The decision making systems make use of heterogeneous information to identify an object class or a target, which are affected by various kinds of imperfection. First, information issued from measures (radar measures, images) of an observation is represented by X variables. Generally, on these X variables, each class can be described through a probability distribution function. These decision systems also integrate expert a <i>prior</i> knowledge to assist the decision. Such information is defined by Y variables and is represented by fuzzy membership function. The question is how to combine appropriately these two kinds of data in order to improve the decision process.
In this paper, we present a decision model combining probabilistic and fuzzy data. The decision is defined using a fuzzy Bayesian approach, which takes into account these two imperfections. Only two classes are considered using one X variable and one Y variable. Then an extension is proposed to more complicated cases.
To validate the interest of this approach, we compare it with the Bayesian classification and fuzzy classification applied separately to synthetic data. In addition, we will see how our approach can be applied to the problem of radar system ranking, on which system resources are limited and as a consequence, decisions about priorities must be taken. Using the system information sources (i.e. probabilistic: radar measurements, fuzzy: <i>prior</i> expert knowledge, evidential), a comparison between Bayesian classification, fuzzy classification, system decision and the proposed approach is presented.
In medical imaging, and more generally in medical information, researches go towards fusion systems. Nowadays, the steps of information source definition, the pertinent data extraction and the fusion need to be conducted as a whole. In this work, our interest is related to the esophagus wall segmentation from ultrasound images sequences. We aim to elaborate a general methodology of data mining that coherently links works on data selection and fusion architectures, in order to extract useful information from raw data and to integrate efficiently the physician a prior. In the presented method, based on fuzzy logic, some fuzzy propositions are defined using physicians a prior knowledge. The use of probabilistic distributions, estimated thanks to a learning base of pathologic and non-pathologic cases, enables the veracity of these propositions to be qualified. This promising idea enables information to be managed through the consideration of both information imprecision and uncertainty. In the same time, the obtained benefit, when a prior knowledge source is injected in a fusion based decision system, can be quantified. By considering that, the fuzzyfication stage is optimized relatively to a given criteria using a genetic algorithm. By this manner, fuzzy sets corresponding to the physicians ambiguous a prior are defined objectively. At this level, we successively compare performances obtained when fuzzy functions are defined empirically and when they are optimized. We conclude this paper with the first results on esophagus wall segmentation and outline some further works.
Medical domain makes intensive use of information fusion. In particular, the gastro-enterology is a discipline where physicians have the choice between several imagery modalities that offer complementary advantages. Among all existing systems, videoendoscopy (based on a CCD sensor) and echoendoscopy (based on an ultrasound sensor) are the most efficient. The use of each system corresponds to a given step in the physician diagnostic elaboration. Nowadays, several works aim to achieve automatic interpretation of videoendoscopic sequences. These systems can quantify color and superficial textures of the digestive tube. Unfortunately the relief information, which is important for the diagnostic, is very difficult to retrieve. On the other hand, some studies have proved that 3D information can be easily quantified using echoendoscopy image sequences. That is why the idea to combine these information, acquired from two very different points of view, can be considered as a real challenge for the medical image fusion topic. In this paper, after a review of actual works concerning numerical exploitation of videoendoscopy and echoendoscopy, the following question will be discussed: how can the use of complementary aspects of the different systems ease the automatic exploitation of videoendoscopy ? In a second time, we will evaluate the feasibility of the achievement of a realistic 3D reconstruction based both on information given by echoendoscopy (relief) and videoendoscopy (texture). Enumeration of potential applications of such a fusion system will then follow. Further discussions and perspectives will conclude this first study.