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Adaptive registration of varying contrast-weighted images for improved tissue characterization (ARCTIC): application to T1 mapping.

https://arctichealth.org/en/permalink/ahliterature268568
Source
Magn Reson Med. 2015 Apr;73(4):1469-82
Publication Type
Article
Date
Apr-2015
Author
Sébastien Roujol
Murilo Foppa
Sebastian Weingärtner
Warren J Manning
Reza Nezafat
Source
Magn Reson Med. 2015 Apr;73(4):1469-82
Date
Apr-2015
Language
English
Publication Type
Article
Keywords
Algorithms
Artifacts
Female
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging, Cine - methods
Male
Middle Aged
Motion
Myocardium - pathology
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and specificity
Subtraction Technique
Abstract
To propose and evaluate a novel nonrigid image registration approach for improved myocardial T1 mapping.
Myocardial motion is estimated as global affine motion refined by a novel local nonrigid motion estimation algorithm. A variational framework is proposed, which simultaneously estimates motion field and intensity variations, and uses an additional regularization term to constrain the deformation field using automatic feature tracking. The method was evaluated in 29 patients by measuring the DICE similarity coefficient and the myocardial boundary error in short axis and four chamber data. Each image series was visually assessed as "no motion" or "with motion." Overall T1 map quality and motion artifacts were assessed in the 85 T1 maps acquired in short axis view using a 4-point scale (1-nondiagnostic/severe motion artifact, 4-excellent/no motion artifact).
Increased DICE similarity coefficient (0.78 ± 0.14 to 0.87 ± 0.03, P
Notes
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PubMed ID
24798588 View in PubMed
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Automated extraction of temporal motor activity signals from video recordings of neonatal seizures based on adaptive block matching.

https://arctichealth.org/en/permalink/ahliterature58176
Source
IEEE Trans Biomed Eng. 2005 Apr;52(4):676-86
Publication Type
Article
Date
Apr-2005
Author
Nicolaos B Karayiannis
Abdul Sami
James D Frost
Merrill S Wise
Eli M Mizrahi
Author Affiliation
Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005, USA. karayiannis@uh.edu
Source
IEEE Trans Biomed Eng. 2005 Apr;52(4):676-86
Date
Apr-2005
Language
English
Publication Type
Article
Keywords
Algorithms
Artificial Intelligence
Cluster analysis
Comparative Study
Feedback
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Infant, Newborn
Infant, Newborn, Diseases - diagnosis - physiopathology
Information Storage and Retrieval - methods
Intensive Care, Neonatal - methods
Models, Biological
Monitoring, Physiologic - methods
Motor Activity
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated - methods
Reproducibility of Results
Research Support, U.S. Gov't, P.H.S.
Seizures - diagnosis - physiopathology
Sensitivity and specificity
Signal Processing, Computer-Assisted
Subtraction Technique
Video Recording - methods
Abstract
This paper presents an automated procedure developed to extract quantitative information from video recordings of neonatal seizures in the form of motor activity signals. This procedure relies on optical flow computation to select anatomical sites located on the infants' body parts. Motor activity signals are extracted by tracking selected anatomical sites during the seizure using adaptive block matching. A block of pixels is tracked throughout a sequence of frames by searching for the most similar block of pixels in subsequent frames; this search is facilitated by employing various update strategies to account for the changing appearance of the block. The proposed procedure is used to extract temporal motor activity signals from video recordings of neonatal seizures and other events not associated with seizures.
PubMed ID
15825869 View in PubMed
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Automatic segmentation and recognition of lungs and lesion from CT scans of thorax.

