Matched polynomial features for the analysis of grayscale biomedical images
A.V. Gaidel

 

Samara State Aerospace University, Samara, Russia

Full text of article: Russian language.

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.

Citation:
Gaidel AV. Matched polynomial features for the analysis of grayscale biomedical images. Computer Optics 2016; 40(2): 232-39. DOI: 10.18287/2412-6179-2016-40-2-232-239.

References:

  1. Gashnikov MV, Glumov NI, Ilyasova NYu, Myasnikov VV, Popov SB, Sergeev VV, Soifer VA, Khramov AG, Chernov AV, Chernov VM, Chicheva MA, Fursov VA. Methods for computer image processing [In Russian]. Ed by Soifer VA. Moscow: Fizmatlit; 2003.
  2. Fukunaga K. Introduction to statistical pattern recognition. San Diego: Academic Press; 1990.
  3. Ilyasova NYu, Kupriyanov AV, Paringer RA. Formation of features for improving the quality of medical diagnosis based on discriminant analysis methods. Computer Optics 2014; 38(4): 851-855.
  4. Gaidel AV, Pervushkin SS. Research of the textural features for the bony tissue diseases diagnostics using the roentgenograms. Computer Optics 2013; 37(1): 113-119.
  5. Gaidel AV, Larionova SN, Khramov AG. Research of the textural features for the diagnostics of nephrological diseases using the ultrasound images [In Russian]. Herald of the Samara State Aerospace University 2014; 43(1): 229-237.
  6. Gaidel AV, Zelter PM, Kapishnikov AV, Khramov AG. Computed tomography texture analysis capabilities in diagnosing a chronic obstructive pulmonary disease. Computer Optics 2014; 38(4): 843-850.
  7. Yang M, Zheng H, Wang H, McClean S. Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. 3rd International Conference on Pervasive Computing Technologies for Healthcare: Pervasive Health’09; London, United Kingdom, 1-3 April, 2009.
  8. Peng Y, Wu Z, Jiang J. A novel feature selection approach for biomedical data classification. J Biomed Inform 2010; 43(1): 15-23.
  9. Neshatian K, Zhang M, Johnston M. Feature construction and dimension reduction using genetic programming. LNCS 2007; 4830: 160-170.
  10. Fan W, Zhong E, Peng J, Verscheure O, Zhang K, Ren J, Yan R, Yang Q. Generalized and heuristic-free feature construction for improved accuracy. Proceedings of the 10th SIAM International Conference on Data Mining, Columbus, OH, United States, 29 April - 1 May 2010: 629-640.
  11. Lillywhite K, Lee D-J, Tippetts B, Archibald J. A feature construction method for general object recognition. Pattern Recogn 2013; 46(12): 3300-3314.
  12. Myasnikov VV. Constructing efficient linear local features in image processing and analysis problems. Automat Rem Contr 2010; 71(3): 514-527.
  13. Myasnikov VV, Bavrina AY, Titova OA. Analysis of methods for construction of efficient linear local features for digital signals and images description [In Russian]. Computer Optics 2010; 34(3): 374-381.
  14. Gaidel AV. A method for adjusting directed texture features in biomedical image analysis problems. Computer Optics 2015; 39(2): 287-293.
  15. Raymond XS. Elementary Introduction to the Theory of Pseudodifferential Operators. Boca Raton: CRC Press; 1991.
  16. Agresti A, Coull BA. Approximate is Better than "Exact" for Interval Estimation of Binomial Proportions. Am Stat 1998; 52(2): 119-126.
  17. Ginsburg SB, Lynch DA, Bowler RP, Schroeder JD. Automated Texture-based Quantification of Centrilobular Nodularity and Centrilobular Emphysema in Chest CT Images. Acad Radiol 2012; 19(10): 1241-1251.

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