Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

Sophal Chan, Kwankamon Dittakan, Matias Garcia-Constantino

Abstract


Knee Osteoarthritis (OA) is one of the most prominent diseases in an ageing society and has affected over 10 million people in Thailand. When people suffer from OA, it is very difficult to recover. Therefore, early detection and prevention are important. The typical way to detect OA is by using X-ray imaging. This research study is focused on early detection of OA by applying image processing and classification techniques to knee X-ray imagery. The fundamental concept is to find a region of interest, use feature extraction techniques and build a classifier that can classify between OA or non-OA imageries. There are four regions of interest obtained from each image: (i) Medial Femur (MF), (ii) Lateral Femur (LF), (iii) Medial Tibia (MT), and (iv) Lateral Tibia (LT). The ten texture analysis techniques are then adopted to generate the embedded properties of the bone surface. Once the feature vector has been generated the variety of techniques of machine learning mechanisms are applied to generate the desired classifiers, which can be used to distinguish between OA and non-OA images. From the conducted experiments, a total of 131 images (68 OA cases and 63 non-OA cases) was used, the results obtained show that LF region with Local Binary Pattern descriptor produced the most appropriate classifier with an AUC value of 0.912.


Keywords


texture analysis; osteoarthritis; knee OA; image classification.

Full Text:

PDF

References


J. C. Mora, R. Przkora, and Y. Cruz-Almeida, “Knee osteoarthritis: Pathophysiology and current treatment modalities,†J. Pain Res., vol. 11, pp. 2189–2196, 2018.

Y. Zhang and J. Jordan, “Epidemyology of Osteoarthritis,†Clin. Geriatr. Med., vol. 26, no. 3, pp. 355–369, 2010.

S. S., P. U., and R. R., “Detection of Osteoarthritis using Knee X-Ray Image Analyses: A Machine Vision based Approach,†Int. J. Comput. Appl., vol. 145, no. 1, pp. 20–26, 2016.

T. Janvier, R. Jennane, H. Toumi, and E. Lespessailles, “Subchondral tibial bone texture predicts the incidence of radiographic knee osteoarthritis: data from the Osteoarthritis Initiative,†Osteoarthr. Cartil., vol. 25, no. 12, pp. 2047–2054, 2017.

M. Kotti, L. D. Duffell, A. A. Faisal, and A. H. McGregor, “Detecting knee osteoarthritis and its discriminating parameters using random forests,†Med. Eng. Phys., vol. 43, pp. 19–29, 2017.

M. Camacho-Encina et al., “Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative,†Ann. Rheum. Dis., p. annrheumdis-2019-215325, 2019.

N. Bayramoglu, A. Tiulpin, J. Hirvasniemi, M. T. Nieminen, and S. Saarakkala, “Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis,†pp. 18–21, 2019.

A. Suponenkovs, Z. Markovics, and A. Platkajis, “Computer Analysis of Knee by Magnetic Resonance Imaging Data,†Procedia Comput. Sci., vol. 104, no. December 2016, pp. 354–361, 2016.

J. Ren, Y. Yuan, Y. Shi, and X. Tao, “Tumor heterogeneity in oral and oropharyngeal squamous cell carcinoma assessed by texture analysis of CT and conventional MRI: a potential marker of overall survival,†Acta radiol., 2019.

C. Jin, Y. Yang, Z.-J. Xue, K.-M. Liu, and J. Liu, “Automated Analysis Method for Screening Knee Osteoarthritis using Medical Infrared Thermography,†J. Med. Biol. Eng., vol. 33, no. 5, p. 471, 2013.

M. George and R. Zwiggelaar, “Comparative study on local binary patterns for mammographic density and risk scoring †,†J. Imaging, vol. 5, no. 2, 2019.

Z. Guo, L. Zhang, and D. Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification,†IEEE Trans. Image Process., vol. 19, no. 6, pp. 1657–1663, 2010.

V. T. Hoang, A. Porebski, N. Vandenbroucke, and D. Hamad, “LBP parameter tuning for texture analysis of lace images,†IPAS 2016 - 2nd Int. Image Process. Appl. Syst. Conf., no. November, 2017.

Z. Wang, R. Huang, W. Yang, and C. Sun, “An enhanced Local Ternary Patterns method for face recognition,†Proc. 33rd Chinese Control Conf. CCC 2014, vol. 0, no. 1, pp. 4636–4640, 2014.

