Edge detection in image sequence processing

Klaus Haarbeck; Johannes Bernarding; Brian Lofy; Jack Sklansky; Thomas Tolxdorff

doi:10.1117/12.274158

25 April 1997 Edge detection in image sequence processing

Klaus Haarbeck, Johannes Bernarding, Brian Lofy, Jack Sklansky, Thomas Tolxdorff

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Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274158
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

Images with a low signal-to-noise ratio (SNR) were processed with different algorithms based on the anisotropic diffusion (AD). This algorithm reduces the noise while preserving or enhancing the edges. Since sequences provide more image information, we developed an extension of the AD. In the modified AD the diffusion coefficients are used to vary the contrast normalization of successive frames. The Chamfer distance was used to measure the displacement of edges between the original and the processed images. Phantom images with varying gray levels and SNR, with fluctuating borders and with gross distortions were tested, as were clinical ultrasound images of the abdomen. The feed forward anisotropic diffusion (FFAD) scheme showed improved edge preserving capability for the phantom images as compared to the AD for the phantom images. Transferring the image information stabilized the edge detection even in cases where gross distortions or fluctuating contrast due to overall signal intensity changes led to geometric shifts in AD. Applying the FFAD to ultrasound images, the differences were less pronounced partly because of the different noise behavior. Keywords: Noise reduction, image-sequence processing, anisotropic diffusion, edge detection

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Klaus Haarbeck, Johannes Bernarding, Brian Lofy, Jack Sklansky, and Thomas Tolxdorff "Edge detection in image sequence processing", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274158

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KEYWORDS

Diffusion

Image processing

Ultrasonography

Anisotropic diffusion

Signal to noise ratio

Edge detection

Denoising

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