Paper Info
Title
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.
Year
DOI
Venue
2008
10.1109/BIBE.2008.4696789
8TH IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING, VOLS 1 AND 2
Keywords
Field
DocType
region of interest,computed tomography,image texture,rayleigh distribution,image reconstruction,bhattacharyya distance,noise,current transformers,gaussian noise,noise measurement
Iterative reconstruction,Autocovariance,Computer vision,Bhattacharyya distance,Pattern recognition,Noise measurement,Image texture,Artificial intelligence,Region of interest,Gaussian noise,Mathematics,Rayleigh distribution
Conference
Volume
ISSN
Citations 
abs/1601.00210
2471-7819
2
PageRank 
References 
Authors
0.41
6
2
Name
Order
Citations
PageRank
Omar S. Al-Kadi1324.32
D. Watson220.41