title: Technical Note: Image filtering to make computer-aided detection robust to image reconstruction kernel choice in lung cancer CT screening.
abstract: OBJECTIVE
  In lung cancer computed tomography (CT) screening, the performance of a computer-aided detection (CAD) system depends on the selection of the image reconstruction kernel. To reduce this dependence on reconstruction kernels, the authors propose a novel application of an image filtering method previously proposed by their group.
  
  
  METHODS
  The proposed filtering process uses the ratio of modulation transfer functions (MTFs) of two reconstruction kernels as a filtering function in the spatial-frequency domain. This method is referred to as MTFratio filtering. Test image data were obtained from CT screening scans of 67 subjects who each had one nodule. Images were reconstructed using two kernels: fSTD (for standard lung imaging) and fSHARP (for sharp edge-enhancement lung imaging). The MTFratio filtering was implemented using the MTFs measured for those kernels and was applied to the reconstructed fSHARP images to obtain images that were similar to the fSTD images. A mean filter and a median filter were applied (separately) for comparison. All reconstructed and filtered images were processed using their prototype CAD system.
  
  
  RESULTS
  The MTFratio filtered images showed excellent agreement with the fSTD images. The standard deviation for the difference between these images was very small, ∼6.0 Hounsfield units (HU). However, the mean and median filtered images showed larger differences of ∼48.1 and ∼57.9 HU from the fSTD images, respectively. The free-response receiver operating characteristic (FROC) curve for the fSHARP images indicated poorer performance compared with the FROC curve for the fSTD images. The FROC curve for the MTFratio filtered images was equivalent to the curve for the fSTD images. However, this similarity was not achieved by using the mean filter or median filter.
  
  
  CONCLUSIONS
  The accuracy of MTFratio image filtering was verified and the method was demonstrated to be effective for reducing the kernel dependence of CAD performance.
authors.affiliation: Graduate School of Health Sciences, Niigata University, Niigata 951-8518, Japan.
authors.forename: Masaki
authors.initials: M
authors.lastname: Ohkubo
authors.affiliation: Graduate School of Health Sciences, Niigata University, Niigata 951-8518, Japan.
authors.forename: Akihiro
authors.initials: A
authors.lastname: Narita
authors.affiliation: Graduate School of Health Sciences, Niigata University, Niigata 951-8518, Japan.
authors.forename: Shinichi
authors.initials: S
authors.lastname: Wada
authors.affiliation: Fujitsu Ltd., Tokyo 144-8588, Japan.
authors.forename: Kohei
authors.initials: K
authors.lastname: Murao
authors.affiliation: Kensei Clinic, Chiba 260-0032, Japan.
authors.forename: Toru
authors.initials: T
authors.lastname: Matsumoto
mesh_terms: D000328:Adult; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*