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Title: Classification of contrast ultrasound images using autoregressive model coupled to gaussian mixture model
Authors: Ghazal, Bilal
Khachab, Maha 
Cachard, Christian
Friboulet, Denis
Mokbel, Chafic 
Affiliations: Faculty of Medicine 
Department of Electrical Engineering 
Keywords: Statistical analysis
Autoregressive processes
Biomedical ultrasonics
Image classification
Medical image processing
Issue Date: 2007
Publisher: IEEE
Part of: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. EMBS 2007.
Start page: 331
End page: 334
Conference: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (29th : 22-26 Aug. 2007 : Lyon, France) 
Contrast ultrasound images are not clear enough to be directly adopted in the diagnostic. In fact, the ultrasound agents enhance the vascular zones but unfortunately the signals backscattered from agent and tissues are still close. Therefore, it is necessary to implement image-processing techniques to enhance the contrast echo and thus have the capability of classification. In this article, we apply a new approach based on the autoregressive model coupled to the Gaussian mixture model to represent both agent and tissue behaviors. Then, we process the resultant image by a classification method based on a fixed window's size in order to obtain a satisfying differentiation of the ultrasound image into two classes. Finally, we adopt the agent to tissue ratio (ATR) factor and the Fisher criterion to compare the performance of this method with existing techniques as harmonic and B mode.
Open URL: Link to full text
Type: Conference Paper
Appears in Collections:Faculty of Medicine

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