Support value based stent-graft marker detection

Authors:
Sheng Zheng;Changcai Yang;Bart L. Kaptein;Emile A. Hendriks;Olivier H. J. Koning;Bangjun Lei
Affiliations:
Institute of Intelligent Vision and Image Information, College of Science, China Three Gorges University, Yichang 443002, China;Institute for Pattern Recognition and Artificial Intelligence, Science and Technology on Multi-Spectral Information Processing Laboratory, Huazhong University of Science and Technology, Wuhan 4300 ...;Department of Orthopedics, and Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands;Information and Communication Theory Group, Mathematics and Computer Science, Delft University of Technology, 2628CD Delft, The Netherlands;Department of Surgery, Division of Vascular Surgery, Leiden University Medical Center, The Netherlands;Institute of Intelligent Vision and Image Information, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Venue:
Pattern Recognition
Year:
2013

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Abstract

With the development of the fluoroscopic roentgenographic stereophotogrammetric analysis (FRSA), it is possible to make the three-dimensional (3D) dynamics of stent-graft. The stent-graft markers, however, are identified manually. In this paper we present a robust solution for automatic detection of stent-graft marker projections in FRSA X-ray images. Several directional support value (dSV) filters and the directional support value transform (dSVT) method are studied. Based on the dSV of the dSVT, a support value matrix is constructed, and the determinant of this matrix is then defined as the markerness measure. The corresponding multi-scale correlations of the rescaled markerness measures are computed for enhancing the multi-scale marker response peaks while suppressing the effects of stent-grafts and Poisson noise. The marker spots are subsequently located by finding the local maximum of the correlated markerness measures. The conditional variance Stabilizer (CVS) is further integrated into this framework for removing Poisson noises. Performance comparisons are carried out among the proposed dSVT, the CVS+dSVT, local threshold operation (LTO) and the frequently adopted spot detectors, including the morphological grayscale opening top-hat filter (MTH), wavelet multiscale products (WMP), and multiscale variance-stabilizing transform (MSVST) methods. The results from experiments on synthetic as well as real FRSA X-ray image data show that the proposed CVS+dSVT method performs better than other detectors, in terms of the free-response receiver operation characteristic (FROC) curves.