, K., Goding, J. C., Zubal, G. I., Furenlid, L. R., and King, M. A., “Preliminary investigation of design parameters of an innovative multi- pinhole system dedicated to brain SPECT imaging”, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Sydney, Australia, 2018.
B. Auer, Könik, A., Fromme, T. J., Kalluri, K., De Beenhouwer, J., Zubal, G. I., Furenlid, L. R., and King, M. A., “Preliminary evaluation of surface mesh modeling of system geometry, anatomy phantom, and source activity for GATE simulations”, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Sydney, Australia, 2018.
B. Auer, Kalluri, K., Abayazeed, A. H., De Beenhouwer, J., Zeraatkar, N., Lindsay, C., Momsen, N., R. Richards, G., May, M., Kupinski, M. A., Kuo, P. H., Furenlid, L. R., and King, M. A., “Aperture size selection for improved brain tumor detection and quantification in multi-pinhole 123I-CLINDE SPECT imaging”, in IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, USA (2020), 2020.
B
K. J. Batenburg and Sijbers, J., “Automatic local thresholding of tomographic reconstructions based on the projection data”, in SPIE Medical Imaging, San Diego, CA, USA, 2008, vol. 6913, p. 69132.PDF icon Download full paper (857.39 KB)
K. J. Batenburg and Sijbers, J., “Automatic multiple threshold scheme for segmentation of tomograms”, in Proceedings of SPIE Medical Imaging: Physics of Medical Imaging, San Diego, CA, USA, 2007.PDF icon Download full paper (630.82 KB)
K. J. Batenburg, Sijbers, J., and Van de Casteele, E., “Automatic multiple threshold scheme for segmentation of tomograms”, in Proceedings of the Biomedical Engineering IEEE/EMBS Benelux Symposium, Brussels, Belgium, 2006, vol. 2, pp. 143-146.

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