Camera systems in human motion analysis for biomedical applications

NASA Astrophysics Data System (ADS)

Chin, Lim Chee; Basah, Shafriza Nisha; Yaacob, Sazali; Juan, Yeap Ewe; Kadir, Aida Khairunnisaa Ab.

2015-05-01

Human Motion Analysis (HMA) system has been one of the major interests among researchers in the field of computer vision, artificial intelligence and biomedical engineering and sciences. This is due to its wide and promising biomedical applications, namely, bio-instrumentation for human computer interfacing and surveillance system for monitoring human behaviour as well as analysis of biomedical signal and image processing for diagnosis and rehabilitation applications. This paper provides an extensive review of the camera system of HMA, its taxonomy, including camera types, camera calibration and camera configuration. The review focused on evaluating the camera system consideration of the HMA system specifically for biomedical applications. This review is important as it provides guidelines and recommendation for researchers and practitioners in selecting a camera system of the HMA system for biomedical applications.

Review of spectral imaging technology in biomedical engineering: achievements and challenges.

PubMed

Li, Qingli; He, Xiaofu; Wang, Yiting; Liu, Hongying; Xu, Dongrong; Guo, Fangmin

2013-10-01

Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Multidimensional Processing and Visual Rendering of Complex 3D Biomedical Images

NASA Technical Reports Server (NTRS)

Sams, Clarence F.

2016-01-01

The proposed technology uses advanced image analysis techniques to maximize the resolution and utility of medical imaging methods being used during spaceflight. We utilize COTS technology for medical imaging, but our applications require higher resolution assessment of the medical images than is routinely applied with nominal system software. By leveraging advanced data reduction and multidimensional imaging techniques utilized in analysis of Planetary Sciences and Cell Biology imaging, it is possible to significantly increase the information extracted from the onboard biomedical imaging systems. Year 1 focused on application of these techniques to the ocular images collected on ground test subjects and ISS crewmembers. Focus was on the choroidal vasculature and the structure of the optic disc. Methods allowed for increased resolution and quantitation of structural changes enabling detailed assessment of progression over time. These techniques enhance the monitoring and evaluation of crew vision issues during space flight.

Ultrasound tissue analysis and characterization

NASA Astrophysics Data System (ADS)

Kaufhold, John; Chan, Ray C.; Karl, William C.; Castanon, David A.

1999-07-01

On the battlefield of the future, it may become feasible for medics to perform, via application of new biomedical technologies, more sophisticated diagnoses and surgery than is currently practiced. Emerging biomedical technology may enable the medic to perform laparoscopic surgical procedures to remove, for example, shrapnel from injured soldiers. Battlefield conditions constrain the types of medical image acquisition and interpretation which can be performed. Ultrasound is the only viable biomedical imaging modality appropriate for deployment on the battlefield -- which leads to image interpretation issues because of the poor quality of ultrasound imagery. To help overcome these issues, we develop and implement a method of image enhancement which could aid non-experts in the rapid interpretation and use of ultrasound imagery. We describe an energy minimization approach to finding boundaries in medical images and show how prior information on edge orientation can be incorporated into this framework to detect tissue boundaries oriented at a known angle.

78 FR 107 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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77 FR 72365 - National Institute of Biomedical Imaging and Bioengineering; Notice of Meeting

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2012-12-05

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Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.

PubMed

Nahid, Abdullah-Al; Mehrabi, Mohamad Ali; Kong, Yinan

2018-01-01

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F -Measure value is achieved on both the 40x and 100x datasets.

An enhanced approach for biomedical image restoration using image fusion techniques

NASA Astrophysics Data System (ADS)

Karam, Ghada Sabah; Abbas, Fatma Ismail; Abood, Ziad M.; Kadhim, Kadhim K.; Karam, Nada S.

2018-05-01

Biomedical image is generally noisy and little blur due to the physical mechanisms of the acquisition process, so one of the common degradations in biomedical image is their noise and poor contrast. The idea of biomedical image enhancement is to improve the quality of the image for early diagnosis. In this paper we are using Wavelet Transformation to remove the Gaussian noise from biomedical images: Positron Emission Tomography (PET) image and Radiography (Radio) image, in different color spaces (RGB, HSV, YCbCr), and we perform the fusion of the denoised images resulting from the above denoising techniques using add image method. Then some quantive performance metrics such as signal -to -noise ratio (SNR), peak signal-to-noise ratio (PSNR), and Mean Square Error (MSE), etc. are computed. Since this statistical measurement helps in the assessment of fidelity and image quality. The results showed that our approach can be applied of Image types of color spaces for biomedical images.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

PubMed

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Reconstruction of Mammary Gland Structure Using Three-Dimensional Computer-Based Microscopy

DTIC Science & Technology

2004-08-01

for image analysis in cytology" Ortiz de Solorzano C., R . Malladi , Lockett S. In: Geometric methods in bio-medical image processing. Ravikanth Malladi ...Deschamps T., Idica A.K., Malladi R ., Ortiz de Solorzano C. Journal of Biomedical Optics 9(3):445-453, 2004.. Manuscripts (in preparation): "* "Three...Deschamps T., Idica A.K., 16 Malladi R ., Ortiz de Solorzano C., Proceedings of Photonics West 2003, Vol. 4964, 2003 "* "Automatic and segmentation

Biochemical imaging of tissues by SIMS for biomedical applications

NASA Astrophysics Data System (ADS)

Lee, Tae Geol; Park, Ji-Won; Shon, Hyun Kyong; Moon, Dae Won; Choi, Won Woo; Li, Kapsok; Chung, Jin Ho

2008-12-01

With the development of optimal surface cleaning techniques by cluster ion beam sputtering, certain applications of SIMS for analyzing cells and tissues have been actively investigated. For this report, we collaborated with bio-medical scientists to study bio-SIMS analyses of skin and cancer tissues for biomedical diagnostics. We pay close attention to the setting up of a routine procedure for preparing tissue specimens and treating the surface before obtaining the bio-SIMS data. Bio-SIMS was used to study two biosystems, skin tissues for understanding the effects of photoaging and colon cancer tissues for insight into the development of new cancer diagnostics for cancer. Time-of-flight SIMS imaging measurements were taken after surface cleaning with cluster ion bombardment by Bi n or C 60 under varying conditions. The imaging capability of bio-SIMS with a spatial resolution of a few microns combined with principal component analysis reveal biologically meaningful information, but the lack of high molecular weight peaks even with cluster ion bombardment was a problem. This, among other problems, shows that discourse with biologists and medical doctors are critical to glean any meaningful information from SIMS mass spectrometric and imaging data. For SIMS to be accepted as a routine, daily analysis tool in biomedical laboratories, various practical sample handling methodology such as surface matrix treatment, including nano-metal particles and metal coating, in addition to cluster sputtering, should be studied.

