Oriented Speckle Reducing Anisotropic Diffusion

• Published in: IEEE transactions on image processing 2007-05, Vol.16 (5), p.1412-1424
• Main Authors: Krissian, K., Westin, C.-F., Kikinis, R., Vosburgh, K.G.
• Format: Article
• Language: English
• Subjects: Algorithms; Anisotropic diffusion; Anisotropic magnetoresistance; Anisotropy; Applied sciences; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Curvature; Detection, estimation, filtering, equalization, prediction; Diffusion; Exact sciences and technology; Filtering; Filters; Filtration; Geometry; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Imaging, Three-Dimensional - methods; Information, signal and communications theory; Liver - diagnostic imaging; local statistics; Medical management aid. Diagnosis aid; Medical sciences; Medical treatment; Noise; Noise reduction; Real time systems; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal, noise; Speckle; Stone; Telecommunications and information theory; Ultrasonic imaging; Ultrasonography - methods; Ultrasound
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2007.891803

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