TY  - JOUR
AU  - Klonowski, Wlodzimierz
AU  - Stepien, P.
AU  - Stepien, R.
AU  - Sedivy, R.
AU  - Ahammer, H.
AU  - Spasić, Slađana
PY  - 2018
UR  - http://rimsi.imsi.bg.ac.rs/handle/123456789/1169
AB  - The ILF (Image Landscapes' Fractal Dimension) method and DF2d method obtained by a 2D generalization of Higuchi's algorithm were applied to a set of 120 digital histological images of Anal Intraepithelial Neoplasia (AIN). The main goal of this research was to examine accuracy that means sensitivity and specificity of these methods and compare the applicability of both methods in the quantitative characterization and differentiation of clinical cases of AIN. Histological examination by an experienced pathologist revealed three grades of AIN tumors in the 120 histological slices: 36 of AIN1, 56 of AIN2 and 28 of AIN3. Statistical tests showed significant differences between calculated fractal dimension values in three datasets (AIN1, AIN2 and AIN3) using ILF and DF2d methods at the level of significance of 0.05. Application of the ILF and DF2d methods has an advantage when it comes to speed, accuracy, simplicity and time necessary for analysis. Both methods can be successfully applied for differentiation between AIN stages giving practically the same results. They can easily be adapted to other histological specimen.
PB  - World Scientific Publ Co Pte Ltd, Singapore
T2  - Fractals-Complex Geometry Patterns and Scaling in Nature and Society
T1  - Analysis of anal intraepithelial neoplasia images using 1d and 2d higuchi's fractal dimension methods
IS  - 3
VL  - 26
DO  - 10.1142/S0218348X18500214
ER  - 

@article{
author = "Klonowski, Wlodzimierz and Stepien, P. and Stepien, R. and Sedivy, R. and Ahammer, H. and Spasić, Slađana",
year = "2018",
abstract = "The ILF (Image Landscapes' Fractal Dimension) method and DF2d method obtained by a 2D generalization of Higuchi's algorithm were applied to a set of 120 digital histological images of Anal Intraepithelial Neoplasia (AIN). The main goal of this research was to examine accuracy that means sensitivity and specificity of these methods and compare the applicability of both methods in the quantitative characterization and differentiation of clinical cases of AIN. Histological examination by an experienced pathologist revealed three grades of AIN tumors in the 120 histological slices: 36 of AIN1, 56 of AIN2 and 28 of AIN3. Statistical tests showed significant differences between calculated fractal dimension values in three datasets (AIN1, AIN2 and AIN3) using ILF and DF2d methods at the level of significance of 0.05. Application of the ILF and DF2d methods has an advantage when it comes to speed, accuracy, simplicity and time necessary for analysis. Both methods can be successfully applied for differentiation between AIN stages giving practically the same results. They can easily be adapted to other histological specimen.",
publisher = "World Scientific Publ Co Pte Ltd, Singapore",
journal = "Fractals-Complex Geometry Patterns and Scaling in Nature and Society",
title = "Analysis of anal intraepithelial neoplasia images using 1d and 2d higuchi's fractal dimension methods",
number = "3",
volume = "26",
doi = "10.1142/S0218348X18500214"
}

Klonowski, W., Stepien, P., Stepien, R., Sedivy, R., Ahammer, H.,& Spasić, S.. (2018). Analysis of anal intraepithelial neoplasia images using 1d and 2d higuchi's fractal dimension methods. in Fractals-Complex Geometry Patterns and Scaling in Nature and Society
World Scientific Publ Co Pte Ltd, Singapore., 26(3).
https://doi.org/10.1142/S0218348X18500214

Klonowski W, Stepien P, Stepien R, Sedivy R, Ahammer H, Spasić S. Analysis of anal intraepithelial neoplasia images using 1d and 2d higuchi's fractal dimension methods. in Fractals-Complex Geometry Patterns and Scaling in Nature and Society. 2018;26(3).
doi:10.1142/S0218348X18500214 .