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Biomedical surface analysis: Evolution and future directions (Review)

PubMed Central

Castner, David G.

2017-01-01

This review describes some of the major advances made in biomedical surface analysis over the past 30–40 years. Starting from a single technique analysis of homogeneous surfaces, it has been developed into a complementary, multitechnique approach for obtaining detailed, comprehensive information about a wide range of surfaces and interfaces of interest to the biomedical community. Significant advances have been made in each surface analysis technique, as well as how the techniques are combined to provide detailed information about biological surfaces and interfaces. The driving force for these advances has been that the surface of a biomaterial is the interface between the biological environment and the biomaterial, and so, the state-of-the-art in instrumentation, experimental protocols, and data analysis methods need to be developed so that the detailed surface structure and composition of biomedical devices can be determined and related to their biological performance. Examples of these advances, as well as areas for future developments, are described for immobilized proteins, complex biomedical surfaces, nanoparticles, and 2D/3D imaging of biological materials. PMID:28438024

Telemedicine optoelectronic biomedical data processing system

NASA Astrophysics Data System (ADS)

Prosolovska, Vita V.

2010-08-01

The telemedicine optoelectronic biomedical data processing system is created to share medical information for the control of health rights and timely and rapid response to crisis. The system includes the main blocks: bioprocessor, analog-digital converter biomedical images, optoelectronic module for image processing, optoelectronic module for parallel recording and storage of biomedical imaging and matrix screen display of biomedical images. Rated temporal characteristics of the blocks defined by a particular triggering optoelectronic couple in analog-digital converters and time imaging for matrix screen. The element base for hardware implementation of the developed matrix screen is integrated optoelectronic couples produced by selective epitaxy.

77 FR 74675 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meetings

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2012-12-17

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Image-Processing Program

NASA Technical Reports Server (NTRS)

Roth, D. J.; Hull, D. R.

1994-01-01

IMAGEP manipulates digital image data to effect various processing, analysis, and enhancement functions. It is keyboard-driven program organized into nine subroutines. Within subroutines are sub-subroutines also selected via keyboard. Algorithm has possible scientific, industrial, and biomedical applications in study of flows in materials, analysis of steels and ores, and pathology, respectively.

Advances in nano-scaled biosensors for biomedical applications.

PubMed

Wang, Jianling; Chen, Guihua; Jiang, Hui; Li, Zhiyong; Wang, Xuemei

2013-08-21

Recently, a growing amount of attention has been focused on the utility of biosensors for biomedical applications. Combined with nanomaterials and nanostructures, nano-scaled biosensors are installed for biomedical applications, such as pathogenic bacteria monitoring, virus recognition, disease biomarker detection, among others. These nano-biosensors offer a number of advantages and in many respects are ideally suited to biomedical applications, which could be made as extremely flexible devices, allowing biomedical analysis with speediness, excellent selectivity and high sensitivity. This minireview discusses the literature published in the latest years on the advances in biomedical applications of nano-scaled biosensors for disease bio-marking and detection, especially in bio-imaging and the diagnosis of pathological cells and viruses, monitoring pathogenic bacteria, thus providing insight into the future prospects of biosensors in relevant clinical applications.

Nanoparticles for Biomedical Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nune, Satish K.; Gunda, Padmaja; Thallapally, Praveen K.

2009-11-01

Background: Synthetic nanoparticles are emerging as versatile tools in biomedical applications, particularly in the area of biomedical imaging. Nanoparticles 1 to 100 nm in diameter possess dimensions comparable to biological functional units. Diverse surface chemistries, unique magnetic properties, tunable absorption and emission properties, and recent advances in the synthesis and engineering of various nanoparticles suggest their potential as probes for early detection of diseases such as cancer. Surface functionalization has further expanded the potential of nanoparticles as probes for molecular imaging. Objective: To summarize emerging research of nanoparticles for biomedical imaging with increased selectivity and reduced non-specific uptake with increasedmore » spatial resolution containing stabilizers conjugated with targeting ligands. Methods: This review summarizes recent technological advances in the synthesis of various nanoparticle probes, and surveys methods to improve the targeting of nanoparticles for their applications in biomedical imaging. Conclusion: Structural design of nanomaterials for biomedical imaging continues to expand and diversify. Synthetic methods have aimed to control the size and surface characteristics of nanoparticles to control distribution, half-life and elimination. Although molecular imaging applications using nanoparticles are advancing into clinical applications, challenges such as storage stability and long-term toxicology should continue to be addressed. Keywords: nanoparticle synthesis, surface modification, targeting, molecular imaging, and biomedical imaging.« less

Biomedical imaging graduate curricula and courses: report from the 2005 Whitaker Biomedical Engineering Educational Summit.

PubMed

Louie, Angelique; Izatt, Joseph; Ferrara, Katherine

2006-02-01

We present an overview of graduate programs in biomedical imaging that are currently available in the US. Special attention is given to the emerging technologies of molecular imaging and biophotonics. Discussions from the workshop on Graduate Imaging at the 2005 Whitaker Educational Summit meeting are summarized.

Mathematical models used in segmentation and fractal methods of 2-D ultrasound images

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Moraru, Luminita; Bibicu, Dorin

2012-11-01

Mathematical models are widely used in biomedical computing. The extracted data from images using the mathematical techniques are the "pillar" achieving scientific progress in experimental, clinical, biomedical, and behavioural researches. This article deals with the representation of 2-D images and highlights the mathematical support for the segmentation operation and fractal analysis in ultrasound images. A large number of mathematical techniques are suitable to be applied during the image processing stage. The addressed topics cover the edge-based segmentation, more precisely the gradient-based edge detection and active contour model, and the region-based segmentation namely Otsu method. Another interesting mathematical approach consists of analyzing the images using the Box Counting Method (BCM) to compute the fractal dimension. The results of the paper provide explicit samples performed by various combination of methods.

