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.

Rationale and design of a randomized placebo-controlled trial assessing the effects of etiologic treatment in Chagas` cardiomyopathy: The BENznidazole Evaluation For Interrupting Trypanosomiasis (BENEFIT)

Background Benznidazole is effective for treating acute and chronic (recently acquired) Tryponosoma cruzi infection (Chagas` disease). Recent data indicate that parasite persistence plays a pivotal role in the pathogenesis of chronic Chagas` cardiomyopathy. However, the efficacy of trypanocidal therapy in preventing clinical complications in patients with preexisting cardiac disease is unknown. Study Design BENEFIT is a multicenter, randomized, double-blind, placebo-controlled clinical trial of 3,000 patients with Chagas` cardiomyopathy in Latin America. Patients are randomized to receive benznidazole (5 mg/kg per day) or matched placebo, for 60 days. The primary outcome is the composite of death; resuscitated cardiac arrest; sustained ventricular tachycardia; insertion of pacemaker or cardiac defibrillator; cardiac transplantation; and development of new heart failure, stroke, or systemic or pulmonary thromboembolic events. The average follow-up time will be 5 years, and the trial has a 90% power to detect a 25% relative risk reduction. The BENEFIT program also comprises a substudy evaluating the effects of benznidazole on parasite clearance and an echo substudy exploring the impact of etiologic treatment on left ventricular function. Recruitment started in November 2004, and >1,000 patients have been enrolled in 35 centers from Argentina, Brazil, and Colombia to date. Conclusion This is the largest trial yet conducted in Chagas` disease. BENEFIT will clarify the role of trypanocidal therapy in preventing cardiac disease progression and death.

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.