https://arctichealth.org/en/permalink/ahliterature90938
Source
Comput Med Imaging Graph. 2009 Jan;33(1):72-82
Publication Type
Article
Date
Jan-2009
Author
Kakar Manish
Olsen Dag Rune
Author Affiliation
Department of Radiation Biology, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical centre, Oslo, Norway. Manish.Kakar@rr-research.no
Source
Comput Med Imaging Graph. 2009 Jan;33(1):72-82
Date
Jan-2009
Language
English
Publication Type
Article
Keywords
Anatomy, Cross-Sectional - methods
Cluster analysis
Fuzzy Logic
Humans
Image Processing, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Lung - pathology - radiography
Lung Neoplasms - pathology - radiography
Models, Statistical
Neural Networks (Computer)
Norway
Pattern Recognition, Automated - methods
Radiography, Thoracic - methods
Sensitivity and specificity
Thorax - pathology
Tomography, X-Ray Computed - methods
Abstract
In this study, a fully automated texture-based segmentation and recognition system for lesion and lungs from CT of thorax is presented. For the segmentation part, we have extracted texture features by Gabor filtering the images, and, then combined these features to segment the target volume by using Fuzzy C Means (FCM) clustering. Since clustering is sensitive to initialization of cluster prototypes, optimal initialization of the cluster prototypes was done by using a Genetic Algorithm. For the recognition stage, we have used cortex like mechanism for extracting statistical features in addition to shape-based features. The segmented regions showed a high degree of imbalance between positive and negative samples, so we employed over and under sampling for balancing the data. Finally, the balanced and normalized data was subjected to Support Vector Machine (SimpleSVM) for training and testing. Results reveal an accuracy of delineation to be 94.06%, 94.32% and 89.04% for left lung, right lung and lesion, respectively. Average sensitivity of the SVM classifier was seen to be 89.48%.
PubMed ID
19059759 View in PubMed
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Constrained intensity-based image registration: application to aligning human back images.

https://arctichealth.org/en/permalink/ahliterature155051
Source
Stud Health Technol Inform. 2008;140:96-102
Publication Type
Article
Date
2008
Author
A S Elsafi
N G Durdle
J V Raso
Author Affiliation
Dept. of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
Source
Stud Health Technol Inform. 2008;140:96-102
Date
2008
Language
English
Publication Type
Article
Keywords
Algorithms
Biomechanical Phenomena
Canada
Computer simulation
Humans
Image Enhancement
Image Interpretation, Computer-Assisted - instrumentation - methods
Pattern Recognition, Automated - methods
Positron-Emission Tomography
Reproducibility of Results
Sensitivity and specificity
Spine - anatomy & histology - physiology
Subtraction Technique
Abstract
In this work, an accurate method to register multi-view images of the human torso is developed. In particular, a new framework that incorporates prior statistical knowledge about the registration is developed and tested. This framework leads to a computationally efficient procedure to accurately align images of the human torso. An intensity based image registration procedure is used to obtain the deformation fields by modelling them as both locally affine and globally smooth. Next, the estimated geometric deformation fields are analyzed in order to construct a prior deformation model. Two subspace analysis projection techniques are used to construct the subspaces of plausible deformations, namely principal component analysis (PCA) and independent component analysis (ICA). Accurate deformations are now guaranteed by projecting the locally computed geometric transformations onto the subspaces of plausible deformations. The proposed registration method was validated using high resolution images of the human torso. In order to handle the high resolution images, a multi-resolution framework was employed in the registration process. Experiments demonstrate promising performance in terms of mean square error and in the computational complexity. The main contribution of this work is the development of image registration method that uses subspace constraints to align images of the human torso. This method did not use the intra and inter image constraints used in most intensity based image registration algorithms in the literature.
PubMed ID
18810007 View in PubMed
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Continuous template collection and updating for electrogram morphology discrimination in implantable cardioverter defibrillators.

https://arctichealth.org/en/permalink/ahliterature82506
Source
Pacing Clin Electrophysiol. 2006 Mar;29(3):244-54
Publication Type
Article
Date
Mar-2006
Author
Compton Steven J
Merrill James J
Dorian Paul
Cao Jian
Zhou Duo
Gillberg Jeffrey M
Author Affiliation
Alaska Heart Institute, Anchorage, Alaska 99508-4615, USA. scompton@alaskaheart.com
Source
Pacing Clin Electrophysiol. 2006 Mar;29(3):244-54
Date
Mar-2006
Language
English
Publication Type
Article
Keywords
Algorithms
Artificial Intelligence
Cohort Studies
Defibrillators, Implantable
Diagnosis, Computer-Assisted - methods
Discriminant Analysis
Electric Countershock - methods
Electrocardiography - methods
Female
Humans
Information Storage and Retrieval - methods
Male
Middle Aged
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and specificity
Tachycardia, Supraventricular - diagnosis - therapy
Tachycardia, Ventricular - diagnosis - therapy
Therapy, Computer-Assisted - methods
Abstract
INTRODUCTION: Electrogram morphology analysis improves discrimination of supraventricular tachycardias (SVTs) from ventricular tachycardias (VTs) in implantable cardioverter defibrillators (ICDs), but electrogram morphology may change with lead maturation, drugs, or disease progression. We report the clinical performance of an automatic algorithm that creates and updates templates from non-paced, slow rhythm and continuously checks the quality of the template used for arrhythmia discrimination. METHODS AND RESULTS: We studied this algorithm in 193 patients with single-chamber ICDs (Marquis VR, Medtronic Inc., Minneapolis, MN, USA). Of the 112 patients who completed 6-month follow-up, 99.1% of the patients had > or =1 automatic template created. Match scores between template and ongoing rhythm are computed using Haar Wavelets. Of the 435 automatic templates evaluated at follow-up, 423 (97.2%) had a median match score > or =70%. Intrinsic rhythm at 1 month had significantly higher match scores (P
PubMed ID
16606391 View in PubMed
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Different classification techniques considering brain computer interface applications.

