cigl logo

Clinical Image Guidance Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Oriented Speckle Reducing Anisotropic Diffusion

Institution:
Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. karl@bwh.harvard.edu
Publisher:
IEEE Trans Image Process
Publication Date:
May-2007
Volume Number:
16
Issue Number:
5
Pages:
1412-1224
Citation:
IEEE Trans Image Process. 2007 May;16(5):1412-24.
PubMed ID:
17491469
Keywords:
Filtering, Anisotropic Diffusion, Ultrasound, Local Statistics, Speckle
Appears in Collections:
SPL, CIGL, LMI, NAC
Generated Citation:
Krissian K., Westin C-F., Kikinis R., Vosburgh K.G. Oriented Speckle Reducing Anisotropic Diffusion. IEEE Trans Image Process. 2007 May;16(5):1412-24. PMID: 17491469.
Downloaded: 2447 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

Ultrasound imaging systems provide the clinician with noninvasive, low-cost, and real-time images that can help them in diagnosis, planning, and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The speckle reducing anisotropic diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2-D synthetic image with two different levels of multiplicative noise and on a 3-D synthetic image of a Y-junction, and the new filter is applied on a 3-D real ultrasound image of the liver.

Additional Material
1 File (214.342kB)
Krissian-IEEE-TIP2007-fig6.jpg (214.342kB)