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.

Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.

Validation of the National Institutes of Health Stroke Scale, Modified Rankin Scale and Barthel Index in Brazil: The Role of Cultural Adaptation and Structured Interviewing

Background: We aimed to validate three widely used scales in stroke research in a multiethnic Brazilian population. Methods: The National Institutes of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS) and Barthel Index (BI) were translated, culturally adapted and applied by two independent investigators. The mRS was applied with or without a previously validated structured interview. Interobserver agreement (kappa statistics) and intraclass correlation coefficients were calculated. Results: 84 patients underwent mRS (56 with and 28 without a structured interview), 57 BI and 62 NIHSS scoring. Intraclass correlation coefficient was 0.902 for NIHSS and 0.967 for BI. For BI, interobserver agreement was good (kappa = 0.70). For mRS, the structured interview improved interobserver agreement (kappa = 0.34 without a structured interview; 0.75 with a structured interview). Conclusion: The NIHSS, BI and mRS show good validity when translated and culturally adapted. Using a structured interview for the mRS improves interobserver concordance rates. Copyright (C) 2008 S. Karger AG, Basel

Shape alterations in the striatum in chorea-acanthocytosis

Chorea-acanthocytosis (ChAc) is an uncommon autosomal recessive disorder due to mutations of the VPS13A gene, which encodes for the membrane protein chorein. ChAc presents with progressive limb and orobuccal chorea, but there is often a marked dysexecutive syndrome. ChAc may first present with neuropsychiatric disturbance such as obsessive-compulsive disorder (OCD), suggesting a particular role for disruption to striatal structures involved in non-motor frontostriatal loops, such as the head of the caudate nucleus. Two previous studies have suggested a marked reduction in volume in the caudate nucleus and putamen, but did not examine morphometric change. We investigated morphometric change in 13 patients with genetically or biochemically confirmed ChAc and 26 age- and gender-matched controls. Subjects underwent magnetic resonance imaging and manual segmentation of the caudate nucleus and putamen, and shape analysis using a non-parametric spherical harmonic technique. Both structures showed significant and marked reductions in volume compared with controls, with reduction greatest in the caudate nucleus. Both structures showed significant shape differences, particularly in the head of the caudate nucleus. No significant correlation was shown between duration of illness and striatal volume or shape, suggesting that much structural change may have already taken place at the time of symptom onset. Our results suggest that striatal neuron loss may occur early in the disease process, and follows a dorsal-ventral gradient that may correlate with early neuropsychiatric and cognitive presentations of the disease. (C) 2010 Elsevier Ireland Ltd. All rights reserved.