THE INFLUENCE OF THE PRINCIPAL COMPONENT ANALYSIS OF TEXTURE FEATURES ON THE CLASSIFICATION QUALITY OF SPONGE TISSUE IMAGES
The aim of this article was to determine the effect of principal component analysis on the results of classification of spongy tissue images. Four hundred computed tomography images of the spine (L1 vertebra) were used for the analyses. The images were from fifty healthy patients and fifty patients diagnosed with osteoporosis. The obtained tissue image samples with a size of 50x50 pixels were subjected to texture analysis. As a result, feature descriptors based on a grey level histogram, gradient matrix, RL matrix, event matrix, autoregressive model and wavelet transform were obtained. The results obtained were ranked in importance from the most important to the least important. The first fifty features from the ranking were used for further experiments. The data were subjected to the principal component analysis, which resulted in a set of six new features. Subsequently, both sets (50 and 6 traits) were classified using five different methods: naive Bayesian classifier, multilayer perceptrons, Hoeffding Tree, 1-Nearest Neighbour and Random Forest. The best results were obtained for data on which principal components analysis was performed and classified using 1-Nearest Neighbour. Such an algorithm of procedure allowed to obtain a high value of TPR and PPV parameters, equal to 97.5%. In the case of other classifiers, the use of principal component analysis worsened the results by an average of 2%.
principal component analysis; classification; texture analysis; medical imaging
Armi L., Fekri-Ershad S.: Texture image analysis and texture classification methods – a review. International Online Journal of Image Processing and Pattern Recognition 2/2019, 1–29.
Bharati M. H., Liu J. J., MacGregor J. F.: Image texture analysis: methods and comparisons. Chemometrics and Intelligent Laboratory Systems 72/ 2004, 57–71, [http://doi.org/10.1016/j.chemolab.2004.02.005].
Bishop C. M.: Pattern Recognition and Machine Learning. Springer, New York, 2006.
Haralick R. M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67/1979, 786–804, [http://doi.org/10.1109/PROC.1979.11328].
Haralick R. M., Shanmugam K., Dinstein I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3, 1973, 610–621, [http://doi.org/10.1109/TSMC.1973.4309314].
Humeau-Heurtier A.: Texture Feature Extraction Methods: A Survey. IEEE Access 7, 2019, 8975–9000, [http://doi.org/10.1109/ACCESS.2018.2890743].
Jain D., Singh V.: Feature selection and classification systems for chronic disease prediction: A review. Egyptian Informatics Journal 19/ 2018, 179–189, [http://doi.org/10.1016/j.eij.2018.03.002].
Jolliffe I. T., Cadima J.: Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374/2016, [http://doi.org/10.1098/rsta.2015.0202].
Lever J., Krzywinski M., Altman N.: Principal component analysis. Nature Methods 14/ 2017, 641–642, [http://doi.org/10.1038/nmeth.4346].
Liu B., Yu X., Zhang P., Yu A., Fu Q., Wei X.: Supervised Deep Feature Extraction for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 56, 2018, 1909–1921, [http://doi.org/10.1109/TGRS.2017.2769673].
Omiotek Z.: Automatyczna klasyfikacja obrazów USG tarczycy. Rozprawa doktorska. Politechnika Lubelska, Lublin 2014.
Oprogramowanie Program MaZda <http://www.eletel.p.lodz.pl/programy/cost/progr_mazda.html> (available 03.07.2020).
Shahabaz, Somwanshi D. K., Yadav A. K., Roy R.: Medical images texture analysis: A review. International Conference on Computer, Communications and Electronics (Comptelix) 2017,[http://doi.org/10.1109/COMPTELIX.2017.8004009].
Shang Z., Li M.: Combined Feature Extraction and Selection in Texture Analysis. 9th International Symposium on Computational Intelligence and Design (ISCID)Presented at the 2016 9th International Symposium on Computational Intelligence and Design (ISCID) 2016, 398–401, [http://doi.org/10.1109/ISCID.2016.1098].
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