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Ear biometrics for patient identification in global health: a field study to test the effectiveness of an image stabilization device in improving identification accuracy

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2019年第1期19卷 114-114页

作者： Etter, Lauren P. Ragan, Elizabeth J. Campion, Rachael Martinez, David Gill, Christopher J. Boston Univ Coll Engn Boston MA 02215 USA Boston Med Ctr Infect Dis Sect Dept Med Boston MA USA Boston Univ Sch Publ Hlth Dept Global Hlth 801 Massachusetts Ave Boston MA 02118 USA

BackgroundIn many low and middle-income countries (LMICs), difficulties in patient identification are a major obstacle to the delivery of longitudinal care. In absence of unique identifiers, biometrics have emerged as an attractive solution to the identification problem. We developed an mHealth App for subject identification using pattern recognition around ear morphology (Project SEARCH (Scanning EARS for Child Health). Early field work with the SEARCH App revealed that image stabilization would be required for optimum *** improve image capture, we designed and tested a device (the Donut'), which standardizes distance, angle, rotation and lighting. We then ran an experimental trial with 194 participants to measure the impact of the Donut on identification rates. Images of the participant's left ear were taken both with and without use of the Donut, then processed by the SEARCH algorithm, measuring the top one and top ten most likely *** the Donut, the top one identification rate and top ten identification rates were 99.5 and 99.5%, respectively, vs. 38.4 and 24.1%, respectively, without the Donut (P<0.0001 for each comparison). In sensitivity analyses, crop technique during pre-processing of images had a powerful impact on identification rates, but this too was facilitated through the *** standardizing lighting, angle and spatial location of the ear, the Donut achieved near perfect identification rates on a cohort of 194 participants, proving the feasibility and effectiveness of using the ear as a biometric *** registrationThis study did not include a medical intervention or assess a medical outcome, and therefore did not meet the definition of a human subjects research study as defined by FDAAA. We did not register our study under ***.

关键词： Ear biometrics Identification Patient identification Global health Public health Electronic medical record Image stabilization pattern recognition algorithm

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A Mobile Application for Early Detection of Melanoma by Image Processing algorithms 25

A Mobile Application for Early Detection of Melanoma by Imag...

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25th Iranian Conference on Biomedical Engineering / 3rd International Iranian Conference on Biomedical Engineering (ICBME)

作者： Alizadeh, Seyed Mohammad Mahloojifar, Ali Tarbiat Modares Univ Dept Biomed Engn Tehran Iran

ISBN: (纸本)9781538679524

Melanoma is the most dangerous skin cancer which causes many deaths annually. However, early detection can help treat it. For accurate detection of melanoma, dermatologists use biopsy which is usually associated with pain, time and cost. With the advancement of technology and the development of smartphones, many mobile applications have been designed for early detection of melanoma. Although they are fast in the detection of melanoma and save time and money, they are not as accurate as the biopsy. In this paper, the authors proposed an application for early detection of melanoma using image processing methods and pattern recognition algorithms by Android Studio software, Java programming language, and the OpenCV library. All detection steps were carried out using the Android smartphone. For better performance in the classification step, in addition to the smartphone, a computer was also used. This application is user-friendly and the calculated Accuracy, Sensitivity, and Specificity are 95%, 98%, and 92.19% on average, respectively. It should be noted that these results are more reliable when the lesions are geometrically distinct.

