Texture Feature Extraction of Infrared River Ice Images using Second-Order Spatial Statistics

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.

• Bharathi P. T
• P. Subashini

• Gray Level Difference Method
• Gray Level Run Length Method
• Kurtosis
• Probabilistic Neural Network
• Skewness
• Spatial Gray Level Dependence Method.

[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
(www.fp.ucalgary.ca/mhallbey/the-glcm.htm, 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.