IMAGE PROCESSING, PATTERN RECOGNITION
Matched polynomial features for the analysis of grayscale biomedical images
A. V. Gaidelab
a Samara State Aerospace University
b Image Processing Systems Institute, Russian Academy of Sciences
We considered the general form of polynomial features represented as polynomials in the image pixels domain. We showed that under natural constraints these polynomial features turned to linear combinations of the image autocovariance function readings. We proposed a number of approaches for matching the features under study with texture properties of images from a training sample. During computational experiments on three sets of real diagnostic images we demonstrated the efficiency of the proposed features, which involved the decrease of the recognition error
probability of X-ray bone tissue images from 0.10 down to 0.06 in comparison with the previously studied methods.
texture analysis, discriminant analysis, feature construction, feature selection, computer-aided diagnostics, polynomial features.
|Russian Foundation for Basic Research
|Ministry of Education and Science of the Russian Federation
|This work was supported by RFBR grant 14-07-97040 - r_povolzhe_a and the Ministry of Education and Science of the Russian Federation in the framework of the Program of increase of competitiveness SSAU among the world's leading research and education centers for 2013-2020 , as well as the Program of Fundamental Research RAS Onita " Bioinformatics , modern information technology and mathematical methods in medicine".
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A. V. Gaidel, “Matched polynomial features for the analysis of grayscale biomedical images”, Computer Optics, 40:2 (2016), 232–239
Citation in format AMSBIB
\paper Matched polynomial features for the analysis of grayscale biomedical images
\jour Computer Optics
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