@InProceedings{8081577,
  author = {Y. Li and B. Sixou and F. Peyrin},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Estimation of the blurring kernel in experimental HR-pQCT images based on mutual information},
  year = {2017},
  pages = {2086-2090},
  abstract = {The analysis of trabecular bone micro structure from in-vivo CT images is still limited due to limited spatial resolution even with the new High Resolution peripheral Quantitative CT (HR-pQCT) scanners. In previous works, it has been proposed to exploit super resolution techniques to improve spatial resolution. However, the application of such methods requires to know the blurring kernel, which is challenging for experimental HR-pQCT images. The goal of this work is to determine the blurring kernel of these scanners in order to facilitate an increase of the resolution of the bone images and of the segmentation of the bone structures. To this aim, we propose a method based on mutual information and compare it with classical ¿2-norm minimization methods.},
  keywords = {bone;computerised tomography;image resolution;image segmentation;medical image processing;super resolution techniques;blurring kernel estimation;high resolution peripheral quantitative CT scanners;spatial resolution techniques;trabecular bone microstructure segmentation;Kernel;Signal resolution;Spatial resolution;Bones;Computed tomography;TV;Deconvolution;super-resolution;Total Variation;3D CT images;bone micro-architecture},
  doi = {10.23919/EUSIPCO.2017.8081577},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570344934.pdf},
}

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In previous works, it has been proposed to exploit super resolution techniques to improve spatial resolution. However, the application of such methods requires to know the blurring kernel, which is challenging for experimental HR-pQCT images. The goal of this work is to determine the blurring kernel of these scanners in order to facilitate an increase of the resolution of the bone images and of the segmentation of the bone structures. To this aim, we propose a method based on mutual information and compare it with classical ¿2-norm minimization methods.","keywords":"bone;computerised tomography;image resolution;image segmentation;medical image processing;super resolution techniques;blurring kernel estimation;high resolution peripheral quantitative CT scanners;spatial resolution techniques;trabecular bone microstructure segmentation;Kernel;Signal resolution;Spatial resolution;Bones;Computed tomography;TV;Deconvolution;super-resolution;Total Variation;3D CT images;bone micro-architecture","doi":"10.23919/EUSIPCO.2017.8081577","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570344934.pdf","bibtex":"@InProceedings{8081577,\n author = {Y. Li and B. Sixou and F. Peyrin},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Estimation of the blurring kernel in experimental HR-pQCT images based on mutual information},\n year = {2017},\n pages = {2086-2090},\n abstract = {The analysis of trabecular bone micro structure from in-vivo CT images is still limited due to limited spatial resolution even with the new High Resolution peripheral Quantitative CT (HR-pQCT) scanners. In previous works, it has been proposed to exploit super resolution techniques to improve spatial resolution. However, the application of such methods requires to know the blurring kernel, which is challenging for experimental HR-pQCT images. The goal of this work is to determine the blurring kernel of these scanners in order to facilitate an increase of the resolution of the bone images and of the segmentation of the bone structures. 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