Mr. Franck Davignon Profile

Franck Davignon

at INSA de Lyon

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Proceedings Article | 12 April 2005 Paper

Ultrasound data segmentation based on tissue characterization

Franck Davignon, Jean-Francois Deprez, Olivier Basset

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.594062

KEYWORDS: Image segmentation, Tissues, Ultrasonography, Signal attenuation, Ultrasonics, Image processing algorithms and systems, Digital signal processing, Image processing, Signal processing, Acoustics

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When an ultrasonic examination is performed, a segmentation tool would often be a very useful tool, either for the detection of pathologies, the early diagnosis of cancer, the follow-up of the lesions, ... Such a tool must be both reliable and accurate. However, because of the relatively poor quality of ultrasound images due to the speckled texture, the segmentation of ultrasound data is a difficult task. We have previously proposed to tackle the problem using a multiresolution bayesian region-based algorithm. Such an approach, applied to very noisy images, leads to good segmentation results. For computation time purposes, a multiresolution version has been implemented. In order to improve the quality of the segmentation, we propose to get more information about the properties of the tissues and take it into account during the segmentation process. Some acoustical parameters have thus been computed, either directly from the images or from the Radio-Frequency (RF) signal. The parameters used are the Integrated BackScatter (IBS), the density of scatterers, and the Mean Central Frequency, which is a measurement related to the attenuation of ultrasound waves in the media. To test the influence of the acoustical parameters in the segmentation process, a set of numerical phantoms has been computed using the Field software. Each phantom consists in two regions with different acoustical properties : the density of scatterers and the scattering amplitude. From both the simulated RF signal and images, parameters have been computed and segmentation has been processed for each phantom. The quantification of the segmentation quality is based on the number of correctly classified pixels and it has been computed for all the combinations of acoustical parameters. Segmentation results performed on agar-gelatine phantoms with different inclusions are also presented and illustrate the interest of a multiparametric segmentation approach.

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