COMPARISON OF THE INFLUENCE OF STANDARDIZATION AND NORMALIZATION OF DATA ON THE EFFECTIVENESS OF SPONGY TISSUE TEXTURE CLASSIFICATION

The aim of this article was to compare the influence of the data pre-processing methods – normalization and standardization – on the results of the classification of spongy tissue images. Four hundred CT images of the spine (L1 vertebra) were used for the analysis. The images were obtained from fifty healthy patients and fifty patients with diagnosed with osteoporosis. The samples of tissue (50×50 pixels) were subjected to a texture analysis to obtain descriptors of features based on a histogram of grey levels, gradient, run length matrix, co-occurrence matrix, autoregressive model and wavelet transform. The obtained results were set in the importance ranking (from the most important to the least important), and the first fifty features were used for further experiments. These data were normalized and standardized and then classified using five different methods: naive Bayes classifier, support vector machine, multilayer perceptrons, random forest and classification via regression. The best results were obtained for standardized data and classified by using multilayer perceptrons. This algorithm allowed for obtaining high accuracy of classification at the level of 94.25%.

[1]  Michael S. Lewicki,et al.  Unsupervised image classification, segmentation, and enhancement using ICA mixture models , 2002, IEEE Trans. Image Process..

[2]  Grzegorz Kozieł.  Implementacja obsługi plikow c3d w środowisku Matlab , 2014 .

[3]  Zbigniew Omiotek,et al.  Zastosowanie metody Hellwiga do redukcji wymiaru przestrzeni cech obrazów USG tarczycy , 2014 .

[4]  J. Lazarek.  Metody analizy obrazu – analiza obrazu mammograficznego na podstawie cech wyznaczonych z tekstury , 2013 .

[5]  C. Reshmalakshmi,et al.  Trabecular bone quality metric from X-ray images for osteoporosis detection , 2017, 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).

[6]  E. Dougherty,et al.  Gray-scale morphological granulometric texture classification , 1994 .

[7]  Erkki Oja,et al.  An Experimental Comparison of Neural ICA Algorithms , 1998 .

[8]  Abdelbasset Brahim,et al.  Diagnosis of osteoporosis disease from bone X-ray images with stacked sparse autoencoder and SVM classifier , 2017, 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[9]  Mariusz Nieniewski,et al.  Extraction of the shape of small defects on the surface of ferrite cores , 2000 .

[10]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[11]  Ismail Mohamad,et al.  Standardization and Its Effects on K-Means Clustering Algorithm , 2013 .

[12]  Zbigniew Omiotek,et al.  Improvement of the classification quality in detection of Hashimoto’s disease with a combined classifier approach , 2017, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[13]  Kashif Rajpoot,et al.  Brain tumor classification from multi-modality MRI using wavelets and machine learning , 2017, Pattern Analysis and Applications.

[14]  Marek Kretowski,et al.  Ekstrakcja cech teksturalnych w klasyfikacji obrazów tomograficznych wątroby , 2007 .

[15]  Patricia A Downey,et al.  Bone biology and the clinical implications for osteoporosis. , 2006, Physical therapy.

[16]  Zbigniew Omiotek,et al.  The Influence of the Normalisation of Spinal CT Images on the Significance of Textural Features in the Identification of Defects in the Spongy Tissue Structure , 2019, Innovations in Biomedical Engineering.

[17]  V. Sadasivam,et al.  Automatic Segmentation and Classification of Diffused Liver Diseases using Wavelet Based Texture Analysis and Neural Network , 2005, 2005 Annual IEEE India Conference - Indicon.

[18]  J. Lygeros.  A formal approach to fuzzy modeling , 1995, Proceedings of 1995 American Control Conference - ACC'95.