doi: 10.7763/IJMO.2012.V2.117
US Image Segmentation Based on Expectation Maximization and Gabor Filter
Anita Khanna
1
,
Meenakshi Sood
2
,
Swapna Devi
3
- 1 Guru Ghasidas Vishwavidyalaya, Bilaspur (C.G), India.
- 2 National Institute of Technology, Hamirpur (H.P), India.
- 3 NITTTR, Chandigarh, India.
Abstract
Segmentation of Ultrasound (US) Images is quite challenging as the images are of poor quality, contains strong speckle noise and so are not of that high quality as CT or MRI images. Both supervised and unsupervised segmentation techniques are used for segmentation. Unsupervised segmentation approaches mainly rely on subjective assessment. We propose an unsupervised segmentation technique based on conventional Expectation Maximization (EM) algorithm applied on texture features extracted by a bank of Gabor filters. The approach includes three steps: decomposition of image using Gabor filters, texture feature extraction and segmentation. The segmentation results are compared with the work done using K-means clustering. K-means being a basic technique results in over-segmentation and converges in local minima. EM technique used after texture feature extraction is tested on many US images and results were quite satisfactory.
Keywords
- Expectation maximization
- feature extraction
- gabor filter
- multi-resolution analysis
How to Cite
Anita Khanna, Meenakshi Sood, and Swapna Devi, "US Image Segmentation Based on Expectation Maximization and Gabor Filter," International Journal of Modeling and Optimization, vol. 2, no. 3, pp. 230-233, 2012. https://doi.org/ 10.7763/IJMO.2012.V2.117
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).