关键词： melanoma image processing smartphone android pattern recognition algorithm OpenCV library

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Laser-induced backscattering imaging for classification of seeded and seedless watermelons

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 140卷 311-316页

作者： Ali, Maimunah Mohd Hashim, Norhashila Bejo, Siti Khairunniza Shamsudin, Rosnah Univ Putra Malaysia Fac Engn Dept Biol & Agr Engn Upm Serdang 43400 Selangor Malaysia Univ Putra Malaysia Fac Engn Dept Food & Proc Engn Upm Serdang 43400 Selangor Malaysia

This paper evaluates the feasibility of laser-induced backscattering imaging for the classification of seeded and seedless watermelons during storage. Backscattering images were obtained from seeded and seedless watermelon samples through a laser diode emitting at 658 nm using a backscattering imaging system developed for the purpose. The pre-processed datasets extracted from the backscattering images were analysed using principal component analysis (PCA). The datasets were separated into training (75%) and testing (25%) datasets as the inputs in the classification algorithms. Three multivariate pattern recognition algorithms were used including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbour (kNN). The QDA-based algorithms obtained the highest overall average classification accuracies (100%) for both the seeded and seedless watermelons. The LDA and kNN-based algorithms also obtained quite high classification accuracies with all the accuracies above 90%. The laser-induced backscattering imaging technique is potentially useful for classification of seeded and seedless watermelons. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Backscattering imaging Laser light Watermelon pattern recognition algorithm Storage

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Time-Aware Multivariate Nearest Neighbor Regression Methods for Traffic Flow Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2015年第6期16卷 3393-3393页

作者： Dell'Acqua, Pietro Bellotti, Francesco Berta, Riccardo De Gloria, Alessandro Univ Insubria Dept Sci & High Technol I-22100 Como Italy Univ Genoa Dept Elect Elect Telecommun Engn & Naval Architec I-16145 Genoa Italy

Traffic flow prediction is a fundamental functionality of intelligent transportation systems. After presenting the state of the art, we focus on nearest neighbor regression methods, which are data-driven algorithms that are effective yet simple to implement. We try to strengthen their efficacy in two ways that are little explored in literature, i.e., by adopting a multivariate approach and by adding awareness of the time of the day. The combination of these two refinements, which represents a novelty, leads to the definition of a new class of methods that we call time-aware multivariate nearest neighbor regression (TaM-NNR) algorithms. To assess this class, we have used publicly available traffic data from a California highway. Computational results show the effectiveness of such algorithms in comparison with state-of-the-art parametric and non-parametric methods. In particular, they consistently perform better than their corresponding standard univariate versions. These facts highlight the importance of context elements in traffic prediction. The ideas presented here may be further investigated considering more context elements (e.g., weather conditions), more complex road topologies (e.g., urban networks), and different types of prediction methods.

关键词： Traffic flow prediction nearest neighbor regression multivariate analysis context awareness pattern recognition algorithm Bayesian networks

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Design and Evaluation of a Smartphone Application for Non-Speech Sound Awareness for People with Hearing Loss 37

Design and Evaluation of a Smartphone Application for Non-Sp...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Mielke, Matthias Brueck, Rainer Univ Siegen Microsyst Engn Grp D-57076 Siegen Germany

ISBN: (纸本)9781424492701

Auditory cues provide people with information about events outside of their field of view and can so help detecting potential hazards. People with severe hearing loss, i.e. hard of hearing, deafened, and deaf people, often cannot or can only partially benefit from auditory cues, which can lead to a lowered quality of life. Even though hearing aids are available, not everyone can benefit from them. To provide deaf people (i.e. deaf, deafened, and hard of hearing) with auditory cues from environmental sounds different assistive devices have been introduced, usually for use at home. A flexible and mobile assistive device basing on a smartphone is presented in this contribution. It detects and recognizes acoustic events by analysing the acoustic environment of the user. By using pattern recognition algorithms the user can define the sounds that should be recognised by the device. Warning sounds occurring in road traffic were chosen as demonstrator. Interviews were conducted with deaf people to evaluate the concept and to gain more insights into the user needs and expectations.