Software and Algorithms for Biomedical Image Data Processing and Visualization

NASA Technical Reports Server (NTRS)

Talukder, Ashit; Lambert, James; Lam, Raymond

2004-01-01

A new software equipped with novel image processing algorithms and graphical-user-interface (GUI) tools has been designed for automated analysis and processing of large amounts of biomedical image data. The software, called PlaqTrak, has been specifically used for analysis of plaque on teeth of patients. New algorithms have been developed and implemented to segment teeth of interest from surrounding gum, and a real-time image-based morphing procedure is used to automatically overlay a grid onto each segmented tooth. Pattern recognition methods are used to classify plaque from surrounding gum and enamel, while ignoring glare effects due to the reflection of camera light and ambient light from enamel regions. The PlaqTrak system integrates these components into a single software suite with an easy-to-use GUI (see Figure 1) that allows users to do an end-to-end run of a patient s record, including tooth segmentation of all teeth, grid morphing of each segmented tooth, and plaque classification of each tooth image. The automated and accurate processing of the captured images to segment each tooth [see Figure 2(a)] and then detect plaque on a tooth-by-tooth basis is a critical component of the PlaqTrak system to do clinical trials and analysis with minimal human intervention. These features offer distinct advantages over other competing systems that analyze groups of teeth or synthetic teeth. PlaqTrak divides each segmented tooth into eight regions using an advanced graphics morphing procedure [see results on a chipped tooth in Figure 2(b)], and a pattern recognition classifier is then used to locate plaque [red regions in Figure 2(d)] and enamel regions. The morphing allows analysis within regions of teeth, thereby facilitating detailed statistical analysis such as the amount of plaque present on the biting surfaces on teeth. This software system is applicable to a host of biomedical applications, such as cell analysis and life detection, or robotic applications, such as product inspection or assembly of parts in space and industry.

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format.

PubMed

Ahmed, Zeeshan; Dandekar, Thomas

2015-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool 'Mining Scientific Literature (MSL)', which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system's output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format.

Development of Multiscale Biological Image Data Analysis: Review of 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics, Santa Barbara, USA (BII06)

PubMed Central

Auer, Manfred; Peng, Hanchuan; Singh, Ambuj

2007-01-01

The 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics was held at Santa Barbara, on Sept 7–8, 2006. Based on the presentations at the workshop, we selected and compiled this collection of research articles related to novel algorithms and enabling techniques for bio- and biomedical image analysis, mining, visualization, and biology applications. PMID:17634090

77 FR 50516 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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Role of Kinetic Modeling in Biomedical Imaging

PubMed Central

Huang, Sung-Cheng

2009-01-01

Biomedical imaging can reveal clear 3-dimensional body morphology non-invasively with high spatial resolution. Its efficacy, in both clinical and pre-clinical settings, is enhanced with its capability to provide in vivo functional/biological information in tissue. The role of kinetic modeling in providing biological/functional information in biomedical imaging is described. General characteristics and limitations in extracting biological information are addressed and practical approaches to solve the problems are discussed and illustrated with examples. Some future challenges and opportunities for kinetic modeling to expand the capability of biomedical imaging are also presented. PMID:20640185

The ImageJ ecosystem: an open platform for biomedical image analysis

PubMed Central

Schindelin, Johannes; Rueden, Curtis T.; Hiner, Mark C.; Eliceiri, Kevin W.

2015-01-01

Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is available – from commercial to academic, special-purpose to Swiss army knife, small to large–but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open-software platform ImageJ has had a huge impact on life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts life science, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem. PMID:26153368

The ImageJ ecosystem: An open platform for biomedical image analysis.

PubMed

Schindelin, Johannes; Rueden, Curtis T; Hiner, Mark C; Eliceiri, Kevin W

2015-01-01

Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is available-from commercial to academic, special-purpose to Swiss army knife, small to large-but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open-software platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem. © 2015 Wiley Periodicals, Inc.

Managing biomedical image metadata for search and retrieval of similar images.

PubMed

Korenblum, Daniel; Rubin, Daniel; Napel, Sandy; Rodriguez, Cesar; Beaulieu, Chris

2011-08-01

Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

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MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format

PubMed Central

Ahmed, Zeeshan; Dandekar, Thomas

2018-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool ‘Mining Scientific Literature (MSL)’, which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system’s output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format. PMID:29721305

Modern technologies for retinal scanning and imaging: an introduction for the biomedical engineer

PubMed Central

2014-01-01

This review article is meant to help biomedical engineers and nonphysical scientists better understand the principles of, and the main trends in modern scanning and imaging modalities used in ophthalmology. It is intended to ease the communication between physicists, medical doctors and engineers, and hopefully encourage “classical” biomedical engineers to generate new ideas and to initiate projects in an area which has traditionally been dominated by optical physics. Most of the methods involved are applicable to other areas of biomedical optics and optoelectronics, such as microscopic imaging, spectroscopy, spectral imaging, opto-acoustic tomography, fluorescence imaging etc., all of which are with potential biomedical application. Although all described methods are novel and important, the emphasis of this review has been placed on three technologies introduced in the 1990’s and still undergoing vigorous development: Confocal Scanning Laser Ophthalmoscopy, Optical Coherence Tomography, and polarization-sensitive retinal scanning. PMID:24779618

A Workstation for Interactive Display and Quantitative Analysis of 3-D and 4-D Biomedical Images

PubMed Central

Robb, R.A.; Heffeman, P.B.; Camp, J.J.; Hanson, D.P.

1986-01-01

The capability to extract objective and quantitatively accurate information from 3-D radiographic biomedical images has not kept pace with the capabilities to produce the images themselves. This is rather an ironic paradox, since on the one hand the new 3-D and 4-D imaging capabilities promise significant potential for providing greater specificity and sensitivity (i.e., precise objective discrimination and accurate quantitative measurement of body tissue characteristics and function) in clinical diagnostic and basic investigative imaging procedures than ever possible before, but on the other hand, the momentous advances in computer and associated electronic imaging technology which have made these 3-D imaging capabilities possible have not been concomitantly developed for full exploitation of these capabilities. Therefore, we have developed a powerful new microcomputer-based system which permits detailed investigations and evaluation of 3-D and 4-D (dynamic 3-D) biomedical images. The system comprises a special workstation to which all the information in a large 3-D image data base is accessible for rapid display, manipulation, and measurement. The system provides important capabilities for simultaneously representing and analyzing both structural and functional data and their relationships in various organs of the body. This paper provides a detailed description of this system, as well as some of the rationale, background, theoretical concepts, and practical considerations related to system implementation. ImagesFigure 5Figure 7Figure 8Figure 9Figure 10Figure 11Figure 12Figure 13Figure 14Figure 15Figure 16

