  Noise is one of the major problems that hinder an effective texture analysis
of disease in medical images, which may cause variability in the reported
diagnosis. In this paper seven texture measurement methods (two wavelet, two
model and three statistical based) were applied to investigate their
susceptibility to subtle noise caused by acquisition and reconstruction
deficiencies in computed tomography (CT) images. Features of lung tumours were
extracted from two different conventional and contrast enhanced CT image
data-sets under filtered and noisy conditions. When measuring the noise in the
background open-air region of the analysed CT images, noise of Gaussian and
Rayleigh distributions with varying mean and variance was encountered, and
Fisher distance was used to differentiate between an original extracted lung
tumour region of interest (ROI) with the filtered and noisy reconstructed
versions. It was determined that the wavelet packet (WP) and fractal dimension
measures were the least affected, while the Gaussian Markov random field,
run-length and co-occurrence matrices were the most affected by noise.
Depending on the selected ROI size, it was concluded that texture measures with
fewer extracted features can decrease susceptibility to noise, with the WP and
the Gabor filter having a stable performance in both filtered and noisy CT
versions and for both data-sets. Knowing how robust each texture measure under
noise presence is can assist physicians using an automated lung texture
classification system in choosing the appropriate feature extraction algorithm
for a more accurate diagnosis.
