2024-03-29T06:45:02Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/3982352019-05-28T06:35:39Zcom_10803_183col_10803_204
nam a 5i 4500
Statistical image processing
Ultrasound B-scan Images
Speckle noise filtering
Generalized central limit theorem
Alpha stable distributions
Log-compressed data distribution
Random walks
Heart ejection fraction measurement
Echocardiographic image segmentation
Electromagnetic models for ultrasound image processing
[Barcelona] :
Universitat Politècnica de Catalunya,
2016
Accés lliure
http://hdl.handle.net/10803/398235
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Navarrete Hurtado, Hugo Ariel,
autor
1 recurs en línia (161 pàgines)
Tesi
Doctorat
Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
2016
Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
Tesis i dissertacions electròniques
Mujal Rosas, Ramón Ma.
(Ramón María)
supervisor acadèmic
TDX
Speckle noise appears when coherent illumination is employed, as for example Laser, Synthetic Aperture Radar (SAR), Sonar, Magnetic Resonance, X-ray and Ultrasound imagery. Backscattered echoes from the randomly distributed scatterers in the microscopic structure of the medium are the origin of speckle phenomenon, which characterizes coherent imaging with a granular appearance. It can be shown that speckle noise is of multiplicative nature, strongly correlated and more importantly, with non-Gaussian statistics. These characteristics differ greatly from the traditional assumption of white additive Gaussian noise, often taken in image segmentation, filtering, and in general, image processing; which leads to reduction of the methods effectiveness for final image information extraction; therefore, this kind of noise severely impairs human and machine ability to image interpretation.
Statistical modeling is of particular relevance when dealing with speckled data in order to obtain efficient image processing algorithms; but, additionally, clinical ultrasound imaging systems employ nonlinear signal processing to reduce the dynamic range of the input echo signal to match the smaller dynamic range of the display device and to emphasize objects with weak backscatter. This reduction in dynamic range is normally achieved through a logarithmic amplifier i.e. logarithmic compression, which selectively compresses large input signals. This kind of nonlinear compression totally changes the statistics of the input envelope signal; and, a closed form expression for the density function of the logarithmic transformed data is usually hard to derive.
This thesis is concerned with the statistical distributions of the Log-compressed amplitude signal in coherent imagery, and its main objective is to develop a general statistical model for log-compressed ultrasound B-scan images. The developed model is adapted, making the pertinent physical analogies, from the multiplicative model in Synthetic Aperture Radar (SAR) context. It is shown that the proposed model can successfully describe log-compressed data generated from different models proposed in the specialized ultrasound image processing literature. Also, the model is successfully applied to model in-vivo echo-cardiographic (ultrasound) B-scan images. Necessary theorems are established to account for a rigorous mathematical proof of the validity and generality of the model. Additionally, a physical interpretation of the parameters is given, and the connections between the generalized central limit theorems, the multiplicative model and the compound representations approaches for the different models proposed up-to-date, are established. It is shown that the log-amplifier parameters are included as model parameters and all the model parameters are estimated using moments and maximum likelihood methods. Finally, three applications are developed: speckle noise identification and filtering; segmentation of in vivo echo-cardiographic (ultrasound) B-scan images and a novel approach for heart ejection fraction evaluation
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