  Five different texture methods are used to investigate their susceptibility
to subtle noise occurring in lung tumor Computed Tomography (CT) images caused
by acquisition and reconstruction deficiencies. Noise of Gaussian and Rayleigh
distributions with varying mean and variance was encountered in the analyzed CT
images. Fisher and Bhattacharyya distance measures were used to differentiate
between an original extracted lung tumor region of interest (ROI) with a
filtered and noisy reconstructed versions. Through examining the texture
characteristics of the lung tumor areas by five different texture measures, it
was determined that the autocovariance measure was least affected and the gray
level co-occurrence matrix was the most affected by noise. Depending on the
selected ROI size, it was concluded that the number of extracted features from
each texture measure increases susceptibility to noise.
