CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Combining Clustering, Morphology and Metaheuristic Optimization Technique for Segmentation of Breast Ultrasound Images to Detect Tumors

by Prabusankarlal. K. M, Thirumoorthy. P, Manavalan. R
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 14
Year of Publication: 2014
Authors: Prabusankarlal. K. M, Thirumoorthy. P, Manavalan. R
10.5120/15055-3465

Prabusankarlal. K. M, Thirumoorthy. P, Manavalan. R . Combining Clustering, Morphology and Metaheuristic Optimization Technique for Segmentation of Breast Ultrasound Images to Detect Tumors. International Journal of Computer Applications. 86, 14 ( January 2014), 28-34. DOI=10.5120/15055-3465

@article{ 10.5120/15055-3465,
author = { Prabusankarlal. K. M, Thirumoorthy. P, Manavalan. R },
title = { Combining Clustering, Morphology and Metaheuristic Optimization Technique for Segmentation of Breast Ultrasound Images to Detect Tumors },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 14 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 28-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number14/15055-3465/ },
doi = { 10.5120/15055-3465 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:14.091855+05:30
%A Prabusankarlal. K. M
%A Thirumoorthy. P
%A Manavalan. R
%T Combining Clustering, Morphology and Metaheuristic Optimization Technique for Segmentation of Breast Ultrasound Images to Detect Tumors
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 14
%P 28-34
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A framework which combines morphological operations and metaheuristic optimization technique with clustering method for the precise segmentation of breast tumours using ultrasound images is proposed in this study. Malignant tumours are pernicious when neglected to detect and treat at the earliest. Women with dense breasts are more prone to this malady and ultrasonagraphy is the suitable screening cum diagnosis method to aid the physician to estimate the amount of malignancy. This method is exclusively proposed for segmenting B- mode breast ultrasound images, characterized by low contrast and critically affected by speckle noise which hinders the finer details. The images are median filtered initially, in order to suppress the speckle noise and they are enhanced by a sticks algorithm based filter. The clustering is performed by FCM algorithm which is optimized by Particle swarm optimization. Automated morphological operations are performed on the clustered image as post processing procedure to improve the accuracy. To evaluate the proposed method, a database of 32 pathologically proven breast lesion images including 18 benign cysts and 14 malignant tumours is used. The segmented contours are compared with manually delineated contours and obtained MR of 93. 24%, OF of 0. 903 and EF of 0. 1017. Moreover, the quantitative results are compared and analyzed with other existing methods and the values evidenced that the proposed method distinctly outperforms other methods.

