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Research on the Key Technology of Image Guided Surgery

100%

Weili S. , Yanfang L. , Yu M. , Yinlong H.

Przegląd Elektrotechniczny

2012

tom R. 88, nr 3b

29-33

It research on the key technology on IGS (image-guided surgery). It proposes medical image segmentation based on PCNN and the virtual endoscopic scenes real-time rendering method based on GPU parallel computing technology, which improves the display quality of IGS’s virtual scene and real-time rendering speed. These methods are very important for IGS’s applications.

Przedstawiono technologię operacji bazującej na prowadzonym systemie obrazu IGS. Zaproponowano segmentację obrazu I możliwość otrzymywania obrazu endoskopowego w trybie czasu rzeczywistego.

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

84%

Franchini S. , Gentile A. , Vassallo G. , Vitabile S.

tom Vol. 30, no. 3

415--433

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.