Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31231
Texture Feature Extraction of Infrared River Ice Images using Second-Order Spatial Statistics

Authors: Bharathi P. T, P. Subashini


Ice cover County has a significant impact on rivers as it affects with the ice melting capacity which results in flooding, restrict navigation, modify the ecosystem and microclimate. River ices are made up of different ice types with varying ice thickness, so surveillance of river ice plays an important role. River ice types are captured using infrared imaging camera which captures the images even during the night times. In this paper the river ice infrared texture images are analysed using first-order statistical methods and secondorder statistical methods. The second order statistical methods considered are spatial gray level dependence method, gray level run length method and gray level difference method. The performance of the feature extraction methods are evaluated by using Probabilistic Neural Network classifier and it is found that the first-order statistical method and second-order statistical method yields low accuracy. So the features extracted from the first-order statistical method and second-order statistical method are combined and it is observed that the result of these combined features (First order statistical method + gray level run length method) provides higher accuracy when compared with the features from the first-order statistical method and second-order statistical method alone.

Keywords: Skewness, kurtosis, probabilistic neural network, Gray Level Difference Method, Gray Level Run Length Method, Spatial Gray Level Dependence Method

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2579


[1] Robert M Haralick et al, "Textural features for Image classification", IEEE Transactions on systems, Man and cybernetics, Vol. Smc - 3, No. 6, November 1973, pp 610 - 621.
[2] Hall - Beyer. M, "The GLCM Texture tutorial", version 2.8 (, university of Calgary, Canada).
[3] K. Venkat Ramana and B. Ramamoorthy, "Statistical Methods to Compare the Texture Features of Machined Surfaces", Pattern Recognition, Vol. 29, No. 9, pp. 1447 1459, 1996.
[4] Michael E. Mavroforakis et al, "Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers", Artificial Intelligence in Medicine (2006) 37, 145ÔÇö162.
[5] Galloway M, "Texture analysis using gray level run lengths", Comp Graph Im Proc 1975; 4:172-9.
[6] Shoshana Rosskamm, "Computer Aided Diagnosis of Cystic Fibrosis and Pulmonary Sarcoidosis using Texture Descriptors Extracted from CT Images", thesis for the Master of Science degree of Applied Mathematics 2010.
[7] Stavroula G. Mougiakakou et al, "Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers", Elsevier Artificial Intelligence in Medicine (2007) 41, 25ÔÇö37.
[8] Wei-Ming Chen et al., "3-D Ultrasound Texture Classification using Run Difference Matrix", Elsevier Ultrasound in Med. & Biol., Vol. 31, No. 6, pp. 763-770, 2005.
[9] Chu, C. M. Sehgal, and J. F. Greenleaf, "Use of gray value distribution of run lengths for texture analysis", Pattern Recognit. Lett, Vol. 11, pp. 415-420. June 1990.
[10] Fritz Albregtsen, "Statistical Texture Measures Computed from Gray Level Coocurrence Matrices", Image Processing Laboratory Department of Informatics University of Oslo, November 5, 2008.
[11] M. M. Mokji, "Gray Level Co-Occurrence Matrix Computation Based on Haar Wavelet", IEEE Trans, Computer Graphics, Imaging and Visualisation CGIV 2007.
[12] E. R. Davies, "Introduction to Texture Analysis", Handbook of Texture Analysis ┬® Imperial College Press.
[13] Foucherot et al., "New methods for analysing colour texture based on the Karhunen-Loeve transform and quantification", Pattern Recognition Society. Published by Elsevier Ltd, 2004.
[14] D. Chappard et al., "Image analysis measurements of roughness by texture and fractal analysis correlate with contact profilometry", Elsevier, Biomaterials 24, 2003, page no. 1399-1407.
[15] Yves Gauthier et al., "A combined classification scheme to characterize river ice from SAR data", EARSel eProceedings 5, 1/2006.
[16] Bharathi .P. T and P. Subashini, "De-noising filters for impulse noise in Glacier ice Infrared images", International Conference on Advances in image Processing and Computation Techniques (PCT) 2012.
[17] Bharathi P.T and P. Subashini, "Automatic identification of noise in ice images using statistical features", Fourth International Conference on Digital Image Processing (ICDIP), April 2012.
[18] Xiaoou Tang, "Texture Information in Run-Length Matrices", IEEE Transactions On Image Processing, Vol. 7, No. 11, November 1998.
[19] Mihran Tuceryan and Anil K. Jain, "Texture Analysis", The Handbook of Pattern Recognition and Computer Vision (2nd Edition), by C. H. Chen, L. F. Pau, P. S. P. Wang (eds.), pp. 207-248, World Scientific Publishing Co., 1998.
[20] G.R.J. Cooper et al., "The use of textural analysis to locate features in geophysical data", Elsevier Computers & Geosciences 31 2005, page no. 882-890.
[21] Christopher Spain, "Automatic Detection of Explosive Devices in Infrared Imagery using Texture with Adaptive Background Mixture Models", Master Of Science Thesis, May 2011.
[22] F. R. Renzetti, L. Zortea, "Use of a gray level co-occurrence matrix to characterize duplex stainless steel phases microstructure", Fratturaed Integrità Strutturale, 16 (2011) 43-51.
[23] Graziano Aretusi et al., "Texture Analysis in Thermal Infrared Imaging for Classification ff Raynaud-s Phenomenon".
[24] Chaoxin Zheng et al., "Recent applications of image texture for evaluation of food qualitiesÔÇöa review", Elsevier Trends in Food Science & Technology 17 (2006) 113-128.
[25] Patel, D., Hannah, I., & Davies, E. R, “Foreign object detection via texture analysis”, 12th IAPR international conference on pattern recognition Proceeding: Vol. 1. Conference A: Computer vision and image processing, 1994.
[26] Li, J., Tan, J., & Shatadal, P, “Classification of tough and tender beef by image texture analysis”, Meat Science, 57, 341–346, 2001.
[27] Bharati M. H, Liu J. J & MacGregor J. F, “Image texture analysis: methods and comparisons”, Chemometrics and Intelligence Laboratory Systems, 72, 57–71, 2004.
[28] G. N. Srinivasan, and Shobha G, “Statistical Texture Analysis”, Proceedings of World Academy of Science, Engineering and Technology Volume 36 December 2008 ISSN 2070-3740.
[29] Ojala, T. and M Pietikäinen, “Texture Classification”, Machine Vision and Media Processing Unit, University of Oulu, Finland.
[30] D. H. TRAN et al, “Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes”, Urban Water Journal, Vol. 3, No. 3, September 2006, 175 – 184.
[31] Padma and Dr.R.Sukanesh, “A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Optimal Statistical Texture Features”, International Journal of Image Processing (IJIP), Volume (5) : Issue (5) : 2011.