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Title: Improvement of the GMM-AR classification of multiframe contrast ultrasound images using Gaussian filter
Authors: Ghazal, Bilal
Khachab, Maha 
Friboulet, Denis
Mokbel, Chafic 
Cachard, Christian
Affiliations: Faculty of Medicine 
Department of Electrical Engineering 
Issue Date: 2008
Part of: Proceedings of European Conference on Noise Control
Start page: 2275
End page: 2280
Conference: European Conference on Noise Control (EURONOISE) (7th : 29 Jun 2008 : Paris) 
Despite the use of contrast agents that enhance the visualization of vascular zones, the backscattered signals from the contrast agent and tissue are still close which prevents the direct wide ultrasonic use in diagnosis. Thus, it was necessary to implement image-processing techniques that enhance the contrast echo and have the capability of classification. We have applied a new approach based on the autoregressive model where an image of prediction errors is calculated in the first phase. Then, a Gaussian filter is applied in order to model well afterward both agent and tissue behaviors by a Gaussian mixture model. The Agent to Tissue Ratio (ATR)factor and Fisher criterion are adopted to compare the performance of this method with existing techniques as the harmonic and B mode techniques. The experiments conducted have shown the advantages of our proposed approach where an increasing of ATR and Fisher are recorded. In fact, our ATR attains 19.20 dB which represents a good improvement in comparison with B mode (9.50 dB) and Harmonic technique (12.13 dB). Whereas Fisher, the parameter of classification feasibility, it reaches 2.01 which matches an excellent amelioration with respect the mentioning techniques with 0.97 and 1.00 respectively.
The abstract of this paper is published in The Journal of the Acoustical Society of America, Vol. 123, No.5.
Type: Conference Paper
Appears in Collections:Faculty of Medicine

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