关键词： handicapped aids hearing hearing aids mobile computing smart phones auditory cues hearing loss mobile assistive device nonspeech sound awareness pattern recognition algorithm smartphone application Acoustics Assistive devices Auditory system Feature extraction Interviews pattern recognition Vibrations Hearing Aids Hearing Disorders Smartphone Auxiliary equipment Hearing Impaired Persons handicapped aids Auditory System Hearing Vibration Acoustics pattern recognition Mobile Computing Feature extraction Interviews SOUND

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BIOCAT: a pattern recognition platform for customizable biological image classification and annotation

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BMC BIOINFORMATICS 2013年第1期14卷 1-14页

作者： Zhou, Jie Lamichhane, Santosh Sterne, Gabriella Ye, Bing Peng, Hanchuan No Illinois Univ Dept Comp Sci De Kalb IL 60115 USA Univ Michigan Inst Life Sci Ann Arbor MI 48109 USA Univ Michigan Dept Cell & Dev Biol Ann Arbor MI 48109 USA Allen Inst Brain Sci Seattle WA 98103 USA

Background: pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. Results: We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. Conclusions: BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three-dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions

关键词： Image Classification Zernike Moment algorithm Module Biological Image pattern recognition algorithm

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Dual channel detection of ultra low concentration of bacteria in real time by scanning fluorescence correlation spectroscopy

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MEASUREMENT SCIENCE AND TECHNOLOGY 2013年第6期24卷 65702页

作者： Altamore, Ilaria Lanzano, Luca Gratton, Enrico Univ Calif Irvine Dept Biomed Engn Fluorescence Dynam Lab Irvine CA 92717 USA

We describe a novel method to detect very low concentrations of bacteria in water. Our device consists of a portable horizontal geometry small confocal microscope with large pinhole and a holder for cylindrical cuvettes containing the sample. Two motors provide fast rotational and slow vertical motion of the cuvette so the device looks like a simplified flow cytometer without flow. To achieve high sensitivity, the design has two detection channels. Bacteria are stained by two different nucleic acid dyes and excited with two different lasers. Data are analyzed with a correlation filter based on particle passage pattern recognition. The passage of a particle through the illumination volume is compared with a Gaussian pattern in both channels. The width of the Gaussian correlates with the time of passage of the particle so one particle is counted when the algorithm finds a match with a Gaussian in both channels. The concentration of particles in the sample is deduced from the total number of coincident hits and the total volume scanned. This portable setup provides higher sensitivity, low-cost advantage, and it can have a wide use ranging from clinical applications to pollution monitors and water and air quality control.

关键词： bacteria detection ultra-low concentration pattern recognition algorithm

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Automated interpretation of ANCA patterns - a new approach in the serology of ANCA-associated vasculitis

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ARTHRITIS RESEARCH & THERAPY 2012年第6期14卷 R271-R271页

作者： Knuetter, Ilka Hiemann, Rico Brumma, Therese Buettner, Thomas Grossmann, Kai Cusini, Marco Pregnolato, Francesca Borghi, Maria Orietta Anderer, Ursula Conrad, Karsten Reinhold, Dirk Roggenbuck, Dirk Csernok, Elena GA Gener Assays GmbH Dept Res & Dev D-15827 Dahlewitz Berlin Germany Lausitz Univ Appl Sci Fac Sci D-01968 Senftenberg Germany Fdn Ca Granda Osped Maggiore Policlin Dept Dermatol I-20122 Milan Italy Univ Milan IRCCS Ist Auxol Italiano Immune Res Lab I-20149 Milan Italy Univ Milan Dept Clin Sci & Commun I-20149 Milan Italy Tech Univ Dresden Inst Immunol D-01307 Dresden Germany Univ Magdeburg Inst Mol & Clin Immunol D-39120 Magdeburg Germany Univ Schleswig Holstein Dept Rheumatol D-24576 Bad Bramstedt Germany Rheumaklin Bad Bramstedt D-24576 Bad Bramstedt Germany

