CFP last date
20 May 2024
Reseach Article

Article:PSO Aided Neuro Fuzzy Inference System for Ultrasound Image Segmentation

by Alamelumangai. N, Dr. DeviShree. J
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 14
Year of Publication: 2010
Authors: Alamelumangai. N, Dr. DeviShree. J
10.5120/1330-1667

Alamelumangai. N, Dr. DeviShree. J . Article:PSO Aided Neuro Fuzzy Inference System for Ultrasound Image Segmentation. International Journal of Computer Applications. 7, 14 ( October 2010), 16-20. DOI=10.5120/1330-1667

@article{ 10.5120/1330-1667,
author = { Alamelumangai. N, Dr. DeviShree. J },
title = { Article:PSO Aided Neuro Fuzzy Inference System for Ultrasound Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { October 2010 },
volume = { 7 },
number = { 14 },
month = { October },
year = { 2010 },
issn = { 0975-8887 },
pages = { 16-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume7/number14/1330-1667/ },
doi = { 10.5120/1330-1667 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:56:17.468451+05:30
%A Alamelumangai. N
%A Dr. DeviShree. J
%T Article:PSO Aided Neuro Fuzzy Inference System for Ultrasound Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 7
%N 14
%P 16-20
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Individual micro calcifications are difficult to be detected as they are variable in shape and size and may be embedded in areas of dense parenchymal tissues. One of the most important problems of medical diagnosis, in general, is the subjectivity of the pattern recognition by diagnosis experts. This is due to the fact that the results are depended on the interpretation of the input from the patients but not on systematic procedure. In this paper, an adaptive neuro-fuzzy model optimized by PSO algorithms has been proposed. The symptoms and signs are gathered and the fuzzy membership values are defined. Feed forward multilayer networks are used to accept the fuzzy input values and is trained using back-propagation algorithm. The system is tested for detecting the micro-calcifications in breast sonograms. Later the results are compared for its performance.

References
  1. Alturki F. A. and Abdennour A. B. 1999, 'Neuro-fuzzy control of a steam boiler turbine unit', Proceeding of the IEEE, International Conference on Control Applications, (1999), 1050-1055.
  2. Benecchi, L., Neuro-fuzzy system for prostate cancer diagnosis. Urology. v68 i2(2009), pp.357-361.
  3. Breastcancer.org. www.breastcancer.org, (2009).
  4. Cancer.org. www.cancer.org, (2010).
  5. Cheng.H.D, Juan Shan, Wen Ju, Yanhui Guo and Ling Zang, Automated Breast Cancer Detection and Classification using Ultrasound images- A Survey, Pattern Recognition, Vol. 43, No. 1 (2010), pp. 299-317.
  6. Cheng.H.D, Cai.X., Chen. X., Hu.L., and Lou.X. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36, 12 (2003), pp.2967-2991.
  7. Gonzalez.R.C and Woods.R.E, Digital Image Processing, 2nd ed. Prentice Hall, 2002.
  8. Guo.Y.H, Cheng.H.D, Huang.J.H., Tian, J.W., Zhao, W., Sun, L.T., and Su, Y.X. Breast ultrasound image enhancement using fuzzy logic. Ultrasound in Medicine and Biology 32, 2 (2006), pp. 237-24.
  9. Joseph, Y.L. and Carey, E.F. Application of artificial neural networks for diagnosis of breast cancer. In Proceedings of the Congress of Evolutionary Computation, (1999), pp.1755-1759.
  10. Kennedy.J and Eberhart. R, Particle swarm optimization, Neural Networks, 1995. Proceedings., IEEE International Conference on, (1995), pp. 1942-1948.
  11. Lee, B., Yan, J.-Y., and Zhuang, T.-G. A dynamic programming based algorithm for optimal edge detection in medical images,. In Proceedings of the International Workshop on Medical Imaging and Augmented Reality, (2001), pp. 193 -198.
  12. Lorenz, A., Blüm, M., Ermert, H. and Senge, T., Comparison of different neuro-fuzzy classification systems for the detection of prostate cancer in ultrasonic images. In: IEEE Ultrasonic Symposium, pp. 1201-1204.+
  13. Malik Braik, Alaa Sheta,Aladdin Ayesh. In the Proceedings of the World Congress on Engineering (2007) Vol I
  14. Russo. F. Evolutionary Neuro-Fuzzy Systems for noise cancellation in image data, IEEE Transactions on Instrumentation and Measurement, Vol.48 (5), (1999), pp.915-920.
  15. Shrimali, V., Anand, R.S., Kumar, V. and Srivastav, R.K. Medical feature-based evaluation of structuring elements for morphological enhancement of ultrasonic images. Journal of Medical Engineering and Technology 33, 2 (2009), pp.158-169.
  16. Suhail M. Odeh, Using An Adaptive Neuro-Fuzzy Inference System (ANFIS) Algorithm For Automatic Diagnosis Of Cancer, Proceedings of European, Mediterranean & Middle Eastern Conference on Information Systems (2010).
  17. Yan.S, Yuan.S.J, and Hou, C. Ultrasound image enhancement for HIFU lesion detection and measurement. In 9th International Conference on Electronic Measurement and Instruments, (2009), pp.193-196.
  18. Yanhui Guo, Cheng.H.D, Jaiwei Tian, Yingtao Zhang. A Novel Approach to Breast Ultrasound Segmentataion Based on Characteristics of Breast Tissue and Particle Swarm Optimization, Proceedings of the 11th Joint Conference on Information Sciences (2008).
Index Terms

Computer Science
Information Sciences

Keywords

Sonograms micro-calcifications fuzzy systems neural networks