当前位置： SCI文献检索 > ARTIFICIAL INTELLIGENCE IN MEDICINE期刊下所有文献 > A frame reduction system based on a color structural similarity (CSS) method and Bayer images analysis for capsule endoscopy.

A frame reduction system based on a color structural similarity (CSS) method and Bayer images analysis for capsule endoscopy.

Abstract：

:A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an increased probability of overlooking an image that may contain an abnormality. An alternative option to increasing the playback speed is the application of abnormality detection systems to detect abnormalities such as ulcers, tumors, polyps and bleeding. However, applying all of these detection systems requires significant computing time and still produces numerous images with high similarity depending on the specificity of the utilized detection systems. An interesting approach to reduce viewing time is the application of a frame reduction system that reduces the number of images by omitting those with a high similarity of information. The advantage of such a system is that the specialist only needs to review a single image that technically represents a series of images with high similarity. This reduces the total number of images that a specialist must review and importantly, images containing any abnormality are not removed from the review, but simply reduced in number. Thus, the current study developed a frame reduction system using various color models using Bayer images for color texture and a modified local binary pattern (LBP) for structural information. The proposed system achieved a reduction ratio of 93.87%, which is higher than the existing systems and required lesser computation due to the utilization of Bayer images.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Al-Shebani Q,Premaratne P,McAndrew DJ,Vial PJ,Abey S

doi

10.1016/j.artmed.2018.12.008

subject

Has Abstract

pub_date

2019-03-01 00:00:00

pages

18-27

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(18)30408-1

journal_volume

94

pub_type

杂志文章

相关文献

ARTIFICIAL INTELLIGENCE IN MEDICINE文献大全
  • Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks.

    abstract：OBJECTIVES:We propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of nasopharyngeal carcinoma (NPC) on a case-control epidemiologic study that consists of 1289 subjects and 150 risk factors. METHODS:This framework build...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.09.002

    authors： Aussem A,de Morais SR,Corbex M

    更新日期：2012-01-01 00:00:00

  • Development and comparison of four sleep spindle detection methods.

    abstract：OBJECTIVE:The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles. METHODS AND MATERIALS:All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored b...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2007.04.003

    authors： Huupponen E,Gómez-Herrero G,Saastamoinen A,Värri A,Hasan J,Himanen SL

    更新日期：2007-07-01 00:00:00

  • Modeling and solving the dynamic patient admission scheduling problem under uncertainty.

    abstract：OBJECTIVE:Our goal is to propose and solve a new formulation of the recently-formalized patient admission scheduling problem, extending it by including several real-world features, such as the presence of emergency patients, uncertainty in the length of stay, and the possibility of delayed admissions. METHOD:We devise...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2012.09.001

    authors： Ceschia S,Schaerf A

    更新日期：2012-11-01 00:00:00

  • Specification of models in large expert systems based on causal probabilistic networks.

    abstract：:Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between node...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/0933-3657(93)90029-3

    authors： Olesen KG,Andreassen S

    更新日期：1993-06-01 00:00:00

  • Fuzzy ensemble clustering based on random projections for DNA microarray data analysis.

    abstract：OBJECTIVE:Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The ma...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2008.07.014

    authors： Avogadri R,Valentini G

    更新日期：2009-02-01 00:00:00

  • Case-based prediction in experimental medical studies.

    abstract：:Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be ut...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(98)00057-8

    authors： Seitz A,Uhrmacher AM,Damm D

    更新日期：1999-03-01 00:00:00

  • Analyzing interactions on combining multiple clinical guidelines.

    abstract：:Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability a...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2017.03.012

    authors： Zamborlini V,da Silveira M,Pruski C,Ten Teije A,Geleijn E,van der Leeden M,Stuiver M,van Harmelen F

    更新日期：2017-09-01 00:00:00

  • A modular approach for representing and executing clinical guidelines.

    abstract：:In this paper, we propose an approach for managing clinical guidelines. We outline a modular architecture, allowing us to separate two conceptually distinct aspects: the representation (and acquisition) of clinical guidelines and their execution. We propose an expressive formalism, which allows one to deal with the co...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章,评审

    doi：10.1016/s0933-3657(01)00087-2

    authors： Terenziani P,Molino G,Torchio M

    更新日期：2001-11-01 00:00:00

  • Using Arden Syntax for the creation of a multi-patient surveillance dashboard.

    abstract：OBJECTIVE:Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not native...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2015.09.009

    authors： Kraus S,Drescher C,Sedlmayr M,Castellanos I,Prokosch HU,Toddenroth D

    更新日期：2018-11-01 00:00:00

  • Using cognitive task analysis to facilitate the integration of decision support systems into the neonatal intensive care unit.

    abstract：OBJECTIVE:New medical systems may be rejected by staff because they do not integrate with local practice. An expert system, FLORENCE, is being developed to help staff in a neonatal intensive care unit (NICU) make decisions about ventilator settings when treating babies with respiratory distress syndrome. For FLORENCE t...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2005.01.004

    authors： Baxter GD,Monk AF,Tan K,Dear PR,Newell SJ

    更新日期：2005-11-01 00:00:00

  • Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors.

    abstract：OBJECTIVE:We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor classifiers. METHODS AND MATERIAL:Gene expression based cancer classificat...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2008.08.004

    authors： Okun O,Priisalu H

    更新日期：2009-02-01 00:00:00

  • Computer models of hippocampal circuit changes of the kindling model of epilepsy.

