title: Noise reduction for curve-linear structures in real time fluoroscopy applications using directional binary masks.
abstract: OBJECTIVE
  Recent efforts in the reconstruction of interventional devices from two distinct views require the segmentation of the object in both fluoroscopic images. Noise might decrease the quality of the segmentation and cause artifacts in the reconstruction. The noise level depends on the x-ray dose the patient is exposed to. The proposed algorithm reduces the noise and enhances the separability of curvilinear devices in background subtracted fluoroscopic images to allow a more accurate segmentation.
  
  
  METHODS
  The algorithm uses a set of binary masks to estimate a line conformity measure that determines the best direction for a directional filter kernel. If the calculated value exceeds a certain threshold, the directional kernel is used to obtain the filtered value. Otherwise, an isotropic filter kernel is used.
  
  
  RESULTS
  The evaluation was performed on a set of 36 fluoroscopic images using a vascular head phantom with three different guidewires and nine different x-ray dosages from 6 nGy/pulse to 45 nGy/pulse as well as a clinical data set containing ten images. Compared with wavelet shrinkage and the bilateral filter, the proposed algorithm increased the average contrast to noise ratio by at least 17.8% for the phantom and 68.9% for the clinical images. The accuracy of the device segmentation was improved on average by at least 17.3% and 14.0%, respectively.
  
  
  CONCLUSIONS
  The proposed algorithm was able to significantly reduce the amount of noise in the images and therefore increase the quality of the device segmentations compared to both the bilateral filter and the wavelet thresholding approach for all acquired noise levels using rotating directional filter kernels near line structures and isotropic kernels for the background. The application of the proposed algorithm for the 3D reconstruction of curvilinear devices from two views would allow a more accurate reconstruction of the device.
authors.affiliation: Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705.
authors.forename: Martin
authors.initials: M
authors.lastname: Wagner
authors.affiliation: Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705 and Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Yang
authors.affiliation: Siemens Medical Solutions USA, Hoffman Estates, Illinois 60192.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schafer
authors.affiliation: Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705.
authors.forename: Charles
authors.initials: C
authors.lastname: Strother
authors.affiliation: Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705.
authors.forename: Charles
authors.initials: C
authors.lastname: Mistretta
mesh_terms: D000465:Algorithms; D016477:Artifacts; D002533:Cerebral Angiography / Q000379:methods; D066264:Datasets as Topic; D004307:Dose-Response Relationship, Radiation; D005471:Fluoroscopy / Q000379:methods*; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008954:Models, Biological; D019047:Phantoms, Imaging; D058067:Wavelet Analysis