X. Tan and B. Triggs, “Recognition Under Difficult Lighting Conditions,†IEEE Trans. image Process., vol. 19, no. 6, pp. 1635–1650, 2010.

P. P. Kawathekar, “Osteoarthritis of knee joint,†pp. 1–4, 2015.

D. Cherezov et al., “Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness,†Sci. Rep., vol. 9, no. 1, pp. 1–9, 2019.

S. Y. Jeong et al., “ Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18 F-FDG Textural Features Machine Learning Approaches with PCA ,†Contrast Media Mol. Imaging, vol. 2019, pp. 1–7, 2019.

M. A. Naveed et al., “Grading of oligodendroglial tumors of the brain with apparent diffusion coefficient, magnetic resonance spectroscopy, and dynamic susceptibility contrast imaging,†Neuroradiol. J., vol. 31, no. 4, pp. 379–385, 2018.

S. Chinnam, V. Sistla, and V. Kolli, “SVM-PUK Kernel Based MRI-brain Tumor Identification Using Texture and Gabor Wavelets,†Trait. du Signal, vol. 36, no. 2, pp. 185–191, 2019.

J. H. Park, Y. J. Bae, B. S. Choi, Y. H. Jung, and W. Jeong, “Texture Analysis of Multi-Shot Echo-planar Diffusion-Weighted Imaging in Head and Neck Squamous Cell Carcinoma : The Diagnostic Value for Nodal Metastasis,†pp. 1–14, 2019.

H. J. Meyer, G. Hamerla, A. K. Höhn, and A. Surov, “CT texture analysis-correlations with histopathology parameters in head and neck squamous cell carcinomas,†Front. Oncol., vol. 9, no. MAY, pp. 1–8, 2019.

K. J. Lafata, Z. Zhou, J. G. Liu, J. Hong, C. R. Kelsey, and F. F. Yin, “An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images,†Sci. Rep., vol. 9, no. 1, pp. 1–9, 2019.

F. W. Feldhaus, D. C. Theilig, R.-H. Hubner, J.-M. Kuhnigk, K. Neumann, and F. Doellinger, “

Quantitative CT analysis in patients with pulmonary emphysema: is lung function influenced by concomitant unspecific pulmonary fibrosis?

,†Int. J. Chron. Obstruct. Pulmon. Dis., vol. Volume 14, pp. 1583–1593, 2019.

R. R. Wildeboer et al., “Automated multiparametric localisation of prostate cancer based on B-mode , shear-wave elastography , and contrast-enhanced ultrasound radiomics,†2019.

M. Zhang et al., “Diagnostic performance of multiparametric transrectal ultrasound in localised prostate cancer: A comparative study with magnetic resonance imaging,†J. Ultrasound Med., vol. 38, no. 7, pp. 1823–1830, 2019.

R. Cuocolo et al., “Machine learning applications in prostate cancer magnetic resonance imaging,†Eur. Radiol. Exp., vol. 3, no. 1, 2019.

B. Badic et al., “Radiogenomics-based cancer prognosis in colorectal cancer,†Sci. Rep., vol. 9, no. 1, pp. 1–7, 2019.

V. Nardone et al., “Magnetic-resonance-imaging texture analysis predicts early progression in rectal cancer patients undergoing neoadjuvant chemoradiation,†Gastroenterol. Res. Pract., vol. 2019, pp. 1–9, 2019.

M. del C. V. Hernández et al., “Application of texture analysis to study small vessel disease and blood-brain barrier integrity,†Front. Neurol., vol. 8, no. JUL, 2017.

N. Jahani et al., “Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration,†Sci. Rep., vol. 9, no. 1, pp. 1–12, 2019.

R. D. Chitalia and D. Kontos, “Role of texture analysis in breast MRI as a cancer biomarker: A review,†J. Magn. Reson. Imaging, vol. 49, no. 4, pp. 927–938, 2019.

C.-C. Chang, C.-J. Chen, W.-L. Hsu, S.-M. Chang, Y.-F. Huang, and Y.-C. Tyan, “Prognostic Significance of Metabolic Parameters and Textural Features on 18F-FDG PET/CT in Invasive Ductal Carcinoma of Breast,†Sci. Rep., vol. 9, no. 1, pp. 1–11, 2019.

L. Sannachi et al., “Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models,†Transl. Oncol., vol. 12, no. 10, pp. 1271–1281, 2019.