https://arctichealth.org/en/permalink/ahliterature82187
Source
J Neural Eng. 2006 Jun;3(2):139-44
Publication Type
Article
Date
Jun-2006
Author
Rezaei Siamak
Tavakolian Kouhyar
Nasrabadi Ali Moti
Setarehdan S Kamaledin
Author Affiliation
Computer Science, University of Northern British Columbia, Prince George, BC, Canada.
Source
J Neural Eng. 2006 Jun;3(2):139-44
Date
Jun-2006
Language
English
Publication Type
Article
Keywords
Algorithms
Artificial Intelligence
Brain - physiology
Communication Aids for Disabled
Diagnosis, Computer-Assisted - methods
Electroencephalography - methods
Evoked Potentials - physiology
Humans
Pattern Recognition, Automated - methods
User-Computer Interface
Abstract
In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.
PubMed ID
16705270 View in PubMed
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Effect of computer-aided detection on independent double reading of paired screen-film and full-field digital screening mammograms.

https://arctichealth.org/en/permalink/ahliterature79046
Source
AJR Am J Roentgenol. 2007 Feb;188(2):377-84
Publication Type
Article
Date
Feb-2007
Author
Skaane Per
Kshirsagar Ashwini
Stapleton Sandra
Young Kari
Castellino Ronald A
Author Affiliation
Department of Radiology, Breast Imaging Center, Ullevaal University Hospital, Kirkeveien 166, N-0407 Oslo, Norway. per.skaane@ulleval.no
Source
AJR Am J Roentgenol. 2007 Feb;188(2):377-84
Date
Feb-2007
Language
English
Publication Type
Article
Keywords
Artificial Intelligence
Breast Neoplasms - epidemiology - radiography
Female
Humans
Mammography - methods - statistics & numerical data
Mass Screening - methods - statistics & numerical data
Middle Aged
Norway - epidemiology
Observer Variation
Pattern Recognition, Automated - methods
Radiographic Image Enhancement
Radiographic Image Interpretation, Computer-Assisted - methods
Reproducibility of Results
Sensitivity and specificity
X-Ray Film
X-Ray Intensifying Screens
Abstract
OBJECTIVE: The purpose of this study was to evaluate the performance and potential contribution of computer-aided detection (CAD) to independent double reading of paired screen-film and full-field digital screening mammograms. MATERIALS AND METHODS: The cases of 3,683 women who underwent both screen-film mammography and full-field digital mammography (FFDM) with independent double reading for each technique were followed for 2 years to include cancers detected in the interval between screening rounds and cancers detected at the next screening round. Fifty-five biopsy-proven cancers were diagnosed. The baseline screening mammograms of the 55 cancers were defined as having positive findings if at least one of two independent readers scored it 2 or higher on a 5-point rating scale. The baseline mammograms of interval (n = 10) or secondround (n = 16) cancers were retrospectively classified as overlooked (n = 2), minimal sign actionable (n = 8), minimal sign nonactionable (n = 5), and normal (n = 11). The baseline mammograms of these cases of cancer were evaluated with a CAD system, and the CAD results were compared (McNemar's test for paired proportions) with the findings at prospective independent double reading of mammograms obtained with each technique. RESULTS: For FFDM, CAD sensitivity was 95% (37/39) compared with 64% (25/39) for double reading (p = 0.006), and for screen-film mammography, CAD sensitivity was 85% (33/39) compared with 77% (30/39) for prospective double reading (p = 0.57) of radiographically visible lesions in baseline mammograms. CAD correctly marked five (13%) of 39 cancers on screen-film mammography and 14 (36%) of 39 cancers on FFDM not detected at prospective independent double reading. CONCLUSION: CAD showed the potential to increase the cancer detection rate for FFDM and for screen-film mammography in breast cancer screening performed with independent double reading.
PubMed ID
17242245 View in PubMed
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Hidden Markov model using Dirichlet process for de-identification.