Bioinspired Polarization Imaging Sensors: From Circuits and Optics to Signal Processing Algorithms and Biomedical Applications

PubMed Central

York, Timothy; Powell, Samuel B.; Gao, Shengkui; Kahan, Lindsey; Charanya, Tauseef; Saha, Debajit; Roberts, Nicholas W.; Cronin, Thomas W.; Marshall, Justin; Achilefu, Samuel; Lake, Spencer P.; Raman, Baranidharan; Gruev, Viktor

2015-01-01

In this paper, we present recent work on bioinspired polarization imaging sensors and their applications in biomedicine. In particular, we focus on three different aspects of these sensors. First, we describe the electro–optical challenges in realizing a bioinspired polarization imager, and in particular, we provide a detailed description of a recent low-power complementary metal–oxide–semiconductor (CMOS) polarization imager. Second, we focus on signal processing algorithms tailored for this new class of bioinspired polarization imaging sensors, such as calibration and interpolation. Third, the emergence of these sensors has enabled rapid progress in characterizing polarization signals and environmental parameters in nature, as well as several biomedical areas, such as label-free optical neural recording, dynamic tissue strength analysis, and early diagnosis of flat cancerous lesions in a murine colorectal tumor model. We highlight results obtained from these three areas and discuss future applications for these sensors. PMID:26538682

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

PubMed Central

Procházka, Aleš; Schätz, Martin; Vyšata, Oldřich; Vališ, Martin

2016-01-01

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human–machine interaction. PMID:27367687

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis.

PubMed

Procházka, Aleš; Schätz, Martin; Vyšata, Oldřich; Vališ, Martin

2016-06-28

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.

Rudiments of curvelet with applications

NASA Astrophysics Data System (ADS)

Zahra, Noor e.

2012-07-01

Curvelet transform is now a days a favored tool for image processing. Edges are an important part of an image and usually they are not straight lines. Curvelet prove to be very efficient in representing curve like edges. In this chapter application of curvelet is shown with some examples like seismic wave analysis, oil exploration, fingerprint identification and biomedical images like mammography and MRI.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Visual communications and image processing '92; Proceedings of the Meeting, Boston, MA, Nov. 18-20, 1992

NASA Astrophysics Data System (ADS)

Maragos, Petros

The topics discussed at the conference include hierarchical image coding, motion analysis, feature extraction and image restoration, video coding, and morphological and related nonlinear filtering. Attention is also given to vector quantization, morphological image processing, fractals and wavelets, architectures for image and video processing, image segmentation, biomedical image processing, and model-based analysis. Papers are presented on affine models for motion and shape recovery, filters for directly detecting surface orientation in an image, tracking of unresolved targets in infrared imagery using a projection-based method, adaptive-neighborhood image processing, and regularized multichannel restoration of color images using cross-validation. (For individual items see A93-20945 to A93-20951)

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval.

PubMed

Rahman, Md Mahmudur; Antani, Sameer K; Demner-Fushman, Dina; Thoma, George R

2015-10-01

This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term "concept" refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature.

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval

PubMed Central

Rahman, Md. Mahmudur; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-01-01

Abstract. This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term “concept” refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature. PMID:26730398

Biomedical imaging ontologies: A survey and proposal for future work

PubMed Central

Smith, Barry; Arabandi, Sivaram; Brochhausen, Mathias; Calhoun, Michael; Ciccarese, Paolo; Doyle, Scott; Gibaud, Bernard; Goldberg, Ilya; Kahn, Charles E.; Overton, James; Tomaszewski, John; Gurcan, Metin

2015-01-01

Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data. Aim: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data. PMID:26167381

Regional shape-based feature space for segmenting biomedical images using neural networks

NASA Astrophysics Data System (ADS)

Sundaramoorthy, Gopal; Hoford, John D.; Hoffman, Eric A.

1993-07-01

In biomedical images, structure of interest, particularly the soft tissue structures, such as the heart, airways, bronchial and arterial trees often have grey-scale and textural characteristics similar to other structures in the image, making it difficult to segment them using only gray- scale and texture information. However, these objects can be visually recognized by their unique shapes and sizes. In this paper we discuss, what we believe to be, a novel, simple scheme for extracting features based on regional shapes. To test the effectiveness of these features for image segmentation (classification), we use an artificial neural network and a statistical cluster analysis technique. The proposed shape-based feature extraction algorithm computes regional shape vectors (RSVs) for all pixels that meet a certain threshold criteria. The distance from each such pixel to a boundary is computed in 8 directions (or in 26 directions for a 3-D image). Together, these 8 (or 26) values represent the pixel's (or voxel's) RSV. All RSVs from an image are used to train a multi-layered perceptron neural network which uses these features to 'learn' a suitable classification strategy. To clearly distinguish the desired object from other objects within an image, several examples from inside and outside the desired object are used for training. Several examples are presented to illustrate the strengths and weaknesses of our algorithm. Both synthetic and actual biomedical images are considered. Future extensions to this algorithm are also discussed.

Optical Tecnology Developments in Biomedicine: History, Current and Future

PubMed Central

Nioka, Shoko; Chen, Yu

2011-01-01

Biomedical optics is a rapidly emerging field for medical imaging and diagnostics. This paper reviews several biomedical optical technologies that have been developed and translated for either clinical or pre-clinical applications. Specifically, we focus on the following technologies: 1) near-infrared spectroscopy and tomography, 2) optical coherence tomography, 3) fluorescence spectroscopy and imaging, and 4) optical molecular imaging. There representative biomedical applications are also discussed here. PMID:23905030

78 FR 58547 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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Architecture for biomedical multimedia information delivery on the World Wide Web

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Goh, Gin-Hua; Neve, Leif; Thoma, George R.

