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Proceedings Paper

Directional denoising and line enhancement for device segmentation in real time fluoroscopic imaging
Author(s): Martin Wagner; Kevin Royalty; Erick Oberstar; Charles Strother; Charles Mistretta
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Paper Abstract

Purpose: The purpose of this work is to improve the segmentation of interventional devices (e.g. guidewires) in fluoroscopic images. This is required for the real time 3D reconstruction from two angiographic views where noise can cause severe reconstruction artifacts and incomplete reconstruction. The proposed method reduces the noise while enhancing the thin line structures of the device in images with subtracted background.

Methods: A two-step approach is presented here. The first step estimates, for each pixel and a given number of directions, a measure for the probability that the point is part of a line segment in the corresponding direction. This can be done efficiently using binary masks. In the second step, a directional filter kernel is applied for pixel that are assumed to be part of a line. For all other pixels a mean filter is used.

Results: The proposed algorithm was able to achieve an average contrast to noise ratio (CNR) of 6.3 compared to the bilateral filter with 5.8. For the device segmentation using global thresholding the number of missing or wrong pixels is reduced to 25 % compared to 40 % using the bilateral approach.

Conclusion: The proposed algorithm is a simple and efficient approach, which can easily be parallelized for the use on modern graphics processing units. It improves the segmentation results of the device compared to other denoising methods, and therefore reduces artifacts and increases the quality of the reconstruction without increasing the delay in real time applications notably.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132C (20 March 2015); doi: 10.1117/12.2081545
Show Author Affiliations
Martin Wagner, Univ. of Wisconsin-Madison (United States)
Kevin Royalty, Univ. of Wisconsin-Madison (United States)
Siemens Medical Solutions USA, Inc. (United States)
Erick Oberstar, Univ. of Wisconsin-Madison (United States)
Charles Strother, Univ. of Wisconsin-Madison (United States)
Charles Mistretta, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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