https://arctichealth.org/en/permalink/ahliterature276372
Source
J Biomed Inform. 2015 Dec;58 Suppl:S60-6
Publication Type
Article
Date
Dec-2015
Author
Tao Chen
Richard M Cullen
Marshall Godwin
Source
J Biomed Inform. 2015 Dec;58 Suppl:S60-6
Date
Dec-2015
Language
English
Publication Type
Article
Keywords
Cohort Studies
Computer Security
Computer simulation
Confidentiality
Data Mining - methods
Electronic Health Records - organization & administration
Machine Learning
Markov Chains
Models, Statistical
Narration
Natural Language Processing
Newfoundland and Labrador
Pattern Recognition, Automated - methods
Vocabulary, Controlled
Abstract
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance.
PubMed ID
26407642 View in PubMed
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Identifying relative cut-off scores with neural networks for interpretation of the Minnesota Living with Heart Failure questionnaire.

https://arctichealth.org/en/permalink/ahliterature146909
Source
Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6242-6
Publication Type
Article
Date
2009
Author
Hassan Behlouli
Deborah E Feldman
Anique Ducharme
Marc Frenette
Nadia Giannetti
François Grondin
Caroline Michel
Richard Sheppard
Louise Pilote
Author Affiliation
Divisions of Internal Medicine and Clinical Epidemiology, McGill University Health Centre.
Source
Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6242-6
Date
2009
Language
English
Publication Type
Article
Keywords
Algorithms
Diagnosis, Computer-Assisted - methods
Heart Failure - diagnosis - psychology
Humans
Neural Networks (Computer)
Pattern Recognition, Automated - methods
Quality of Life
Quebec
Questionnaires
Reproducibility of Results
Sensitivity and specificity
Abstract
Quality of life (QoL) is an important end point in heart failure (HF) studies. The Minnesota Living with Heart Failure questionnaire (MLHFQ) is the instrument most widely used to evaluate QoL in Heart Failure (HF) patients. It is a questionnaire containing 21 questions with scores ranging from 0 to 105. A best cut-off value for MLHFQ scores to identify those patients with good, moderate or poor QoL has not been determined.
To determine a cut-off score for the MLHFQ based on the neural network (NN) approach. These cut-off scores will help discriminate between HF patients having good, moderate or poor QoL.
This research was carried out in the context of a longitudinal cohort study of new patients attending specialized HF clinics in six participating centers in Quebec, Canada. Patients completed a questionnaire that included the MLHFQ. In addition to this scale, self-perceived health status and clinical information related to the severity of HF were obtained including: the New York Heart Association (NYHA) functional class, 6 minute walk test and survival status. We analyzed the database using NN and conventional statistical tools. The NN is a statistical program that recognizes clusters of MLHFQ and relates similar QoL measures to one another. Among the 531 eligible patients, 447 patients with complete questionnaires were used to build randomly two sets for training (learning set) and for testing (validation set) the NN.
Participants had a mean age of 65 years and 24% were women. The median MLHFQ score was 45 (inter-quartile range: 27 to 64). NN identified 3 distinct clusters of MLHFQ that represent the full spectrum of possible scores on the MLHFQ. We estimated that a score of 45 represents a poor QoL. Validation with the different severity measures confirmed these categories. These cut-offs allowed us to reach a good total accuracy (91%). These cutoffs were strongly correlated with survival status (p = 0.004), self-perceived health status (p = 0.0032), NYHA functional class (p
PubMed ID
19965089 View in PubMed
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Machine learning and pattern classification in identification of indigenous retinal pathology.

https://arctichealth.org/en/permalink/ahliterature127871
Source
Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5951-4
Publication Type
Article
Date
2011
Author
Herbert F Jelinek
Anderson Rocha
Tiago Carvalho
Siome Goldenstein
Jacques Wainer
Author Affiliation
Centre for Research in Complex Systems, School of Community Health, Albury, NSW, Australia. hjelinek@csu.edu.au
Source
Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5951-4
Date
2011
Language
English
Publication Type
Article
Keywords
Algorithms
Artificial Intelligence
Diabetic Retinopathy - pathology
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Information Storage and Retrieval - methods
Pattern Recognition, Automated - methods
Reproducibility of Results
Retinoscopy - methods
Sensitivity and specificity
Abstract
Diabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important features required to identify retinal pathology. Variation in images of the human retina with respect to differences in pigmentation and presence of diverse lesions can be analyzed without the necessity of preprocessing and utilizing different training sets to account for ethnic differences for instance.
PubMed ID
22255695 View in PubMed
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19 records – page 1 of 2.