Abstract

Texture analysis of X-ray bone image using Laws’ mask for direct evaluation of the bone quality has been popular. Nevertheless, detailed reliability evaluation of the system classification has been relatively unknown. In this study, we will examine the reliability of the Laws’ mask system classification by using the confusion matrix approach. The precise detection system by using standard deviation statistical descriptor is supported by the true positive of 87.5% and true negative of 83.33%. In conclusion, the statistical analysis of the texture based osteoporosis detection system’s reliability discloses a true potential in this detection technique. Nevertheless, future researches should include a larger image database to enhance the reliability of the results.

Keywords:

Confusion matrix, osteoporosis, statistical descriptor, texture,

References

1. Bauer, J.S. and T.M. Link, 2009. Advances in osteoporosis imaging. Eur. J. Radiol., 71: 440-449.
   CrossRef    PMid:19651482    
2. Bharati, M.H., J.J. Liu and J.F. MacGregor, 2004. Image texture analysis: Methods and comparisons. Chemometr. Intell. Lab., 72: 57-71.
   CrossRef    
3. Chauhan, R. and S.S. Bhadoria, 2011. An improved image contrast enhancement based on histogram equalization and brightness preserving weight clustering histogram equalization. Proceeding of the International Conference on Communication Systems and Network Technologies (CSNT). Katra, Jammu, pp: 597-600.
   CrossRef    
4. Clausi, D.A., 2002. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens., 28: 45-62.
   CrossRef    
5. Danielsson, P.E., 1980. Euclidean distance mapping. Comput. Vision Graph., 14: 227-248.
   CrossRef    
6. Davies, E.R., 2008. Introduction to Texture Analysis. In: Mirmehdi, M., X. Xie and J. Suri (Eds.), Handbook of Texture Analysis. Imperial College Press, pp: 1-31.
   CrossRef    
7. Daxin, T., W. Yunpeng, L. Guangquan and Y. Guizhen, 2010. A VANETs routing algorithm based on Euclidean distance clustering. Proceeding of the International Conference on Future Computer and Communication (ICFCC), pp: V1-183-V1-187.
   
8. Eastell, R., 2005. Osteoporosis. Medicine, 33: 61-65.
   CrossRef    
9. Gonzalez, R.C. and R.E. Woods, 2007. Digital Image Processing. 3rd Edn., Prentice Hall, NY.
   
10. Hong Seng, G. and T. Tian Swee, 2013a. Osteoarthritis detection system using optimal dynamic feature configuration. Int. J. Circ. Syst. Signal Process., 7: 231-239.
    
11. Hong Seng, G. and T. Tian Swee, 2013b. Spectral coefficient system for osteoarthritis detection. Int. J. Circ. Syst. Signal Process., 7: 151-159.
    
12. Laws, K.L., 1980. Textured image segmentation. Ph.D. Thesis, University of South California, Los Angeles, California.
    
13. Menotti, D., L. Najman, J. Facon and A.A. de Araujo, 2007. Multi-histogram equalization methods for contrast enhancement and brightness preserving. IEEE T. Consum. Electr., 53: 1186-1194.
    CrossRef    
14. Rachidi, K.L., 2008. Rapid texture identification. Proceeding of the SPIE Conference on Image Processing for Missile Guidance, pp: 376-380.
    
15. Rachidi, M., C. Chappard, A. Marchadier, C. Gadois, E. Lespessailles and C.L. Benhamou, 2008a. Application of Laws' masks to bone texture analysis: An innovative image analysis tool in osteoporosis. Proceeding of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp: 1191-1194.
    CrossRef    
16. Rachidi, M., C. Chappard, A. Marchadier, C. Gadois, E. Lespessailles and C.L. Benhamou, 2008b. Laws' masks descriptors applied to bone texture analysis: An innovative and discriminant tool in osteoporosis. Skeletal Radiol., 37: 541-548.
    CrossRef    PMid:18327577    
17. Rajavel, P., 2010. Image dependent brightness preserving histogram equalization. IEEE T. Consum. Electr., 56: 756-763.
    CrossRef    
18. Srinivasan, S.G. and G. Shobha, 2008. Statistical analysis. Proceedings of World Academy of Science, Engineering and Technology, 36: 6.
    
19. Tuceryan, M. and A.K. Jain, 1998. Texture Analysis. In: Chen, C.H., L.F. Pau and P.S.P. Wang (Eds.), The Handbook of Pattern Recognision and Computer Vision. World Scientific Publishing Co., Hackensack, NJ, 2: 204-248.
    
20. Wang, C. and Y. Zhongfu, 2005. Brightness preserving histogram equalization with maximum entropy: A variational perspective. IEEE T. Consum. Electr., 51: 1326-1334.
    CrossRef    
21. Yan Chai, H., T. Tianswee., G. Hongseng. and L. Khinwee, 2013. Multipurpose contrast enhancement on epiphyseal plates and ossification centers for bone age assessment. Biomed. Eng. Online, 12:1-19.
    
22. Zhan-Ao, X., C. Feng and W. Li-Ping, 2008. A weighting fuzzy clustering algorithm based on euclidean distance. Proceedings of the 2008 5th International Conference on Fuzzy Systems and Knowledge Discovery, October 18-20, 1: 172-175.
    

Competing interests

The authors have no competing interests.

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