Share This Article:

Generalized α-Entropy Based Medical Image Segmentation

Full-Text HTML XML Download Download as PDF (Size:1413KB) PP. 62-67
DOI: 10.4236/jsea.2014.71007    3,843 Downloads   5,649 Views   Citations

ABSTRACT

In 1953, Rènyi introduced his pioneering work (known as α-entropies) to generalize the traditional notion of entropy. The functionalities of α-entropies share the major properties of Shannons entropy. Moreover, these entropies can be easily estimated using a kernel estimate. This makes their use by many researchers in computer vision community greatly appealing. In this paper, an efficient and fast entropic method for noisy cell image segmentation is presented. The method utilizes generalized α-entropy to measure the maximum structural information of image and to locate the optimal threshold desired by segmentation. To speed up the proposed method, computations are carried out on 1D histograms of image. Experimental results show that the proposed method is efficient and much more tolerant to noise than other state-of-the-art segmentation techniques.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. Sadek and S. Abdel-Khalek, "Generalized α-Entropy Based Medical Image Segmentation," Journal of Software Engineering and Applications, Vol. 7 No. 1, 2014, pp. 62-67. doi: 10.4236/jsea.2014.71007.

References

[1] M. Albuquerque, I. A. Esquef and A. R. Gesualdi, “Image Thresholding Using Tsallis Entropy,” Pattern Recognition Letters, Vol. 25, No. 9, 2004, pp. 1059-1065. http://dx.doi.org/10.1016/j.patrec.2004. 03.003
[2] P.-L. Bazin and D. L. Pham, “Homeomorphic Brain Image Segmentation with Topological and Statistical Atlases,” Medical Image Analysis, Vol. 12, No. 5, 2008, pp. 616-625. http://dx.doi.org/10. 1016/j.media.2008.06.008
[3] P.-L. Bazin and D. L. Pham, “Topology Correction of Segmented Medical Images Using a Fast Marching Algorithm,” Programs in Biomedicine, Vol. 88, No. 2, 2007, pp. 182-290. http://dx.doi.org/ 10.1016/j.cmpb.2007.08.006
[4] J. C. Carter, D. C. Lanham, G. Bibat, S. Naidu and W. E. Kaufmann, “Selective Cerebral Volume Reduction in Rett Syndrome: A Multiple Approach MRI Study,” American Journal of Neuroradiology, Vol. 29, No. 3, 2008, pp. 436-441. http://dx.doi.org/10.3174/ajnr.A0857
[5] R. C. Gonzalez and R. E. Woods, “Digital Image Processing Using Matlab,” 2nd Edition, Prentice Hall, Inc., Upper Saddle River, 2003.
[6] W. E. L. Grimson, G. J. Ettinger, T. Kapur, M. E. Leventon and W. M. Wells, “Utilizing Segmented MRI Data in Image-Guided Surgery,” International Journal of Pattern Recognition and Artificial Intelligence, Vol. 11, No. 8, 1997, pp. 1367-1397. http://dx.doi.org/10.1142/S0218001497000639
[7] V. S. Khoo, D. P. Dearnaley, D. J. Finnigan, A. Padhani, S. F. Tanner and M. O. Leach, “Magnetic Resonance Imaging (MRI): Considerations and Applications in Radiotheraphy Treatment Planning,” Radiotherapy Oncology, Vol. 42, No. 1, 1997, pp. 1-15. http://dx.doi.org/10.1016/S0167-8140(96) 01866-X
[8] S. M. Larie and S. S. Abukmeil, “Brain Abnormality in Schizophrenia: A Systematic and Quantitative Review of Volumetric Magnetic Resonance Imaging Studies,” Journal of Psychiatry, Vol. 172, 1998, pp. 110-120.
[9] I. Levner and H. Zhang, “Classification-Driven Watershed Segmentation,” EEE Transactions on Image Processing, Vol. 16, No. 5, 2007, pp. 1437-1445. http://dx.doi.org/10.1109/TIP.2007.894239
[10] M. A. Mofaddel and S. Sadek, “Adult Image Content Filtering: A Statistical Method Based on Multi-Color Skin Modeling,” IEEE International Symposium on Signal Processing and Information Technology (ISSPIT’10), Luxor, 2010, pp. 366-370. http://dx.doi.org/10.1109/ISSPIT.2010.5711812
[11] A. Rényi, “On a Theorem of P. Erdǒs and Its Application in Information Theory,” Mathematica, Vol. 1, 1959, pp. 341-344.
