Comptes Rendus
Numerical Analysis
Fast semi-automatic segmentation based on reduced basis
Comptes Rendus. Mathématique, Volume 358 (2020) no. 9-10, pp. 981-987.

This note adresses the following segmentation problem in medical imaging: minimize expert intervention for semi-automatic segmentation process. Using a reduced basis, we have an a priori knowledge of the objet we want to identify on the images, like a muscle on a CT-Scan. We just have to identify the coefficients associated to the object of interest in the reduced basis, by solving a linear system taking as input the coordinates of some selected points in the image. An example implemented in 2D is shown. This method is independent of the grayscale of the image, and can therefore be applied to all objects and images.

Nous présentons ici une méthode de segmentation d’imagerie médicale semi-automatique minimisant l’intervention d’un expert. A l’aide d’une base réduite, nous connaissons a priori la forme de l’objet à identifier sur les images, comme un muscle sur un scanner. Il suffit d’identifier les coefficients associés à l’objet d’intérêt dans la base réduite, via la résolution d’un système linéaire prenant en entrée les coordonnées de quelques points sélectionnés sur l’image. Un exemple implémenté en 2D est proposé. Cette méthode est indépendante des niveaux de gris de l’image, et peut donc être appliquée sur tous objets et toutes imageries.

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Published online:
DOI: 10.5802/crmath.89

Damiano Lombardi 1; Yvon Maday 2; Lydie Uro 3

1 COMMEDIA, Inria Paris, 2 rue Simone Iff, 75012, Paris, et Sorbonne Université, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France
2 Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris et Institut Universitaire de France, France
3 Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), F-75005 Paris, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
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Damiano Lombardi; Yvon Maday; Lydie Uro. Fast semi-automatic segmentation based on reduced basis. Comptes Rendus. Mathématique, Volume 358 (2020) no. 9-10, pp. 981-987. doi : 10.5802/crmath.89. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.89/

[1] Siqi Bao; Albert C. S. Chung Multi-scale structured cnn with label consistency for brain mr image segmentation, Comput. Methods Biomech. Biomed. Eng. Imaging. Vis., Volume 6 (2018) no. 1, p. 113--117 | DOI

[2] F. Bernard; L. Salamanca; J. Thunberg; A. Tack; D. Jentsch; H. Lamecker; S. Zachow; F. Hertel; J. Goncalves; P. Gemmar Shape-aware surface reconstruction from sparse 3d point-clouds, Med. Image Anal., Volume 38 (2017), pp. 77-89 | DOI

[3] Alexander de Brebisson; Giovanni Montana Deep neural networks for anatomical brain segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2015), pp. 20-28

[4] Tobias Heimann; Hans-Peter Meinzer Statistical shape models for 3d medical image segmentation: a review, Med Image Anal, Volume 13 (2009) no. 4, pp. 543-563 | DOI

[5] Juan Eugenio Iglesias; Mert R. Sabuncu Multi-atlas segmentation of biomedical images: a survey, Med Image Anal, Volume 24 (2015) no. 1, pp. 205-219 | DOI

[6] Yan Kang; Klaus Engelke; Willi A. Kalender A new accurate and precise 3-d segmentation method for skeletal structures in volumetric ct data, IEEE transactions on medical imaging, Volume 22 (2003) no. 5, pp. 586-598 | DOI

[7] Geert Litjens; Thijs Kooi; Babak Ehteshami Bejnordi; Arnaud A. A. Setio; Francesco Ciompi; Mohsen Ghafoorian; Jeroen A.W.M. Van Der Laak; Bram Van Ginneken; Clara I. Sánchez A survey on deep learning in medical image analysis, Med. Image Anal., Volume 42 (2017), pp. 60-88 | DOI

[8] Antonino M. López; Felipe Lumbreras; Joan Serrat; Juan J. Villanueva Evaluation of methods for ridge and valley detection, IEEE Trans. Pattern Anal. Mach. Intell., Volume 21 (1999) no. 4, pp. 327-335 | DOI

[9] Dzung L. Pham; Chenyang Xu; Jerry L. Prince Current methods in medical image segmentation, Annu Rev Biomed Eng, Volume 2 (2000) no. 1, pp. 315-337 | DOI

[10] Dzung L. Pham; Chenyang Xu; Jerry L. Prince Current methods in medical image segmentation, Annu Rev Biomed Eng, Volume 2 (2000) no. 1, pp. 315-337 | DOI

[11] Mohd Shafry Mohd Rahim; Alireza Norouzi; Amjad Rehman; Tanzila Saba 3d bones segmentation based on ct images visualization, Biomedical Research, Volume 28 (2017) no. 8, pp. 3641-3644

[12] N. Ramesh; J.-H. Yoo; I. Sethi Thresholding based on histogram approximation, IEE Proceedings-Vision, Image and Signal Processing, Volume 142 (1995) no. 5, pp. 271-279 | DOI

[13] Neeraj Sharma; Lalit M. Aggarwal Automated medical image segmentation techniques, J. Med. Phys., Volume 35 (2010) no. 1, pp. 3-14 | DOI | Zbl

[14] T. S. Spisz; I. Bankman Handbook of medical imaging, Academic Press, 2000

[15] D. J. Withey; Zoltan J. Koles Medical image segmentation: Methods and software, 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, IEEE, 2007, pp. 140-143 | DOI

[16] J. Zhang; C.-H. Yan; C.-K. Chui; S.-H. Ong Fast segmentation of bone in ct images using 3d adaptive thresholding, Computers in biology and medicine, Volume 40 (2010) no. 2, pp. 231-236 | DOI

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