Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

The use of strategic visioning to enhance local tourism planning in periphery communities

Tourism has had a profound impact upon destinations worldwide, and although this impact has been positive for many destinations, there are numerous examples where tourism has adversely impacted upon the environment and social fabric of the destination community (Coccossis 1996; Murphy 1985). The negative impacts of tourism have been attributed, among other things, to inadequate or non-existent planning for development (Gunn 1994; Hall2000). This has led to increased calls for tourism planning to offset some of the negative impacts that tourism can have on the destination community. While a number of approaches have been advocated, a collaborative philosophy, based on the principles of sustainability, is more likely to result in acceptable and successful policies and programmes for tourism destinations (Farrell1986; Jamal & Getz 1995; Maitland 2002; Minca & Getz 1995). Such an approach focuses on cooperation and broader based participation in tourism planning and decision-making between stakeholders to lead to agreement on planning directions and goals, with one of the primary objectives of collaborative arrangements being to develop a strategic vision for a destination (Bramwell & Lane 2000). [Extract from introduction]