1997-10-01

Research engineers at the National Library of Medicine are building a prototype system for the delivery of multimedia biomedical information on the World Wide Web. This paper discuses the architecture and design considerations for the system, which will be used initially to make images and text from the third National Health and Nutrition Examination Survey (NHANES) publicly available. We categorized our analysis as follows: (1) fundamental software tools: we analyzed trade-offs among use of conventional HTML/CGI, X Window Broadway, and Java; (2) image delivery: we examined the use of unconventional TCP transmission methods; (3) database manager and database design: we discuss the capabilities and planned use of the Informix object-relational database manager and the planned schema for the HNANES database; (4) storage requirements for our Sun server; (5) user interface considerations; (6) the compatibility of the system with other standard research and analysis tools; (7) image display: we discuss considerations for consistent image display for end users. Finally, we discuss the scalability of the system in terms of incorporating larger or more databases of similar data, and the extendibility of the system for supporting content-based retrieval of biomedical images. The system prototype is called the Web-based Medical Information Retrieval System. An early version was built as a Java applet and tested on Unix, PC, and Macintosh platforms. This prototype used the MiniSQL database manager to do text queries on a small database of records of participants in the second NHANES survey. The full records and associated x-ray images were retrievable and displayable on a standard Web browser. A second version has now been built, also a Java applet, using the MySQL database manager.

Metrological reliability of optical coherence tomography in biomedical applications

NASA Astrophysics Data System (ADS)

Goloni, C. M.; Temporão, G. P.; Monteiro, E. C.

2013-09-01

Optical coherence tomography (OCT) has been proving to be an efficient diagnostics technique for imaging in vivo tissues, an optical biopsy with important perspectives as a diagnostic tool for quantitative characterization of tissue structures. Despite its established clinical use, there is no international standard to address the specific requirements for basic safety and essential performance of OCT devices for biomedical imaging. The present work studies the parameters necessary for conformity assessment of optoelectronics equipment used in biomedical applications like Laser, Intense Pulsed Light (IPL), and OCT, targeting to identify the potential requirements to be considered in the case of a future development of a particular standard for OCT equipment. In addition to some of the particular requirements standards for laser and IPL, also applicable for metrological reliability analysis of OCT equipment, specific parameters for OCT's evaluation have been identified, considering its biomedical application. For each parameter identified, its information on the accompanying documents and/or its measurement has been recommended. Among the parameters for which the measurement requirement was recommended, including the uncertainty evaluation, the following are highlighted: optical radiation output, axial and transverse resolution, pulse duration and interval, and beam divergence.

Evaluating performance of biomedical image retrieval systems – an overview of the medical image retrieval task at ImageCLEF 2004–2013

PubMed Central

Kalpathy-Cramer, Jayashree; de Herrera, Alba García Seco; Demner-Fushman, Dina; Antani, Sameer; Bedrick, Steven; Müller, Henning

2014-01-01

Medical image retrieval and classification have been extremely active research topics over the past 15 years. With the ImageCLEF benchmark in medical image retrieval and classification a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluations campaigns. A detailed analysis of the data also highlights the value of the resources created. PMID:24746250

Integration of OLEDs in biomedical sensor systems: design and feasibility analysis

NASA Astrophysics Data System (ADS)

Rai, Pratyush; Kumar, Prashanth S.; Varadan, Vijay K.

2010-04-01

Organic (electronic) Light Emitting Diodes (OLEDs) have been shown to have applications in the field of lighting and flexible display. These devices can also be incorporated in sensors as light source for imaging/fluorescence sensing for miniaturized systems for biomedical applications and low-cost displays for sensor output. The current device capability aligns well with the aforementioned applications as low power diffuse lighting and momentary/push button dynamic display. A top emission OLED design has been proposed that can be incorporated with the sensor and peripheral electrical circuitry, also based on organic electronics. Feasibility analysis is carried out for an integrated optical imaging/sensor system, based on luminosity and spectrum band width. A similar study is also carried out for sensor output display system that functions as a pseudo active OLED matrix. A power model is presented for device power requirements and constraints. The feasibility analysis is also supplemented with the discussion about implementation of ink-jet printing and stamping techniques for possibility of roll to roll manufacturing.

Biomedical imaging with THz waves

NASA Astrophysics Data System (ADS)

Nguyen, Andrew

2010-03-01

We discuss biomedical imaging using radio waves operating in the terahertz (THz) range between 300 GHz to 3 THz. Particularly, we present the concept for two THz imaging systems. One system employs single antenna, transmitter and receiver operating over multi-THz-frequency simultaneously for sensing and imaging small areas of the human body or biological samples. Another system consists of multiple antennas, a transmitter, and multiple receivers operating over multi-THz-frequency capable of sensing and imaging simultaneously the whole body or large biological samples. Using THz waves for biomedical imaging promises unique and substantial medical benefits including extremely small medical devices, extraordinarily fine spatial resolution, and excellent contrast between images of diseased and healthy tissues. THz imaging is extremely attractive for detection of cancer in the early stages, sensing and imaging of tissues near the skin, and study of disease and its growth versus time.

A concept-based interactive biomedical image retrieval approach using visualness and spatial information

NASA Astrophysics Data System (ADS)

Rahman, Md M.; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-03-01

This paper presents a novel approach to biomedical image retrieval by mapping image regions to local concepts and represent images in a weighted entropy-based concept feature space. The term concept refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist user in interactively select a Region-Of-Interest (ROI) and search for similar image ROIs. Further, a spatial verification step is used as a post-processing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval, is validated through experiments on a data set of 450 lung CT images extracted from journal articles from four different collections.

Three-dimensional Imaging and Scanning: Current and Future Applications for Pathology

PubMed Central

Farahani, Navid; Braun, Alex; Jutt, Dylan; Huffman, Todd; Reder, Nick; Liu, Zheng; Yagi, Yukako; Pantanowitz, Liron

2017-01-01

Imaging is vital for the assessment of physiologic and phenotypic details. In the past, biomedical imaging was heavily reliant on analog, low-throughput methods, which would produce two-dimensional images. However, newer, digital, and high-throughput three-dimensional (3D) imaging methods, which rely on computer vision and computer graphics, are transforming the way biomedical professionals practice. 3D imaging has been useful in diagnostic, prognostic, and therapeutic decision-making for the medical and biomedical professions. Herein, we summarize current imaging methods that enable optimal 3D histopathologic reconstruction: Scanning, 3D scanning, and whole slide imaging. Briefly mentioned are emerging platforms, which combine robotics, sectioning, and imaging in their pursuit to digitize and automate the entire microscopy workflow. Finally, both current and emerging 3D imaging methods are discussed in relation to current and future applications within the context of pathology. PMID:28966836

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2013-01-17

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Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

PubMed Central

2012-01-01

Background Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme. Results Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered. Conclusions We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. PMID:22316103

Securing quality of camera-based biomedical optics

NASA Astrophysics Data System (ADS)

Guse, Frank; Kasper, Axel; Zinter, Bob

2009-02-01

As sophisticated optical imaging technologies move into clinical applications, manufacturers need to guarantee their products meet required performance criteria over long lifetimes and in very different environmental conditions. A consistent quality management marks critical components features derived from end-users requirements in a top-down approach. Careful risk analysis in the design phase defines the sample sizes for production tests, whereas first article inspection assures the reliability of the production processes. We demonstrate the application of these basic quality principles to camera-based biomedical optics for a variety of examples including molecular diagnostics, dental imaging, ophthalmology and digital radiography, covering a wide range of CCD/CMOS chip sizes and resolutions. Novel concepts in fluorescence detection and structured illumination are also highlighted.

