Classification of contrast ultrasound images using autoregressive model coupled to Gaussian mixture model

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.