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Three dimensional visualization of histopathological data

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The histopathological examination provides information about the spatial assessment of pathological changes in the tissue. The authors present a method of extending this histopathological spatial assessment with a 3D view consisting of images of microscopic layers. The proposed solution creates 3D models based on images obtained from the database of the Medical University of Gdańsk (Digital Pathology). First, a series of medical images related to the study of a specific pathological tissue undergoes a process of background detection and removal through an algorithm. Next, images aligned with each other. Then, two types of 3D models are created: 1) classical model with Marching Cubes algorithm and 2) the use Cloud of Points.
Rocznik
Strony
1--11
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
  • Gdansk University of Technology, Centre of Informatics – Tricity Academic Supercomputer Network
Bibliografia
  • [1] M. Bolcewicz, J. Gulczyński, K. Jendernalik, L. Kalinowski,A. Lewandowska, J. Skokowski, and T. van de Wetering, “The digital tissue and cell atlas and the virtual microscope,”Sharing research data across disciplines, vol. 1, pp. 61–69, 01 2022.
  • [2] D. Mason and contributors, “pydicom: DICOM for Python. ”https://pydicom.github.io/pydicom/stable/, 2023.Ac-cessed: 02.06.2023.
  • [3] H. R. Gamba, P. Nohama, A. A. C. Paz, I. J. Sanches, and M. A.Souza, “3d thermal medical image visualization tool: Integration between MRI and thermographic images,” in 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. s. 61–64, IEEE, 08 2014.
  • [4] J. Cychnerski, K. Kaczor, A. Kwaśniewska, P. Nadachowski,M. Operlejn, A. Piastowski, and M. Zielonka, “Comparison of image pre-processing methods in liver segmentation task,” in 15th International Conference on Human System Interaction (HSI), pp. s.1–6, IEEE, 07 2022.
  • [5] F. Wang, J. Zhang, and Q. Xue, “A human neck finite element model basing on DICOM data,” in 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. s. 1–4,IEEE, 06 2009.
  • [6] F. Pu, D. Jin, S. Li, D. Li, H. Niu, Y. Yang, and Y. Fan, “Reconstruction of three-dimensional model of normal female pelvic cavity based on magnetic resonance imaging,” in 2007 IEEE/ICME International Conference on Complex Medical Engineering, pp. 732–735, 2007.
  • [7] V. Simic, M. Milosevic, I. Saveljic, B. Milicevic, N. Filipovic,and M. Kojic, “3D reconstruction and computational modeling of solid-fluid interaction in realistic heart model,” in BIBE 2021- 21st IEEE International Conference on Bio-Informatics and Bio-Engineering, Proceedings, BIBE 2021 - 21st IEEE International Conference on Bio-Informatics and Bio-Engineering, Proceedings,(United States), Institute of Electrical and Electronics Engineers Inc., 2021.
  • [8] P. Valchanov and S. Pavlov, “High fidelity anthropomorphic 3D printed models - accuracy, precision and quality control,” pp. 1–4,11 2022.
  • [9] DICOM Standards Committee, “DICOM standard.” https://www.dicomstandard.org/, 2023. Accessed: 16.05.2023.
  • [10] N. Otsu, “A threshold selection method from gray-level his-to grams, ”IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
  • [11] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge University Press, 2 ed., 2003.
  • [12] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in 2011 International Conference on Computer Vision, pp. 2564–2571, 2011.
  • [13] R. W. Hamming, “Error detecting and error correcting codes, ”TheBell System Technical Journal, vol. 29, no. 2, pp. 147–160, 1950.
  • [14] R. C. Bolles and M. A. Fischler, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, ”Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981.
  • [15] P. Thévenaz, U. E. Ruttimann, and M. Unser, “A pyramid approach to subpixel registration based on intensity,” IEEE Trans-actions on Image Processing, vol. 7, no. 1, pp. 27–41, 1998.
  • [16] J. F. Canny, “A computational approach to edge detection,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8,no. 6, pp. 679–698, 1986.
  • [17] H. E. Cline, W. E. Lorensen, S. Ludke, C. R. Crawford, B. C. Teeter, J. W. Hetrick, R. A. Schachar, D. P. Greenberg, C. R. Volpe, J. W. Philbrick,et al., “Two algorithms for the three-dimensional reconstruction of tomograms, ”Computer Graphics, vol. 21, no. 4, pp. 163–169, 1987.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-38e3c67e-7521-451c-8456-a09822df7371
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