Google glass based immunochromatographic diagnostic test analysis

NASA Astrophysics Data System (ADS)

Feng, Steve; Caire, Romain; Cortazar, Bingen; Turan, Mehmet; Wong, Andrew; Ozcan, Aydogan

2015-03-01

Integration of optical imagers and sensors into recently emerging wearable computational devices allows for simpler and more intuitive methods of integrating biomedical imaging and medical diagnostics tasks into existing infrastructures. Here we demonstrate the ability of one such device, the Google Glass, to perform qualitative and quantitative analysis of immunochromatographic rapid diagnostic tests (RDTs) using a voice-commandable hands-free software-only interface, as an alternative to larger and more bulky desktop or handheld units. Using the built-in camera of Glass to image one or more RDTs (labeled with Quick Response (QR) codes), our Glass software application uploads the captured image and related information (e.g., user name, GPS, etc.) to our servers for remote analysis and storage. After digital analysis of the RDT images, the results are transmitted back to the originating Glass device, and made available through a website in geospatial and tabular representations. We tested this system on qualitative human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) RDTs. For qualitative HIV tests, we demonstrate successful detection and labeling (i.e., yes/no decisions) for up to 6-fold dilution of HIV samples. For quantitative measurements, we activated and imaged PSA concentrations ranging from 0 to 200 ng/mL and generated calibration curves relating the RDT line intensity values to PSA concentration. By providing automated digitization of both qualitative and quantitative test results, this wearable colorimetric diagnostic test reader platform on Google Glass can reduce operator errors caused by poor training, provide real-time spatiotemporal mapping of test results, and assist with remote monitoring of various biomedical conditions.

LOCAL ORTHOGONAL CUTTING METHOD FOR COMPUTING MEDIAL CURVES AND ITS BIOMEDICAL APPLICATIONS

PubMed Central

Einstein, Daniel R.; Dyedov, Vladimir

2010-01-01

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method called local orthogonal cutting (LOC) for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stability and consistency tests. These concepts lend themselves to robust numerical techniques and result in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods. PMID:20628546

Design of e-Science platform for biomedical imaging research cross multiple academic institutions and hospitals

NASA Astrophysics Data System (ADS)

Zhang, Jianguo; Zhang, Kai; Yang, Yuanyuan; Ling, Tonghui; Wang, Tusheng; Wang, Mingqing; Hu, Haibo; Xu, Xuemin

2012-02-01

More and more image informatics researchers and engineers are considering to re-construct imaging and informatics infrastructure or to build new framework to enable multiple disciplines of medical researchers, clinical physicians and biomedical engineers working together in a secured, efficient, and transparent cooperative environment. In this presentation, we show an outline and our preliminary design work of building an e-Science platform for biomedical imaging and informatics research and application in Shanghai. We will present our consideration and strategy on designing this platform, and preliminary results. We also will discuss some challenges and solutions in building this platform.

In-line phase contrast micro-CT reconstruction for biomedical specimens.

PubMed

Fu, Jian; Tan, Renbo

2014-01-01

X-ray phase contrast micro computed tomography (micro-CT) can non-destructively provide the internal structure information of soft tissues and low atomic number materials. It has become an invaluable analysis tool for biomedical specimens. Here an in-line phase contrast micro-CT reconstruction technique is reported, which consists of a projection extraction method and the conventional filter back-projection (FBP) reconstruction algorithm. The projection extraction is implemented by applying the Fourier transform to the forward projections of in-line phase contrast micro-CT. This work comprises a numerical study of the method and its experimental verification using a biomedical specimen dataset measured at an X-ray tube source micro-CT setup. The numerical and experimental results demonstrate that the presented technique can improve the imaging contrast of biomedical specimens. It will be of interest for a wide range of in-line phase contrast micro-CT applications in medicine and biology.

Image BOSS: a biomedical object storage system

NASA Astrophysics Data System (ADS)

Stacy, Mahlon C.; Augustine, Kurt E.; Robb, Richard A.

1997-05-01

Researchers using biomedical images have data management needs which are oriented perpendicular to clinical PACS. The image BOSS system is designed to permit researchers to organize and select images based on research topic, image metadata, and a thumbnail of the image. Image information is captured from existing images in a Unix based filesystem, stored in an object oriented database, and presented to the user in a familiar laboratory notebook metaphor. In addition, the ImageBOSS is designed to provide an extensible infrastructure for future content-based queries directly on the images.

Multiscale Integration of -Omic, Imaging, and Clinical Data in Biomedical Informatics

PubMed Central

Phan, John H.; Quo, Chang F.; Cheng, Chihwen; Wang, May Dongmei

2016-01-01

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data. PMID:23231990

Multiscale integration of -omic, imaging, and clinical data in biomedical informatics.

PubMed

Phan, John H; Quo, Chang F; Cheng, Chihwen; Wang, May Dongmei

2012-01-01

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.

Functional requirements for a central research imaging data repository.

PubMed

Franke, Thomas; Gruetz, Romanus; Dickmann, Frank

2013-01-01

The current situation at many university medical centers regarding the management of biomedical research imaging data leaves much to be desired. In contrast to the recommendations of the German Research Foundation (DFG) and the German Council of Sciences and Humanities regarding the professional management of research data, there are commonly many individual data pools for research data in each institute and the management remains the responsibility of the researcher. A possible solution for this situation would be to install local central repositories for biomedical research imaging data. In this paper, we developed a scenario based on abstracted use-cases for institutional research undertakings as well as collaborative biomedical research projects and analyzed the functional requirements that a local repository would have to fulfill. We determined eight generic categories of functional requirements, which can be viewed as a basic guideline for the minimum functionality of a central repository for biomedical research imaging data.

