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Computer Optics, 2016, Volume 40, Issue 2, Pages 232–239 (Mi co137)  

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

Abstract: 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.

Keywords: texture analysis, discriminant analysis, feature construction, feature selection, computer-aided diagnostics, polynomial features.

Funding Agency Grant Number
Russian Foundation for Basic Research 14-07-97040-р_поволжье_а
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".


DOI: https://doi.org/10.18287/2412-6179-2016-40-2-232-239

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Full text: http://www.computeroptics.smr.ru/.../400214.html
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Received: 06.04.2016
Revised: 22.04.2016

Citation: A. V. Gaidel, “Matched polynomial features for the analysis of grayscale biomedical images”, Computer Optics, 40:2 (2016), 232–239

Citation in format AMSBIB
\Bibitem{Gai16}
\by A.~V.~Gaidel
\paper Matched polynomial features for the analysis of grayscale biomedical images
\jour Computer Optics
\yr 2016
\vol 40
\issue 2
\pages 232--239
\mathnet{http://mi.mathnet.ru/co137}
\crossref{https://doi.org/10.18287/2412-6179-2016-40-2-232-239}


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