    abstract：:Abnormalities in the organization of brain circuits may underlie many types of epilepsy. This hypothesis can best be evaluated in the case of temporal lobe epilepsy, where evidence of rewiring (synaptic reorganization) can be found in the dentate gyrus. Computer modeling of normal and reorganized dentate gyrus was use...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(98)00005-0

    authors： Lytton WW,Hellman KM,Sutula TP

    更新日期：1998-05-01 00:00:00

  • Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

    abstract：OBJECTIVES:The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system. METHOD:Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorit...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2019.06.005

    authors： Alexiadis A

    更新日期：2019-07-01 00:00:00

  • Bridge and brick network motifs: identifying significant building blocks from complex biological systems.

    abstract：OBJECTIVE:A major focus in computational system biology research is defining organizing principles that govern complex biological network formation and evolution. The task is considered a major challenge because network behavior and function prediction requires the identification of functionally and statistically impor...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2007.07.006

    authors： Huang CY,Cheng CY,Sun CT

    更新日期：2007-10-01 00:00:00

  • Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies.

    abstract：OBJECTIVE:Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2017.07.002

    authors： Lamy JB

    更新日期：2017-07-01 00:00:00

  • Symptoms and medications change patterns for Parkinson's disease patients stratification.

    abstract：:Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients' medical conditions and changes in the presc...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2018.04.010

    authors： Valmarska A,Miljkovic D,Konitsiotis S,Gatsios D,Lavrač N,Robnik-Šikonja M

    更新日期：2018-09-01 00:00:00

  • Bayesian network multi-classifiers for protein secondary structure prediction.

    abstract：:Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.01.009

    authors： Robles V,Larrañaga P,Peña JM,Menasalvas E,Pérez MS,Herves V,Wasilewska A

    更新日期：2004-06-01 00:00:00

  • Selection of patients for clinical trials: an interactive web-based system.

    abstract：:The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual pro...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.01.017

    authors： Fink E,Kokku PK,Nikiforou S,Hall LO,Goldgof DB,Krischer JP

    更新日期：2004-07-01 00:00:00

  • Anatomical sketch understanding: recognizing explicit and implicit structure.

    abstract：OBJECTIVE:Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applicat...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2006.07.010

    authors： Haddawy P,Dailey MN,Kaewruen P,Sarakhette N,Hai le H

    更新日期：2007-02-01 00:00:00

  • An appraisal of INTERNIST-I.

    abstract：:INTERNIST-I was an expert system designed in the early 1970's to diagnose multiple diseases in internal medicine by modelling the behaviour of clinicians. Its form and operation are described, and evaluations of the system are surveyed. The major result of the project was its knowledge base which has been used in succ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/0933-3657(94)00028-q

    authors： Wolfram DA

    更新日期：1995-04-01 00:00:00

  • Temporal similarity measures for querying clinical workflows.

    abstract：OBJECTIVE:In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal concept...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2008.07.013

    authors： Combi C,Gozzi M,Oliboni B,Juarez JM,Marin R

    更新日期：2009-05-01 00:00:00

  • A fuzzy cognitive map approach to differential diagnosis of specific language impairment.

    abstract：:This paper presents a computer-based model for differential diagnosis of specific language impairment (SLI), a language disorder that, in many cases, cannot be easily diagnosed. This difficulty necessitates the development of a methodology to assist the speech therapist in the diagnostic process. The methodology tool ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(02)00076-3

    authors： Georgopoulos VC,Malandraki GA,Stylios CD

    更新日期：2003-11-01 00:00:00

  • Treatment of missing data values in a neural network based decision support system for acute abdominal pain.

    abstract：:In this study different substitution methods for the replacement of missing data values were inspected for the use of these cases in a neural network based decision support system for acute appendicitis. The leucocyte count had the greatest number of missing values and was used in the analyses. Four different methods ...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(98)00027-x

    authors： Pesonen E,Eskelinen M,Juhola M

    更新日期：1998-07-01 00:00:00

  • Classifying free-text triage chief complaints into syndromic categories with natural language processing.

    abstract：OBJECTIVE:Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. INTRODUCTION:Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated med...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2004.04.001

    authors： Chapman WW,Christensen LM,Wagner MM,Haug PJ,Ivanov O,Dowling JN,Olszewski RT

    更新日期：2005-01-01 00:00:00

  • Bounded-depth threshold circuits for computer-assisted CT image classification.

    abstract：:We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed,...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(01)00101-4

    authors： Albrecht A,Hein E,Steinhöfel K,Taupitz M,Wong CK

    更新日期：2002-02-01 00:00:00

  • On the use of pairwise distance learning for brain signal classification with limited observations.

    abstract：:The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral pr...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101852

    authors： Calhas D,Romero E,Henriques R

    更新日期：2020-05-01 00:00:00

  • An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation.

    abstract：:Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particl...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2020.101790

    authors： Xiong H,Qiu B,Liu J

    更新日期：2020-04-01 00:00:00

  • Modern parameterization and explanation techniques in diagnostic decision support system: a case study in diagnostics of coronary artery disease.

    abstract：OBJECTIVE:Coronary artery disease has been described as one of the curses of the western world, as it is one of its most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary arter...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.04.009

    authors： Kukar M,Kononenko I,Grošelj C

    更新日期：2011-06-01 00:00:00

  • Case-based reasoning for medical decision support tasks: the Inreca approach.

    abstract：:We describe an approach for developing knowledge-based medical decision support systems based on the new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca + project which mainly deals with medical applications. One goal was to s...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/s0933-3657(97)00038-9

    authors： Althoff KD,Bergmann R,Wess S,Manago M,Auriol E,Larichev OI,Bolotov A,Zhuravlev YI,Gurov SI

    更新日期：1998-01-01 00:00:00

  • Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques.

    abstract：OBJECTIVE:This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does n...

    journal_title：Artificial intelligence in medicine

    pub_type： 杂志文章

    doi：10.1016/j.artmed.2011.05.001

    authors： Monte-Moreno E

    更新日期：2011-10-01 00:00:00