Scientific Programs and Funding Opportunities at the National Institute of Biomedical Imaging and Bioengineering

NASA Astrophysics Data System (ADS)

Baird, Richard

2006-03-01

The mission of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) is to improve human health by promoting the development and translation of emerging technologies in biomedical imaging and bioengineering. To this end, NIBIB supports a coordinated agenda of research programs in advanced imaging technologies and engineering methods that enable fundamental biomedical discoveries across a broad spectrum of biological processes, disorders, and diseases and have significant potential for direct medical application. These research programs dramatically advance the Nation's healthcare by improving the detection, management and, ultimately, the prevention of disease. The research promoted and supported by NIBIB also is strongly synergistic with other NIH Institutes and Centers as well as across government agencies. This presentation will provide an overview of the scientific programs and funding opportunities supported by NIBIB, highlighting those that are of particular important to the field of medical physics.

Local Orthogonal Cutting Method for Computing Medial Curves and Its Biomedical Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiao, Xiangmin; Einstein, Daniel R.; Dyedov, Volodymyr

2010-03-24

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stabilitymore » and consistency tests. These concepts lend themselves to robust numerical techniques including eigenvalue analysis, weighted least squares approximations, and numerical minimization, resulting in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods.« less

On-Chip Biomedical Imaging

PubMed Central

Göröcs, Zoltán; Ozcan, Aydogan

2012-01-01

Lab-on-a-chip systems have been rapidly emerging to pave the way toward ultra-compact, efficient, mass producible and cost-effective biomedical research and diagnostic tools. Although such microfluidic and micro electromechanical systems achieved high levels of integration, and are capable of performing various important tasks on the same chip, such as cell culturing, sorting and staining, they still rely on conventional microscopes for their imaging needs. Recently several alternative on-chip optical imaging techniques have been introduced, which have the potential to substitute conventional microscopes for various lab-on-a-chip applications. Here we present a critical review of these recently emerging on-chip biomedical imaging modalities, including contact shadow imaging, lensfree holographic microscopy, fluorescent on-chip microscopy and lensfree optical tomography. PMID:23558399

Laser Speckle Imaging to Monitor Microvascular Blood Flow: A Review.

PubMed

Vaz, Pedro G; Humeau-Heurtier, Anne; Figueiras, Edite; Correia, Carlos; Cardoso, Joao

2016-01-01

Laser speckle is a complex interference phenomenon that can easily be understood, in concept, but is difficult to predict mathematically, because it is a stochastic process. The use of laser speckle to produce images, which can carry many types of information, is called laser speckle imaging (LSI). The biomedical applications of LSI started in 1981 and, since then, many scientists have improved the laser speckle theory and developed different imaging techniques. During this process, some inconsistencies have been propagated up to now. These inconsistencies should be clarified in order to avoid errors in future works. This review presents a review of the laser speckle theory used in biomedical applications. Moreover, we also make a review of the practical concepts that are useful in the construction of laser speckle imagers. This study is not only an exposition of the concepts that can be found in the literature but also a critical analysis of the investigations presented so far. Concepts like scatterers velocity distribution, effect of static scatterers, optimal speckle size, light penetration angle, and contrast computation algorithms are discussed in detail.

Extraction of endoscopic images for biomedical figure classification

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; You, Daekeun; Chachra, Suchet; Antani, Sameer; Long, L. R.; Demner-Fushman, Dina; Thoma, George R.

2015-03-01

Modality filtering is an important feature in biomedical image searching systems and may significantly improve the retrieval performance of the system. This paper presents a new method for extracting endoscopic image figures from photograph images in biomedical literature, which are found to have highly diverse content and large variability in appearance. Our proposed method consists of three main stages: tissue image extraction, endoscopic image candidate extraction, and ophthalmic image filtering. For tissue image extraction we use image patch level clustering and MRF relabeling to detect images containing skin/tissue regions. Next, we find candidate endoscopic images by exploiting the round shape characteristics that commonly appear in these images. However, this step needs to compensate for images where endoscopic regions are not entirely round. In the third step we filter out the ophthalmic images which have shape characteristics very similar to the endoscopic images. We do this by using text information, specifically, anatomy terms, extracted from the figure caption. We tested and evaluated our method on a dataset of 115,370 photograph figures, and achieved promising precision and recall rates of 87% and 84%, respectively.

Multifunctional Poly(L-lactide)-Polyethylene Glycol-Grafted Graphene Quantum Dots for Intracellular MicroRNA Imaging and Combined Specific-Gene-Targeting Agents Delivery for Improved Therapeutics.

PubMed

Dong, Haifeng; Dai, Wenhao; Ju, Huangxian; Lu, Huiting; Wang, Shiyan; Xu, Liping; Zhou, Shu-Feng; Zhang, Yue; Zhang, Xueji

2015-05-27

Photoluminescent (PL) graphene quantum dots (GQDs) with large surface area and superior mechanical flexibility exhibit fascinating optical and electronic properties and possess great promising applications in biomedical engineering. Here, a multifunctional nanocomposite of poly(l-lactide) (PLA) and polyethylene glycol (PEG)-grafted GQDs (f-GQDs) was proposed for simultaneous intracellular microRNAs (miRNAs) imaging analysis and combined gene delivery for enhanced therapeutic efficiency. The functionalization of GQDs with PEG and PLA imparts the nanocomposite with super physiological stability and stable photoluminescence over a broad pH range, which is vital for cell imaging. Cell experiments demonstrate the f-GQDs excellent biocompatibility, lower cytotoxicity, and protective properties. Using the HeLa cell as a model, we found the f-GQDs effectively delivered a miRNA probe for intracellular miRNA imaging analysis and regulation. Notably, the large surface of GQDs was capable of simultaneous adsorption of agents targeting miRNA-21 and survivin, respectively. The combined conjugation of miRNA-21-targeting and survivin-targeting agents induced better inhibition of cancer cell growth and more apoptosis of cancer cells, compared with conjugation of agents targeting miRNA-21 or survivin alone. These findings highlight the promise of the highly versatile multifunctional nanocomposite in biomedical application of intracellular molecules analysis and clinical gene therapeutics.

[A web-based biomedical image mosaicing system].

