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dc.creatorKlonowski, Wlodzimierz
dc.creatorStepien, P.
dc.creatorStepien, R.
dc.creatorSedivy, R.
dc.creatorAhammer, H.
dc.creatorSpasić, Slađana
dc.date.accessioned2022-04-05T15:16:26Z
dc.date.available2022-04-05T15:16:26Z
dc.date.issued2018
dc.identifier.issn0218-348X
dc.identifier.urihttp://rimsi.imsi.bg.ac.rs/handle/123456789/1169
dc.description.abstractThe 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.en
dc.publisherWorld Scientific Publ Co Pte Ltd, Singapore
dc.relationNalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/173045/RS//
dc.rightsrestrictedAccess
dc.sourceFractals-Complex Geometry Patterns and Scaling in Nature and Society
dc.subjectTumor Stagingen
dc.subjectImage Analysisen
dc.subjectHiguchi Algorithmen
dc.subjectFractal Dimensionen
dc.titleAnalysis of anal intraepithelial neoplasia images using 1d and 2d higuchi's fractal dimension methodsen
dc.typearticle
dc.rights.licenseARR
dc.citation.issue3
dc.citation.other26(3): -
dc.citation.rankM21
dc.citation.volume26
dc.identifier.doi10.1142/S0218348X18500214
dc.identifier.scopus2-s2.0-85047916902
dc.identifier.wos000435968700002
dc.type.versionpublishedVersion


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