Klonowski, Wlodzimierz, Stepien, P., Stepien, R., Sedivy, R., Ahammer, H., Spasić, Slađana, "Analysis of anal intraepithelial neoplasia images using 1d and 2d higuchi's fractal dimension methods" in Fractals-Complex Geometry Patterns and Scaling in Nature and Society, 26, no. 3 (2018),
https://doi.org/10.1142/S0218348X18500214 . .

TY  - JOUR
AU  - Jovanović, Aleksandar
AU  - Perović, Aleksandar
AU  - Klonowski, Wlodzimierz
AU  - Duch, Wlodzislaw
AU  - Đorđević, Zoran
AU  - Spasić, Slađana
PY  - 2010
UR  - http://rimsi.imsi.bg.ac.rs/handle/123456789/381
AB  - In this article structures in biological signals are treated. The simpler-directly visible in the signals, which still demand serious methods and algorithms in the feature detection, similarity investigation and classification. The major actions in this domain are of geometric, thus simpler sort, though there are still hard problems related to simple situations. The other large class of less simple signals unsuitable for direct geometric or statistic approach, are signals with interesting frequency components and behavior, those suitable for spectroscopic analysis. Semantics of spectroscopy, spectroscopic structures and research demanded operations and transformations on spectra and time spectra are presented. The both classes of structures and related analysis methods and tools share a large common set of algorithms, all of which aiming to the full automatization. Some of the signal features present in the brain signal patterns are demonstrated, with the contexts relevant in BCI, brain computer interfaces. Mathematical representations, invariants and complete characterization of structures in broad variety of biological signals are in the central focus.
PB  - Springer, New York
T2  - Journal of Signal Processing Systems for Signal Image and Video Technology
T1  - Detection of Structural Features in Biological Signals
EP  - 129
IS  - 1
SP  - 115
VL  - 60
DO  - 10.1007/s11265-009-0407-7
ER  - 

@article{
author = "Jovanović, Aleksandar and Perović, Aleksandar and Klonowski, Wlodzimierz and Duch, Wlodzislaw and Đorđević, Zoran and Spasić, Slađana",
year = "2010",
abstract = "In this article structures in biological signals are treated. The simpler-directly visible in the signals, which still demand serious methods and algorithms in the feature detection, similarity investigation and classification. The major actions in this domain are of geometric, thus simpler sort, though there are still hard problems related to simple situations. The other large class of less simple signals unsuitable for direct geometric or statistic approach, are signals with interesting frequency components and behavior, those suitable for spectroscopic analysis. Semantics of spectroscopy, spectroscopic structures and research demanded operations and transformations on spectra and time spectra are presented. The both classes of structures and related analysis methods and tools share a large common set of algorithms, all of which aiming to the full automatization. Some of the signal features present in the brain signal patterns are demonstrated, with the contexts relevant in BCI, brain computer interfaces. Mathematical representations, invariants and complete characterization of structures in broad variety of biological signals are in the central focus.",
publisher = "Springer, New York",
journal = "Journal of Signal Processing Systems for Signal Image and Video Technology",
title = "Detection of Structural Features in Biological Signals",
pages = "129-115",
number = "1",
volume = "60",
doi = "10.1007/s11265-009-0407-7"
}

Jovanović, A., Perović, A., Klonowski, W., Duch, W., Đorđević, Z.,& Spasić, S.. (2010). Detection of Structural Features in Biological Signals. in Journal of Signal Processing Systems for Signal Image and Video Technology
Springer, New York., 60(1), 115-129.
https://doi.org/10.1007/s11265-009-0407-7

Jovanović A, Perović A, Klonowski W, Duch W, Đorđević Z, Spasić S. Detection of Structural Features in Biological Signals. in Journal of Signal Processing Systems for Signal Image and Video Technology. 2010;60(1):115-129.
doi:10.1007/s11265-009-0407-7 .

Jovanović, Aleksandar, Perović, Aleksandar, Klonowski, Wlodzimierz, Duch, Wlodzislaw, Đorđević, Zoran, Spasić, Slađana, "Detection of Structural Features in Biological Signals" in Journal of Signal Processing Systems for Signal Image and Video Technology, 60, no. 1 (2010):115-129,
https://doi.org/10.1007/s11265-009-0407-7 . .