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 application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Behavioral Types for Embedded Software - A Survey

At present, there is a variety of formalisms for modeling and analyzing the communication behavior of components. Due to a tremendous increase in size and complexity of embedded systems accompanied by shorter time to market cycles and cost reduction, so called behavioral type systems become more and more important. This chapter presents an overview and a taxonomy of behavioral types. The intentions of this taxonomy are to provide a guidance for software engineers and to form the basis for future research.

Specification and Evaluation of Safety Properties in a Component-Based Software Engineering Process

Over the past years, component-based software engineering has become an established paradigm in the area of complex software intensive systems. However, many techniques for analyzing these systems for critical properties currently do not make use of the component orientation. In particular, safety analysis of component-based systems is an open field of research. In this chapter we investigate the problems arising and define a set of requirements that apply when adapting the analysis of safety properties to a component-based software engineering process. Based on these requirements some important component-oriented safety evaluation approaches are examined and compared.

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.

Spontaneous Blinking in Patients With Graves` Upper Eyelid Retraction

Purpose: To describe spontaneous blink kinematics in Graves` upper eyelid retraction (UER). Methods: The magnetic search coil technique was used to record spontaneous blinks of 15 healthy subjects (aged 23-56 years, 15 eyelids) and 15 patients with Graves` UER (aged 22-62 years, 15 eyelids) during a 5-min period of video observation, and the signals were digitized at 200 Hz (12 bits). Overall, a total of 2,798 blinks were recorded for the controls and 1,860 for the patients. The distance between pupil center and upper eyelid margin in the primary position of gaze (MRD) was measured with the Image J software. Results: The blinking rate of patients was lower than that of control subjects, with a mean (+/-SEM) blinking rate (blinks/min) of 13.0 +/- 1.7 for patients and of 20.0 +/- 2.1 for the controls (t = 2.58, P = 0.016). There were no statistically significant differences in blink amplitude between controls (22.7 +/- 3.1 degrees) and Graves` patients (24.7 +/- 3.3 degrees). However, while only 22% of the blinks performed by controls were smaller than MRD, this rate was 78% for patients. In addition, in blinks larger than 25, patients showed lower down-phase velocity than controls. Conclusions: Patients with Graves` UER show reduced blinks rates and abnormal blink kinematics, which might be related to the development of exposure keratitis in this disease.

Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

Feasibility of an evidence-based literature search service for general practitioners

Objective: To test the feasibility of an evidence-based clinical literature search service to help answer general practitioners' (GPs') clinical questions. Design: Two search services supplied GPs who submitted questions with the best available empirical evidence to answer these questions. The GPs provided feedback on the value of the service, and concordance of answers from the two search services was assessed. Setting: Two literature search services (Queensland and Victoria), operating for nine months from February 1999. Main outcome measures: Use of the service; time taken to locate answers; availability of evidence; value of the service to GPs; and consistency of answers from the two services. Results: 58 GPs asked 160 questions (29 asked one, 11 asked five or more). The questions concerned treatment (65%), aetiology (17%), prognosis (13%), and diagnosis (5%). Answering a question took a mean of 3 hours 32 minutes of personnel time (95% Cl, 2.67-3.97); nine questions took longer than 10 hours each to answer, the longest taking 23 hours 30 minutes. Evidence of suitable quality to provide a sound answer was available for 126 (79%) questions. Feedback data for 84 (53%) questions, provided by 42 GPs, showed that they appreciated the service, and asking the questions changed clinical care. There were many minor differences between the answers from the two centres, and substantial differences in the evidence found for 4/14 questions. However, conclusions reached were largely similar, with no or only minor differences for all questions. Conclusions: It is feasible to provide a literature search service, but further assessment is needed to establish its cost effectiveness.