An approach that aims to reveal and to explain the pattern of information potentially present in a dataset consisting of n objects by ordering them using the possibility-based Robinsonian similarity matrix is proposed. The similarity is estimated between objects containing imperfect and heterogeneously-assigned data. A graph-based model is proposed to visualize these patterns. This method is applied to a medical database. Without any a priori medical knowledge and without knowing the key attributes of the pathologies, the objects have been ranked according to their corresponding classes.
Clinical assessment of venous thrombosis (VT) is essential to evaluate the risk of size increase or embolism. Analyses like echogenecity and echostructure characterization, examine ancillary evidence to improve diagnosis. However, such analyses are inherently uncertain and operator dependent, adding enormous complexity to the task of indexing diagnosed images for medical practice support, by retrieving similar images, or to exploit electronic patient record repositories for data mining. This paper proposes a VT ultrasound image indexing and retrieval approach, which shows the suitability of neural network VT characterization, combined with a fuzzy similarity. Three types of image descriptors (sliding window, wavelet coefficients energy and co-occurrence matrix), are processed by three different neural networks, producing equivalent VT characterizations. Resulting values are projected on fuzzy membership functions and then compared with the fuzzy similarity. Compared to nominal and Euclidean distances, an experimental validation indicates that the fuzzy similarity increases image retrieval precision beyond the identification of images that belong to the same diagnostic class, taking into account the characterization result uncertainty, and allowing the user to privilege any particular feature.
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