The week's pick
Reseach Article
Application of Wavelet based K-means Algorithm in Mammogram Segmentation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 52 - Number 15 |
| Year of Publication: 2012 |
| Authors: Shruti Dalmiya, Avijit Dasgupta, Soumya Kanti Datta |
10.5120/8276-1883
|
Shruti Dalmiya, Avijit Dasgupta, Soumya Kanti Datta . Application of Wavelet based K-means Algorithm in Mammogram Segmentation. International Journal of Computer Applications. 52, 15 ( August 2012), 15-19. DOI=10.5120/8276-1883
Abstract
Research in image processing has gained lots of momentum during past two decades. Now-a-days image processing techniques have found their way into computer vision, image compression, image security, medical imaging and more. This paper presents a research on mammography images using wavelet transformation and K – means clustering for cancer tumor mass segmentation. The first step is to perform image segmentation. It allows distinguishing masses and micro calcifications from background tissue. In this paper wavelet transformation and K- means clustering algorithm have been used for intensity based segmentation. The proposed algorithm is robust against noise. In this case, discrete wavelet transform (DWT) is used to extract high level details from MRI images. The processed image is added to the original image to get the sharpened image. Then K-means algorithm is applied to the sharpened image in which the tumor region can be located using the thresholding method. This paper validates the algorithm by detecting tumor region from an MRI image of mammogram. The combination of noise-robust nature of applied processes and the simple K-means algorithm gives better results.
References
- Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Pearson Education, 2002. .
- S. Saheb Basha, Dr. K. Satya Prasad , Automatic detection of breast cancer mass in mammograms using morphological operators and Fuzzy C-means Clustering, Journal of Theoretical and Applied Information Technology,2009.
- Suzuki, H. and J. Toriwaki, 1991. Automatic Segmentation of Head MRI Images by Knowledge Guided Thresholding. Computer Med. Imaging Graph: The official J. Computerized Imaging Society, 15 (4): 233-240.
- Robb, R. A. , 2000. Biomedical Imaging, Visualization and Analysis. Wiley-Liss, USA.
- Li, N. , M. Liu and Y. Li, Image segmentation algorithm using watershed transform and level set method. In Proc. ICASSP 2007. IEEE International Conference on Acoustics, Speech and Signal Processing (2007), 1: I-613-I-616.
- Ahmed, M. and D. B. Mohammad, 2008. Segmentation of Brain MR Images for Tumor Extraction by Combining K-means Clustering and Perona Malik Anisotropic Diffusion Model. International Journal of Image Processing, 2: 27-34.
- Mostafa, M. G. , M. F. Tolba, T. F. Gharib and M. A. Mageed, Medical Image Segmentation Using Wavelet Based Mutiresolution EM Algorithm.
- Jung, C. , R. , "Combining wavelets and watersheds for robust multi scale image segmentation", Image and Vision Computing 25(2007), pp. 24–33.
- JA Hartigan, Clustering Algorithms, John Wiley & Sons, New York, 1975.
- Hartigan, J. A. , Wong, M. A. : Algorithm AS136: A K-Means Clustering Algorithm. Applied Statistics 28, pp. 100–108 (1979).
Index Terms
Keywords