Service Communautaire d'Information sur la Recherche et le Développement - CORDIS

Algorithms for ultrasonic image filtering and segmentation

Ultrasonic imaging has become an important modality in the field of medical imaging systems. Ultrasonic images suffer from a special kind of noise called speckle. Signal-adaptive techniques for speckle noise removal have been developed. Both envelope-detected and displayed ultrasonic images have been considered. Motivated by the observation that for displayed ultrasonic data the maximum likelihood (ML) estimator of the original (noiseless) signal closely resembles the L2 mean (ie the ML estimator of the original signal for envelope-detected data), signal-adaptive L2 mean filters have been designed for both cases. The derived filters have a homomorphic structure (ie the L2 mean filter) due to the speckle noise statistics (Rayleigh distribution or signal-dependent Gaussian distribution). Secondly, the segmentation of ultrasonic images using self-organizing neural networks has been implemented. A novel variant of Learning Vector Quantizer (LVQ) neural network has been developed that is able to segment ultrasonic images in classes representing various tissue and lesion characteristics. This is the so called L2 LVQ algorithm. It can be combined with signal-adaptive filtering in order to allow preservation of image edges and details as well as maximum speckle reduction in homogeneous regions. The design of filtering processes combining segmentation and optimum L-filtering, and their use for the suppression of speckle noise in ultrasonic images has been proved very successful in practice.


Ioannis PITAS, (Researcher)
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