M. A. M. Shukran, N. S. M. Ahmad, S. Ramli, and F. Rahmat, “Melanoma cancer diagnosis device using image processing techniques,†Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 490–494, 2019.

N. K. El Abbadi and Z. Faisal, “Detection and analysis of skin cancer from skin lesions,†Int. J. Appl. Eng. Res., vol. 12, no. 19, pp. 9046–9052, 2017.

E. Mohammed and M. E., “Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review,†Int. J. Comput. Appl., vol. 178, no. 17, pp. 37–43, 2019.

R. Sahoo and C. Sekhar, “Detection of Diabetic Retinopathy from Retinal Fundus Image using Wavelet based Image Segmentation,†Int. J. Comput. Appl., vol. 182, no. 47, pp. 46–50, 2019.

A. Imran, J. Li, Y. Pei, J.-J. Yang, and Q. Wang, “Comparative Analysis of Vessel Segmentation Techniques in Retinal Images,†IEEE Access, vol. 7, pp. 114862–114887, 2019.

L. Yu et al., “Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis,†BMC Cancer, vol. 19, no. 1, pp. 1–12, 2019.

B. Owen, D. Gandara, K. Kelly, E. Moore, D. Shelton, and F. Knollmann, “CT volumetry and basic texture analysis as surrogate markers in advanced non-small cell lung cancer,†Clin. Lung Cancer, 2019.

T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,†Pattern Recognit., vol. 29, no. 1, pp. 51–59, 1996.

R. Mehta and K. Egiazarian, “Rotated Local Binary Pattern (RLBP): Rotation invariant texture descriptor,†ICPRAM 2013 - Proc. 2nd Int. Conf. Pattern Recognit. Appl. Methods, pp. 497–502, 2013.

R. Mehta and K. Egiazarian, “Dominant Rotated Local Binary Patterns (DRLBP) for texture classification,†Pattern Recognit. Lett., vol. 71, pp. 16–22, 2016.

Y. Guo, G. Zhao, and M. Pietikäinen, “Texture classification using a linear configuration model based descriptor,†BMVC 2011 - Proc. Br. Mach. Vis. Conf. 2011, pp. 1–10, 2011.

M. Hall, “Correlation-based Feature Selection for Machine Learning,†Methodology, vol. 21i195-i20, no. April, pp. 1–5, 1999.

M. Lewandowski, D. Makris, and J. C. Nebel, “Automatic configuration of spectral dimensionality reduction methods,†Pattern Recognit. Lett., vol. 31, no. 12, pp. 1720–1727, 2010.

K. Pearson, “and the Chi-squared Test,†vol. 51, no. 1, pp. 59–72, 2014.

D. Roobaert, G. Karakoulas, and N. V Chawla, “Information Gain , Correlation and Support Vector Machines,†Featur. Extr. Found. Appl., vol. 470, no. 2006, pp. 463–470, 2006.

S. Lei, “A Feature Selection Method Based on Information Gain and Genetic Algorithm,†2012 Int. Conf. Comput. Sci. Electron. Eng., pp. 355–358, 2012.

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.

K. Kira and L. Rendell, “A practical approach to feature selection,†Proceedings of the Ninth International Conference on Machine Learning. pp. 249–256, 1994.

J. R. Quinlan, “Induction of Decision Trees,†Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986.

G. I. Webb, J. R. Boughton, and Z. Wang, “Not so naive Bayes: Aggregating one-dependence estimators,†Mach. Learn., vol. 58, no. 1, pp. 5–24, 2005.

N. Friedman, K. Murphy, and S. Russell, “Learning the structure of dynamic probabilistic networks,†Proc. Fourteenth , 1998.

D. Lowd and P. Domingos, “Naive Bayes models for probability estimation,†ICML 2005 - Proc. 22nd Int. Conf. Mach. Learn., no. May, pp. 529–536, 2005.

C. Cortes and V. Vapnik, “Support-Vector Networks,†Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.

K. A. Lorenz, “Modeling Binary Correlated Responses using,†pp. 17–24, 2015.

J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,†Adv. kernel methods, pp. 185–208, 1998.

H. J. Kelley, “Gradient Theory of Optimal Flight Paths,†ARS J., vol. 30, no. 10, pp. 947–954, 1960.




DOI: http://dx.doi.org/10.18517/ijaseit.10.6.8279

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development