This article is part of the supplement: The 2009 International Conference on Bioinformatics & Computational Biology (BioComp 2009)
Speckle reducing bilateral filter for cattle follicle segmentation
1 Image Processing and Bioimaging Research Laboratory, System Research Institute & Department of Advanced Technologies, Alcorn State University, Alcorn State, MS, USA
2 School of Computing, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406-0001, USA
3 SpecPro Inc, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA
4 The Fifth Hospital of Harbin, Harbin, Heilongjiang, China
Citation and License
BMC Genomics 2010, 11(Suppl 2):S9 doi:10.1186/1471-2164-11-S2-S9Published: 2 November 2010
Ultrasound imaging technology has wide applications in cattle reproduction and has been used to monitor individual follicles and determine the patterns of follicular development. However, the speckles in ultrasound images affect the post-processing, such as follicle segmentation and finally affect the measurement of the follicles. In order to reduce the effect of speckles, a bilateral filter is developed in this paper.
We develop a new bilateral filter for speckle reduction in ultrasound images for follicle segmentation and measurement. Different from the previous bilateral filters, the proposed bilateral filter uses normalized difference in the computation of the Gaussian intensity difference. We also present the results of follicle segmentation after speckle reduction. Experimental results on both synthetic images and real ultrasound images demonstrate the effectiveness of the proposed filter.
Compared with the previous bilateral filters, the proposed bilateral filter can reduce speckles in both high-intensity regions and low intensity regions in ultrasound images. The segmentation of the follicles in the speckle reduced images by the proposed method has higher performance than the segmentation in the original ultrasound image, and the images filtered by Gaussian filter, the conventional bilateral filter respectively.