Implementation and evaluation of medical imaging techniques based on conformal geometric algebra

• Autorzy: Franchini Silvia , Gentile Antonio , Vassallo Giorgio , Vitabile Salvatore

Identyfikatory

DOI

10.34768/amcs-2020-0031

• Języki publikacji: EN

Abstrakty

EN

Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10^-5.

Słowa kluczowe

EN

medical image segmentation; medical image registration; computational geometry; Clifford algebra; conformal geometric algebra

PL

segmentacja obrazu; rejestracja obrazu medycznego; geometria obliczeniowa; algebra Clifforda

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2020
• Tom: Vol. 30, no. 3
• Strony: 415--433
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Franchini Silvia

• Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, Via del Vespro, 129, 90127 Palermo, Italy

autor

Gentile Antonio

• Department of Engineering, University of Palermo, Viale delle Scienze, Edificio 6, 90128 Palermo, Italy

autor

Vassallo Giorgio

• Department of Engineering, University of Palermo, Viale delle Scienze, Edificio 6, 90128 Palermo, Italy

autor

Vitabile Salvatore

• Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, Via del Vespro, 129, 90127 Palermo, Italy

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).