Cooperation of fuzzy segmentation operators for correction aliasing phenomenon in 3D color Doppler imaging

The study described in this paper concerns natural object modeling in the context of uncertain, imprecise and inconsistent representation. We propose a fuzzy system which offers a global modeling of object properties such as color, shape, velocity, etc. This modeling makes a transition from a low level reasoning (pixel level), which implies a local precise but uncertain representation, to a high level reasoning (region level), inducing a certain assignment. So, we use fuzzy structured partitions characterizing these properties. At this level. each property will have its own global modeling. Then, these different models are merged for decision making. Our approach was tested with several applications. In particular, we show here its performance in the area of blood flow analysis from 3D color Doppler images in order to quantify and study the development of this flow. We present methods that detect and correct aliasing phenomenon, i.e. inconsistent information. At first, the flow space is partitioned into fuzzy sectors where each sector is defined by a center, an angle and a direction. In parallel, the velocity information carried by the pixels is classified into fuzzy classes. Then, by combining these two partitions, we obtain the velocity distribution into sectors. Moreover, for each found path (from the first sector to the last one), we locate and correct inconsistent velocities by applying global rules. After extracting some meaningful sector features, the fuzzy modeling, applied to the aliasing correction, makes it possible to simplify and synthesize the blood flow direction.