A review of the efficient algorithm implementation for image processing in the ImageJ and Matlab environments

Barbara Kopacz, Adam Piórkowski

Abstract


This article shows methods of time-consuming numerical procedures implementation for the Matlab environment. There are considered possibilities to compile code written in C and the executable file mex, compile the source code in C# dll and call it in Matlab, and the use of plug-ins for ImageJ environment. These implementations were referred to the equivalent algorithm created in MATLAB scripting code. There were tested two variants: for the calculation of single-threaded and parallel. The study was conducted by implementing the algorithm of statistical dominance for preprocessing images. The results are shown in the tables, and annotated.

Keywords


image processing; Matlab

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References


Piórkowski A.: A Statistical Dominance Algorithm for Edge Detection and Segmentation of Medical Images. ITIB 2016, AISC, vol. 471, Springer, 2016, s. 3÷14.

Popowicz A., Kurek A.R.: An Algorithm for Joint and Bone Localization in USG Images of Rheumatoid Arthritis. Studia Informatica, 37 (3B), 2016, s. 7÷19.

Bielecka M., Korkosz M.: Generalized Shape Language Application to Detection of a Specific Type of Bone Erosion in X-ray Images. LNCS, Springer, 2016, s. 531÷540.

Bielecka M., Bielecki A., Korkosz M., Skomorowski M.,Wojciechowski W., Zielinski B.: Application of Shape Description Methodology to Hand Radiographs Interpretation. LNCS, Springer, 2010, s. 11÷18.

Tselikis G., Tselikas N.: C: From Theory to Practice. Boca Raton, CRC Press, 2014.

C# Language Specification, Ecma (dostęp 29.12.2016), http://www.ecma-international. org/publications/files/ECMA-ST/Ecma-334.pdf

Blanchet G., Charbit M.: Digital Signal and Image Processing Using MATLAB. John Wiley & Sons, London 2006.

Augustyn D., Kunc S.: Efektywność programów przeznaczonych do symulacji ciągłych układów dynamicznych, wykorzystujących moduł Parallel Extensions to. NET Framework, uruchamianych na komputerach z procesorami wielordzeniowymi. Studia Informatica, 31(3), 2010, s. 53÷76.

Broeke J. i in.: Image Processing with ImageJ. Packt Publishing, Birmingham 2015.

Sarang P.: Java Programming. Mc Graw Hill, New York 2012.

Kernighan B., Ritchie D.: Język ANSI C. WNT, Warszawa 2000.

Reiter E., Johnson C.: Limits of Computation: An Introduction to the Undecidable and the Intractable. Chapman and Hall/CRC, Boca Raton 2012.

Novák I. i in.: Visual Studio 2010 and .NET 4 Six-in-One. Wrox Press Ltd., 2010.

Toub S.: Patterns of Parallel Programming. Understanding and Applying Parallel Patterns with the .Net Framework 4 and Visual CSharp. Parallel Computing Platform, Microsoft Corporation, February 2010.

http://bigwww.epfl.ch/sage/soft/mij/, dostęp 28.12.2016.

https://imagej.nih.gov, dostęp 28.12.2016.

https://www.mathworks.com/help/matlab/matlab_external/bringing-java-classes-and-methods-into-matlab-workspace.html, dostęp 27.12.2016.

https://www.mathworks.com/help/matlab/matlab_external/introducing-mex-files.html, dostęp 27.12.2016.




DOI: http://dx.doi.org/10.21936/si2017_v38.n3.820