Susceptibility of texture measures to noise: An application to lung tumor CT images
Abstract
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.
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