This paper presents a deconvolution technique for ultrasound images based on a Maximum Likelihood (ML) estimation procedure. In our approach the ultrasonic radio-frequency (RF) signal is considered as sequence affected by intersymbol interference (ISI) and AWGN noise. In order to reduce the computational cost, the estimation is performed with a reduced-state Viterbi algorithm. The channel effect is estimated in two different ways: either measuring the tranducer response with an experimental setting or with blind homomorphic techniques. We verified an image quality enhancement with respect to different metrics. Extensive tests are made to estimate the quantization alphabet that gives the best performances.
A Reduced Complexity Estimation Algorithm for Ultrasound Images De-Blurring / A. Palladini; N. Testoni; L. De Marchi; N. Speciale. - STAMPA. - (2007), pp. 275-280. (Intervento presentato al convegno 20th IEEE International Symposium on Computer-Based Medical Systems 2007 (CBMS 2007) tenutosi a Maribor, Slovenia nel June 20-22, 2007).
A Reduced Complexity Estimation Algorithm for Ultrasound Images De-Blurring
PALLADINI, ALESSANDRO;TESTONI, NICOLA;DE MARCHI, LUCA;SPECIALE, NICOLO'ATTILIO
2007
Abstract
This paper presents a deconvolution technique for ultrasound images based on a Maximum Likelihood (ML) estimation procedure. In our approach the ultrasonic radio-frequency (RF) signal is considered as sequence affected by intersymbol interference (ISI) and AWGN noise. In order to reduce the computational cost, the estimation is performed with a reduced-state Viterbi algorithm. The channel effect is estimated in two different ways: either measuring the tranducer response with an experimental setting or with blind homomorphic techniques. We verified an image quality enhancement with respect to different metrics. Extensive tests are made to estimate the quantization alphabet that gives the best performances.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
