title: Effects of magnetic resonance image interpolation on the results of texture-based pattern classification: a phantom study.
abstract: OBJECTIVE
  To (1) determine whether magnetic resonance (MR) image interpolation at the pixel or k-space level can improve the results of texture-based pattern classification, and (2) compare the effects of image interpolation on texture features of different categories, with regard to their ability to distinguish between different patterns.
  
  
  METHODS
  We obtained T2-weighted, multislice multiecho MR images of 2 sets of each 3 polystyrene spheres and agar gel (PSAG) phantoms with different nodular patterns (sphere diameter: PSAG-1, 0.8-1.25 mm; PSAG-2, 1.25-2.0 mm; PSAG-3, 2.0-3.15 mm), using a 3.0 Tesla scanner equipped with a dedicated microimaging gradient insert. Image datasets, which consisted of 20 consecutive axial slices each, were obtained with a constant field of view (30 x 30 mm(2)), but with variations of matrix size (MTX): 16 x 16; 32 x 32; 64 x 64; 128 x 128; and 256 x 256. Original images were interpolated to higher matrix sizes (up to 256 x 256) by means of linear and cubic B-spline (pixel level) as well as zero-fill (k-space level) interpolation. For both original and interpolated image datasets, texture features derived from the co-occurrence (COC) and run-length matrix (RUN), absolute gradient (GRA), autoregressive model, and wavelet transform (WAV) were calculated independently. Based on the 3 best texture features of each category, as determined by calculation of Fisher coefficients using images from the first set of PSAG phantoms (training dataset), k-means clustering was performed to separate PSAG-1, PSAG-2, and PSAG-3 images belonging to the second set of phantoms (test dataset). This was done independently for all original and interpolated image datasets. Rates of misclassified data vectors were used as primary outcome measures.
  
  
  RESULTS
  For images based on a very low original resolution (MTX = 16 x 16), misclassification rates remained high, despite the use of interpolation. For higher resolution images (MTX = 32 x 32 and 64 x 64), interpolation enhanced the ability of texture features, in all categories except WAV, to discriminate between the 3 phantoms. This positive effect was particularly pronounced for COC and RUN features, and to a lesser degree, also GRA features. No consistent improvements, and even some negative effects, were observed for WAV features, after interpolation. Although there was no clear superiority of any single interpolation techniques at very low resolution (MTX = 16 x 16), zero-fill interpolation outperformed the two pixel interpolation techniques, for images based on higher original resolutions (MTX = 32 x 32 and 64 x 64). We observed the most considerable improvements after interpolation by a factor of 2 or 4.
  
  
  CONCLUSIONS
  MR image interpolation has the potential to improve the results of pattern classification, based on COC, RUN, and GRA features. Unless spatial resolution is very poor, zero-filling is the interpolation technique of choice, with a recommended maximum interpolation factor of 4.
authors.affiliation: Department of Radiology, MR Center, Medical University of Vienna, Vienna, Austria. marius.mayerhoefer@meduniwien.ac.at
authors.forename: Marius E
authors.initials: ME
authors.lastname: Mayerhoefer
authors.forename: Pavol
authors.initials: P
authors.lastname: Szomolanyi
authors.forename: Daniel
authors.initials: D
authors.lastname: Jirak
authors.forename: Andreas
authors.initials: A
authors.lastname: Berg
authors.forename: Andrzej
authors.initials: A
authors.lastname: Materka
authors.forename: Albert
authors.initials: A
authors.lastname: Dirisamer
authors.forename: Siegfried
authors.initials: S
authors.lastname: Trattnig
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity