Advertisement

Partial Fractional Derivative (PFD) based Texture Analysis Model for Medical Image Segmentation

  • S. Hemalatha
  • S. Margret Anouncia
Chapter

Abstract

The early detection of diseases such as brain tumor and lung cancer is achieved through segmenting these images. As these images are typified to contain obscure structures, a precise segmentation method needs to be evolved. The principal idea is to devise an unsupervised segmentation technique for medical images involving a partial fractional derivative (PFD)-dependent texture extraction model and an unsupervised clustering algorithm. Basically, image segmentation process allocates different tags to diverse image regions. In this chapter, the process is considered to be texture-based segmentation by representing every tag with an exclusive texture label. The textural features are extorted using PFD-based model. The ISODATA clustering is suggested for segmentation through pixel-based classification. This process is experimented on different test cases such as images of lung cancer detection and brain tumor detection and is able to produce higher accuracy than existing methods.

Keywords

Partial fractional derivatives Texture analysis ISODATA clustering algorithm Lung cancer detection Brain tumor detection Confusion matrix Classification accuracy 

References

  1. 1.
    Chang, T., & Kuo, C. C. (1993). Texture analysis and classification with tree-structured wavelet transform. IEEE Transactions on Image Processing, 2(4), 429–441.CrossRefGoogle Scholar
  2. 2.
    Chen, Y. T. (2017). Medical image segmentation using independent component analysis-based kernelized fuzzy-means clustering. Mathematical Problems in Engineering, 2017.Google Scholar
  3. 3.
    Chuang, E. R., & Sher, D. (1993). \({\chi^{2}}\) test for feature detection. Pattern Recognition, 26(11), 1673–1681.Google Scholar
  4. 4.
    Cimpoi, M., Maji, S., Kokkinos, I., & Vedaldi, A. (2015). Deep filter banks for texture recognition, description, and segmentation. International Journal of Computer Vision, 1–30.Google Scholar
  5. 5.
    Costa, E., Lorena, A., Carvalho, A. C. P. L. F., & Freitas, A. (2007). A review of performance evaluation measures for hierarchical classifiers. In Evaluation methods for machine learning II: Papers from the AAAI-2007 workshop (pp. 1–6). Vancouver, Canada: AAAI.Google Scholar
  6. 6.
    Danesh, H., Kafieh, R., Rabbani, H., & Hajizadeh, F. (2014). Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts. Computational and Mathematical Methods in Medicine, 2014. Article ID. 479268.Google Scholar
  7. 7.
    Hamouchene, I., & Aouat, S. (2016). A new approach for texture segmentation based on NBP method. Multimedia Tools and Applications, 1–20.Google Scholar
  8. 8.
    Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621.CrossRefGoogle Scholar
  9. 9.
    He, D. C., & Wang, L. (1990). Texture unit, texture spectrum, and texture analysis. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 509–512.CrossRefGoogle Scholar
  10. 10.
    Hemalatha, S., & Anouncia, S. M. (2016). A computational model for texture analysis in images with fractional differential filter for texture detection. International Journal of Ambient Computing and Intelligence (IJACI), 7(2), 93–113.CrossRefGoogle Scholar
  11. 11.
    Hemalatha, S., & Anouncia, S. M. (2017). Unsupervised segmentation of remote sensing images using FD based texture analysis model and ISODATA. International Journal of Ambient Computing and Intelligence (IJACI), 8(3), 58–75.CrossRefGoogle Scholar
  12. 12.
    Iyer, M. (2014). Defect detection in pattern texture analysis. In Proceedings of the 2014 International Conference on Communications and Signal Processing (ICCSP) (pp. 172–175). Melmaruvathur, TN, India: IEEE.Google Scholar
  13. 13.
    Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264–323.CrossRefGoogle Scholar
  14. 14.
    Karthikeyan, T., & Krishnamoorthy, R. (2012). Autoregressive model based on Bayesian approach for texture representation. ICTACT Journal on Image and Video Processing, 3(1), 485–491.CrossRefGoogle Scholar
  15. 15.
    Korchiyne, R., Sbihi, A., Farssi, S. M., Touahni, R., & Alaoui, M. T. (2012, May). Medical image texture segmentation using multifractal analysis. In 2012 International Conference on Multimedia Computing and Systems (ICMCS) (pp. 422–425). IEEE.Google Scholar
  16. 16.
    Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.CrossRefGoogle Scholar
  17. 17.
    Memarsadeghi, N., Mount, D. M., Netanyahu, N. S., & Le Moigne, J. (2007). A fast implementation of the ISODATA clustering algorithm. International Journal of Computational Geometry & Applications, 17(01), 71–103.MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Nabizadeh, N., & Kubat, M. (2015). Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Computers & Electrical Engineering, 45, 286–301.CrossRefGoogle Scholar
  19. 19.
    Neel, M. C., & Joelson, M. (2011). Generalizing Grünwald-Letnikov’s formulas for fractional derivatives. In Proceedings of the 6th EUROMECH Nonlinear Dynamics Conference. Saint Petersburg, RUSSIA: IPACS Electronic Library.Google Scholar
  20. 20.
    Ojala, T., Valkealahti, K., Oja, E., & Pietikäinen, M. (2001). Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recognition, 34(3), 727–739.CrossRefMATHGoogle Scholar
  21. 21.
    Priestley, M. B., & Chao, M. T. (1972). Non-parametric function fitting. Journal of the Royal Statistical Society. Series B (Methodological), 385–392.Google Scholar
  22. 22.
    Pu, Y. F. (2010). Fractional differential mask: A fractional differential-based approach for multiscale texture enhancement. IEEE Transactions on Image Processing, 19(2), 491–511.MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Roy, M., Ghosh, S., & Ghosh, A. (2014). A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system. Information Sciences, 269, 35–47.CrossRefGoogle Scholar
  24. 24.
    Sezgin, M. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146–168.CrossRefGoogle Scholar
  25. 25.
    Sharma, N., Ray, A. K., Sharma, S., Shukla, K. K., Pradhan, S., & Aggarwal, L. M. (2008). Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network. Journal of Medical Physics, 33(3), 119.CrossRefGoogle Scholar
  26. 26.
    Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437.CrossRefGoogle Scholar
  27. 27.
    Targhi, A. T., Hayman, E., & Olof Eklundh, J. (2006, May). Real-time texture detection using the LU-transform. In Proceeding of the Workshop on Computation Intensive Methods for Computer Vision in Conjunction with ECCV (Vol. 713). Graz, Austria: Springer-Verlag Berlin Heidelberg.Google Scholar
  28. 28.
    The National Library of Medicine. (2010). MedPix, Online. Accessed December 15, 2016. Also available as https://medpix.nlm.nih.gov/home.
  29. 29.
    Tirandaz, Z., & Akbarizadeh, G. (2016). A two-phase algorithm based on kurtosis curvelet energy and unsupervised spectral regression for segmentation of SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(3), 1244–1264.CrossRefGoogle Scholar
  30. 30.
    Tsai, F., Chou, M. J., & Wang, H. H. (2005). Texture analysis of high resolution satellite imagery for mapping invasive plants. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (Vol. 4, pp. 3024–3027). Seoul: IEEE.Google Scholar
  31. 31.
    Vese, L. A., & Osher, S. J. (2003). Modeling textures with total variation minimization and oscillating patterns in image processing. Journal of Scientific Computing, 19(1–3), 553–572.MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Xie, X. (2008). A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 7(3).Google Scholar
  33. 33.
    Yoshimura, M. (1997, July). Edge detection of texture image using genetic algorithms. In I. SICE’97 (Ed.), Proceedings of the 36th SICE Annual Conference (pp. 1261–1266). Tokushima, Japan: IEEE.Google Scholar
  34. 34.
    Yu, H., Yang, W., Xia, G. S., & Liu, G. (2016). A color-texture-structure descriptor for high-resolution satellite image classification. Remote Sensing, 8(3), 259.CrossRefGoogle Scholar
  35. 35.
    Yuan, J., Wang, D., & Cheriyadat, A. M. (2015). Factorization-based texture segmentation. IEEE Transactions on Image Processing, 24(11), 3488–3497.MathSciNetCrossRefGoogle Scholar
  36. 36.
    Yue, J., Li, Z., Liu, L., & Fu, Z. (2011). Content-based image retrieval using color and texture fused features. Mathematical and Computer Modelling, 54(3), 1121–1127.CrossRefGoogle Scholar
  37. 37.
    Zhang, D. W. (2000). Content-based image retrieval using Gabor texture features. In Proceeding of the IEEE Pacific-Rim Conference on Multimedia (pp. 392–395). Shanghai, China: IEEE.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  2. 2.School of Computer Science and EngineeringVIT UniversityVelloreIndia

Personalised recommendations