Introduction: Indirect immunofluorescence (IIF) employing ethanol-fixed neutrophils (ethN) is still the method of choice for assessing antineutrophil cytoplasmic antibodies (ANCA) in ANCA-associated vasculitides (AAV). However, conventional fluorescence microscopy is subjective and prone to high variability. The objective of this study was to evaluate novel pattern recognition algorithms for the standardized automated interpretation of ANCA patterns. Methods: Seventy ANCA-positive samples (20 antimyeloperoxidase ANCA, 50 antiproteinase3 ANCA) and 100 controls from healthy individuals analyzed on ethN and formalin-fixed neutrophils (formN) by IIF were used as a 'training set' for the development of pattern recognition algorithms. Sera from 342 patients ('test set') with AAV and other systemic rheumatic and infectious diseases were tested for ANCA patterns using the novel pattern recognition algorithms and conventional fluorescence microscopy. Results: Interpretation software employing pattern recognition algorithms was developed enabling positive/negative discrimination and classification of cytoplasmic ANCA (C-ANCA) and perinuclear ANCA (P-ANCA). Comparison of visual reading of the 'test set' samples with automated interpretation revealed Cohen's kappa (kappa) values of 0.955 on ethN and 0.929 on formN for positive/negative discrimination. Analysis of the 'test set' with regard to the discrimination between C-ANCA and P-ANCA patterns showed a high agreement for ethN (kappa = 0.746) and formN (kappa = 0.847). There was no significant difference between visual and automated interpretation regarding positive/negative discrimination on ethN and formN, as well as ANCA pattern recognition (P > 0.05, respectively). Conclusions: pattern recognition algorithms can assist in the automated interpretation of ANCA IIF. Automated reading of ethN and formN IIF patterns demonstrated high consistency with visual ANCA assessment.

关键词： Visual Interpretation pattern recognition algorithm Birmingham Vasculitis Activity Score Automate Interpretation Automate Reading

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Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2011年第1期8卷 25-25页

作者： Lorrain, Thomas Jiang, Ning Farina, Dario Univ Gottingen Univ Med Ctr Gottingen Bernstein Ctr Computat Neurosci Dept Neurorehabil Engn Gottingen Germany Aalborg Univ Denmark Dept Hlth Sci & Technol Aalborg Denmark Otto Bock HealthCare GmbH Strateg Technol Management D-37115 Duderstadt Germany

Background: For high usability, myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. Methods: A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. Results: It is shown that, combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. Conclusions: Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses.

关键词： Support Vector Machine Linear Discriminant Analysis Dynamic Contraction pattern recognition algorithm Myoelectric Control

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Activity-Based Process Mining for Clinical Pathways Computer Aided Design

Activity-Based Process Mining for Clinical Pathways Computer...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Fernandez-Llatas, Carlos Meneu, Teresa Benedi, Jose Miguel Traver, Vicente Univ Politecn Valencia ITACA Inst TSB Grp Valencia Spain Univ Politecn Valencia ITI Inst Valencia Spain

ISBN: (纸本)9781424441242

Current trends in health management improvement demand the standardization of care protocols to achieve better quality and efficiency. The use of Clinical Pathways is an emerging solution for that problem. However, current Clinical Pathways are big manuals written in natural language and highly affected by human subjectivity. These problems make the deployment and dissemination of them extremely difficult in real practice environments. In this work, a complete computer based architecture to help the representation and execution of Clinical Pathways is suggested. Furthermore, the difficulties inherent to the design of formal Clinical Pathways in this way requires new specific design tools to help making the system useful. Process Mining techniques can help to automatically infer processes definition from execution samples. Yet, the classical Process Mining paradigm is not totally compatible with the Clinical Pathways paradigm. In this paper, a pattern recognition algorithm based in an evolution of the Process Mining classical paradigm is presented and evaluated as a solution to this situation. The proposed algorithm is able to infer Clinical Pathways from execution logs to support the design of Clinical Pathways.

关键词： CAD bioinformatics classical process mining paradigm clinical pathways paradigm data mining health care health management medical computing pattern recognition pattern recognition algorithm process mining technique

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