References
  1. Lee. C,(2002),Screening mammography: proven benefit, continued controversy. Radiologic Clinics of North America. 40(3), 395-407.
  2. Jalalian. A, (2013) Computer-aided detection/ diagnosis of breast cancer in mammography and ultrasound: a review, Clinical Imaging 37 (2013) 420–426
  3. Cheng, H. D. , Shan, J. , Ju, W. , Guo, Y. , Zhang, L. (2010). Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition, 43(1), 299-317.
  4. Bird RE, Wallace T, Yankaskas B. 1992. Analysis of cancers missed at screening mammography. Radiology 184(3):613-7.
  5. Jesneck. J, Lo. J, Baker. J, (2007) . Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors, Radiology 244 (2) 390–398.
  6. Shankar. P, Piccoli. C, Reid. C, Forsberg. J, Goldberg. B, (2005) Application of the compound probability density function for characterization of breast masses in ultrasound B scans, Physics in Medicine and Biology 50 (10) 2241–2248.
  7. Anderson. B, Shyyan. R, Eniu. A, Smith. R, Yip. C, (2006) Breast cancer in limited- resource countries: an overview of the breast health global initiative 2005 guidelines, The Breast Journal 12 S3–15.
  8. Drukker. K, Giger. M. L, Horsch. K, Kupinski. M. A, Vyborny. C. J, Mendelson. E. B, (2002) Computerized lesion detection on breast ultrasound, Medical Physics 29 (7) 1438–1446.
  9. Huang. Y. L, ChenD. R, Liu. Y. K, (2004), Breast cancer diagnosis using image retrieval for different ultrasonic systems, in: International Conference on Image Processing, vol. 5, 2598–2960.
  10. 'Ultrasoundcases',http://ultrasoundcases. info/category. aspx?cat=67 , Accessed October 2013
  11. Bridal. S. L, Correas. J. M, Saied. A, Laugier. P, (2003), Milestones on the road to higher resolution, quantitative, and functional ultrasonic imaging, Proceedings of the IEEE, vol. 91, no. 10, 1543–1561.
  12. Wagner. R. F, Smith. S. W, Sandrik. J. M, Lopez. H, (1983) Statistics of speckle in ultrasound B-scans, IEEE Trans. Sonics Ultrason. 30 ,156–163.
  13. Madabhushi. A, Metaxas. D. N, (2003) Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions, IEEE Trans. Med. Imag. 22 ,155–169.
  14. Kanungo, T. , Mount, D. M. , Netanyahu, N. S. , Piatko, C. , Silverman, R. , Wu, A. Y. (2000). The analysis of a simple k-means clustering algorithm. Proc. of sixteenth ACM annual symposium on Computational geometry(pp. 100-109).
  15. Izakian, H. , Abraham, A. , Snasel, V. (2009). Fuzzy clustering using hybrid Fuzzy C-Means and fuzzy particle swarm optimization. Proc. of IEEE World Congress on Nature & Biologically Inspired Computing, pp. 1690-1694.
  16. Bezdek, J. C. , Ehrlich, R. , Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2), 191-203.
  17. Groll, L. , Jakel, J. (2005). A new convergence proof of Fuzzy C-Means. Fuzzy Systems, IEEE Transactions on, 13(5), 717-720.
  18. Mehdizadeh, E. , Sadi-Nezhad, S. , Tavakkoli-Moghaddam, R. (2008). Optimization of fuzzy clustering criteria by a hybrid PSO and Fuzzy C-Means clustering algorithm. Iranian Journal of Fuzzy Systems, 5(3).
  19. Kennedy. J. F, Kennedy. J, Eberhart. R. C,(2001) Swarm intelligence, Acdemic Press.
  20. Bilal . M. Zahran , Kanaan. G,(2009), Text Feature Selection using Particle Swarm Optimization Algorithm, World Applied Sciences Journal 7 (Special Issue of Computer & IT): 69-74.
  21. Eng, H. L. , . Ma, K. K. (2001). Noise adaptive soft-switching median filter. IEEE Transactions on Image Processing, , 10(2), 242-251.
  22. Czerwinski. R. N, Jones. D. L, O'Brien. W. D, (1999),Detection of lines and boundaries in speckle images—Application to medical ultrasound, IEEE Trans. Med. Imag. , vol. 18, no. 2, pp. 126–136.
  23. Yih, J. M. , Lin, Y. H. , Liu, H. C. (2007). Clustering Analysis Method based on Fuzzy C-Means Algorithm of PSO and PPSO with Application in Real Data. International Journal of Geology, 4(1).
  24. Qian Z. Y, Hua G. W, Cheng C. Z, Tian T. J, Yun L. L, (2005). Medical Images Edge Detection Based on Mathematical Morphology, Proc of IEEE Engineering in Medicine and Biology 27th Annual Conference, 6492-6495.
  25. Huang, Y. L. , Jiang, Y. R. , Chen, D. R. , & Moon, W. K. (2007). Level set contouring for breast tumor in sonography. Journal of digital imaging, 20(3), 238-247.
Index Terms

Computer Science
Information Sciences

Keywords

Breast Ultrasound Segmentation Clustering Morphology Particle swarm optimization.

Powered by PhDFocusTM