1,721,020 research outputs found
Matching of medical images by self-organizing neural networks
No full textA general approach to the problem of image matching which exploits a multi-scale representation of local image structure and the principles of self-organizing neural networks is introduced. The problem considered is relevant in many imaging applications and has been largely investigated in medical imagery, especially as regards the integration of different imaging procedures. A given pair of images to be matched, named target and stimulus respectively, are represented by Gabor Wavelets. Correspondence is computed by exploiting the learning procedure of a neural network derived from Kohonen's SOM. The SOM units coincide with the pixels of the target image and their weight are pointers to those of the stimulus images. The standard SOM rule is modified so as to account for image features. The properties of our method are tested by experiments performed on synthetic images. The considered implementation has shown that is able to recover a wide range of transformations including global affine transformations and local distortions. Tests in the presence of additive noise indicate considerable robustness against statistical variability. Applications to clinical images are presented
Knowledge-based system for the diagnosis and treatment of hypertension
No full textA knowledge-based system to assist the physician in the diagnosis and treatment of hypertension has been developed as the result of cooperation between the Department of Electronic Engineering of the University of Florence and the Interuniversity Centre of Clinical Chronobiology. The system input consists of the data recorded over a 24 h (or longer) period by monitoring (automatically or through self-measurements) the blood pressure of the subject undergoing the system analysis, and the associatd anamnestic data. The process results in a report that states from which kind of hypertensive syndrome, if any, the subject is suffering and which anti-hypertensive therapy appears to be most suitable. The system consists of three modules: the first diagnoses hypertension by applying cluster analysis to a set of parameters derived from the principal components of the time series resulting from the subject's blood pressure monitoring; the other two classify hypertension and offer advice about the most advisable treatment, respectively, by using high-level data representation and processing. The knowledge embedded in the system is internally represented by means of frames and rules. This paper describes the structure of the system, illustrates the techniques that have been used for its development and discusses the results of its application.
A knowledge-based system to assist the physician in the diagnosis and treatment of hypertension has been developed as the result of cooperation between the Department of Electronic Engineering of the University of Florence and the Interuniversity Centre of Clinical Chronobiology. The system input consists of the data recorded over a 24 h (or longer) period by monitoring (automatically or through self-measurements) the blood pressure of the subject undergoing the system analysis, and the associated anamnestic data. The process results in a report that states from which kind of hypertensive syndrome, if any, the subject is suffering and which anti-hypertensive therapy appears to be most suitable. The system consists of three modules: the first diagnoses hypertension by applying cluster analysis to a set of parameters derived from the principal components of the time series resulting from the subject's blood pressure monitoring; the other two classify hypertension and offer advice about the most advisable treatment, respectively, by using high-level data representation and processing. The knowledge embedded in the system is internally represented by means of frames and rules. This paper describes the structure of the system, illustrates the techniques that have been used for its development and discusses the results of its application
Neural network segmentation of magnetic resonance spin echo images of the brain
No full textThis paper describes a neural network system to segment magnetic resonance (MR) spin echo images of the brain. Our approach relies on the analysis of MR signal decay and on anatomical knowledge; the system processes two early echoes of a standard multislice sequence. Three main subsystems can be distinguished. The first implements a model of MR signal decay; it synthesizes a four-echo multiecho sequence, in order to add images characterized by long echo-times to the input sequence. The second subsystem exploits a priori anatomical knowledge by producing an image, in which pixels belonging to brain parenchyma are highlighted. Such anatomical information allows the following submodule to distinguish biologically different tissues with similar water content, and hence similar appearance, which might produce misclassifications. The grey levels of the reconstructed sequence and the output of the second module are processed by the third subsystem, which performs the segmentation of the sequence. Each pixel is assigned to one of five different tissue classes that can be revealed with brain MR spin echo imaging. With a suitable encoding, a five-level segmented image can then be produced. The system is based on feed-forward networks trained with the back-propagation algorithm; experiments to assess its performance have been carrried out on both simulated and clinical images.
This paper describes a neural network system to segment magnetic resonance (MR) spin echo images of the brain. Our approach relies on the analysis of MR signal decay and on anatomical knowledge; the system process two early echoes of a standard multislice sequence. Three main subsystems can be distinguished. The first implements a model of MR signal decay; it synthesizes a four-echo multiecho sequence, in order to add images characterized by long echo-times to the input sequence. The second subsystem exploits a priori anatomical knowledge by producing an image, in which pixels belonging to brain parenchyma are highlighted. Such anatomical information allows the following submodule to distinguish biologically different tissues with similar water content, and hence similar appearance, which might produce misclassifications. The grey levels of the reconstructed sequence and the output of the second module are processed by the third subsystem, which performs the segmentation of the sequence. Each pixel is assigned to one of five different tissue classes that can be revealed with brain MR spin echo imaging. With a suitable encoding, a five-level segmented image can then be produced. The system is based on feed-forward networks trained with the back-propagation algorithm; experiments to assess its performance have been carried out on both simulated and clinical images
A neural network expert system for diagnosing and treating hypertension
No full textHypernet (Hypertension Neural Expert Therapist), a neural network expert system for diagnosing and treating hypertension, is described. After a brief look at artificial neural networks, the authors describe the structure of the three modules that make up Hypernet, starting with the specific problem each network is intended to solve and explaining how the network is expected to operate. The tools developed for implementing the system are a compiler for a simple descriptive language that enables the authors to define, train, and test networks; a graphic editor that translates the network drawn by the user into the proper statements; and a set of programs for interactively generating the examples. The data used as examples, the learning phase, and the results of tests for evaluating the performance of each network are described, and conclusions about overall system results are presented
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Neural Networks and prior knowledge help the segmentation of medical images
Full text linkThis paper describes some achievements in the segmentation of medical images using artificial neural networks. We have identified three main sources of a priori information available to help perform the task of medical image segmentation: anatomical knowledge about the imaged region, the physical principles of image generation and the "regulari ties" of biological structures. The exploitation of each of these forms of knowledge can be effectively achieved with suitable neural architectures, three of which are described in the paper. An important lesson learnt from using these architectures is that different kinds of knowledge unavoidably induce different limitations in the resulting segmentation systems, either in terms of generality or of performance. Our experience indicates that in several applications some of such limitations can be overcome through a careful exploitation and integration of available knowledge sources via proper neural modules
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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