[12] S. M. Resnick, D. L. Pham, M. A. Kraut, A. B. Zonderman and C. Davatzikos, “Longitudinal MRI Studies of Older Adults: A Shrinking Brain,” Journal of Neuroscience, Vol. 23, No. 8, 2003, pp. 3295-3301.
[13] S. Sadek, A. Al-Hamadi, M. Elmezain, B. Michaelis and U. Sayed, “Human Activity Recognition Using Temporal Shape Moments,” IEEE International Symposium on Signal Processing and Information Technology (ISSPIT’10), Luxor, 2010, pp. 79-84. http://dx.doi.org/10.1109/ISSPIT.2010.5711729
[14] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “A Fast Statistical Approach for Human Activity Recognition,” International Journal of Intelligence Science (IJIS), Vol. 2, No. 1, 2012, pp. 9-15.
[15] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “An Efficient Method for Real-Time Activity Recognition,” Proceedings of the International Conference on Soft Computing and Pattern Recognition (SoCPaR’10), Paris, 2010, pp. 7-10.
[16] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “An Image Classification Approach Using Multilevel Neural Networks,” Proceedings of IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS’09), Shanghai, 2009, pp. 180-183.
[17] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “Face Detection and Localization in Color Images: An Efficient Neural Approach,” Journal of Software Engineering and Applications (JSEA), Vol. 4, No. 12, 2011, pp. 682-687. http://dx.doi.org/10.4236/jsea.2011.412080
[18] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “Human Action Recognition via Affine Moment Invariants,” 21st International Conference on Pattern Recognition (ICPR’12), Tsukuba Science City, 2012, pp. 218-221.
[19] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “Human Action Recognition: A Novel Scheme Using Fuzzy Log-Polar Histogram and Temporal Self-Similarity,” EURASIP Journal on Advances in Signal Processing, 2011. http://dx.doi.org/10.1155/2011/540375
[20] S. Sadek, A. Al-Hamadi, A. Wannig, B. Michaelis and U. Sayed, “A New Approach to Image Segmentation via Fuzzification of Rènyi Entropy of Generalized Distributions. Proceedings of International Conference on Image, Signal and Vision Computing (ICISVC’09), Singapore, 2009, pp. 598-603.
[21] S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, “A Robust Neural System for Objectionable Image Recognition,” IEEE International Conference on Machine Vision (ICMV’09), 2009, pp. 32-36.
[22] S. Sadek, M. A. Mofaddel and B. Michaelis, “Multicolor Skin Modeling with Application to Skin Detection,” Journal of Computations & Modelling, Vol. 3, No. 1, 2013, pp. 153-167.
[23] C. E. Shannon and W. Weaver, “The Mathematical Theory of Communication,” University of Illinois Press, Urbana, 1949.
[24] W. Tatsuaki and S. Takeshi, “When Nonextensive Entropy Becomes Extensive,” Physica A, Vol. 301, No. 1-4, 2001, pp. 284-290. http://dx.doi.org/10.1016/S0378-4371(01)00400-9
[25] P. Taylor, “Invited Review: Computer Aids for Decision-Making in Diagnostic Radiology—A Literature Review,” British Journal of Radiology, Vol. 68, No. 813, 1995, pp. 945-957. http://dx.doi.org/10. 1259/0007-1285-68-813-945
[26] D. Tosun, M. E. Rettmann, X. Han, X. Tao, C. Xu, S. M. Resnick and J. L. Prince, “Cortical Surface Segmentation and Mapping,” NeuroImage, Vol. 23, No. 1, 2004, pp. S108-S118. http://dx.doi.org/10. 1016/j.neuroimage.2004.07.042
[27] C. Tsallis, S. Abe and Y. Okamoto, “Nonextensive Statistical Mechanics and Its Applications,” Series Lecture Notes in Physics, Springer, Berlin, 2001.
[28] A. J. Worth, N. Makris, V. S. Caviness and D. N. Kennedy, “Neuroanatomical Segmentation in MRI: Technological Objectives,” International Journal of Pattern Recognition and Artificial Intelligence, Vol. 11, No. 8, 1997, pp. 1161-1187. http://dx.doi.org/10.1142/S0218001497000548

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.