PubMed

Zhang, Meng; Yan, Zhuang-zhi; Pan, Zhi-jun; Shao, Shi-jie

2006-11-01

This paper describes a web service for biomedical image mosaicing. A web site based on CGI (Common Gateway Interface) is implemented. The system is based on Browser/Server model and is tested in www. Finally implementation examples and experiment results are provided.

Window classification of brain CT images in biomedical articles.

PubMed

Xue, Zhiyun; Antani, Sameer; Long, L Rodney; Demner-Fushman, Dina; Thoma, George R

2012-01-01

Effective capability to search biomedical articles based on visual properties of article images may significantly augment information retrieval in the future. In this paper, we present a new method to classify the window setting types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone dysplasia which are usually examined using the " bone window" setting and illustrated in biomedical articles using "bone window images". Due to the inherent large variations of images among articles, it is important that the proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or non-bone window in a 210 image data set.

Image segmentation for biomedical applications based on alternating sequential filtering and watershed transformation

NASA Astrophysics Data System (ADS)

Gorpas, D.; Yova, D.

2009-07-01

One of the major challenges in biomedical imaging is the extraction of quantified information from the acquired images. Light and tissue interaction leads to the acquisition of images that present inconsistent intensity profiles and thus the accurate identification of the regions of interest is a rather complicated process. On the other hand, the complex geometries and the tangent objects that very often are present in the acquired images, lead to either false detections or to the merging, shrinkage or expansion of the regions of interest. In this paper an algorithm, which is based on alternating sequential filtering and watershed transformation, is proposed for the segmentation of biomedical images. This algorithm has been tested over two applications, each one based on different acquisition system, and the results illustrate its accuracy in segmenting the regions of interest.

Preprocessing film-copied MRI for studying morphological brain changes.

PubMed

Pham, Tuan D; Eisenblätter, Uwe; Baune, Bernhard T; Berger, Klaus

2009-06-15

The magnetic resonance imaging (MRI) of the brain is one of the important data items for studying memory and morbidity in elderly as these images can provide useful information through the quantitative measures of various regions of interest of the brain. As an effort to fully automate the biomedical analysis of the brain that can be combined with the genetic data of the same human population and where the records of the original MRI data are missing, this paper presents two effective methods for addressing this imaging problem. The first method handles the restoration of the film-copied MRI. The second method involves the segmentation of the image data. Experimental results and comparisons with other methods suggest the usefulness of the proposed image analysis methodology.

Directional ratio based on parabolic molecules and its application to the analysis of tubular structures

NASA Astrophysics Data System (ADS)

Labate, Demetrio; Negi, Pooran; Ozcan, Burcin; Papadakis, Manos

2015-09-01

As advances in imaging technologies make more and more data available for biomedical applications, there is an increasing need to develop efficient quantitative algorithms for the analysis and processing of imaging data. In this paper, we introduce an innovative multiscale approach called Directional Ratio which is especially effective to distingush isotropic from anisotropic structures. This task is especially useful in the analysis of images of neurons, the main units of the nervous systems which consist of a main cell body called the soma and many elongated processes called neurites. We analyze the theoretical properties of our method on idealized models of neurons and develop a numerical implementation of this approach for analysis of fluorescent images of cultured neurons. We show that this algorithm is very effective for the detection of somas and the extraction of neurites in images of small circuits of neurons.

Human emotions detection based on a smart-thermal system of thermographic images

NASA Astrophysics Data System (ADS)

Cruz-Albarran, Irving A.; Benitez-Rangel, Juan P.; Osornio-Rios, Roque A.; Morales-Hernandez, Luis A.

2017-03-01

This work presents a noninvasive methodology to obtain biomedical thermal imaging which provide relevant information that may assist in the diagnosis of emotions. Biomedical thermal images of the facial expressions of 44 subjects were captured experiencing joy, disgust, anger, fear and sadness. The analysis of these thermograms was carried out through its thermal value not with its intensity value. Regions of interest were obtained through image processing techniques that allow to differentiate between the subject and the background, having only the subject, the centers of each region of interest were obtained in order to get the same region of the face for each subject. Through the thermal analysis a biomarker for each region of interest was obtained, these biomarkers can diagnose when an emotion takes place. Because each subject tends to react differently to the same stimuli, a self-calibration phase is proposed, its function is to have the same thermal trend for each subject in order to make a decision so that the five emotions can be correctly diagnosed through a top-down hierarchical classifier. As a final result, a smart-thermal system that diagnose emotions was obtained and it was tested on twenty-five subjects (625 thermograms). The results of this test were 89.9% successful.

Finding and accessing diagrams in biomedical publications.

PubMed

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar and line charts by the shape and relative location of the different image elements that make up the charts. With recall and precisions of close to 90% for the detection of relevant figures, we discuss the use of this technology in an existing biomedical image search engine, and outline how it enables new forms of literature queries over biomedical relationships that are represented in these charts.

[The application of stereology in radiology imaging and cell biology fields].

PubMed

Hu, Na; Wang, Yan; Feng, Yuanming; Lin, Wang

2012-08-01

Stereology is an interdisciplinary method for 3D morphological study developed from mathematics and morphology. It is widely used in medical image analysis and cell biology studies. Because of its unbiased, simple, fast, reliable and non-invasive characteristics, stereology has been widely used in biomedical areas for quantitative analysis and statistics, such as histology, pathology and medical imaging. Because the stereological parameters show distinct differences in different pathology, many scholars use stereological methods to do quantitative analysis in their studies in recent years, for example, in the areas of the condition of cancer cells, tumor grade, disease development and the patient's prognosis, etc. This paper describes the stereological concept and estimation methods, also illustrates the applications of stereology in the fields of CT images, MRI images and cell biology, and finally reflects the universality, the superiority and reliability of stereology.

Histology image analysis for carcinoma detection and grading

PubMed Central

He, Lei; Long, L. Rodney; Antani, Sameer; Thoma, George R.

2012-01-01

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems. PMID:22436890

The development of biomedical engineering as experienced by one biomedical engineer

PubMed Central

2012-01-01

This personal essay described the development of the field of Biomedical Engineering from its early days, from the perspective of one who lived through that development. It describes the making of a major invention using data that had been rejected by other scientists, the re-discovery of an obscure fact of physiology and its use in developing a major medical instrument, the development of a new medical imaging modality, and the near-death rescue of a research project. The essay concludes with comments about the development and present status of impedance imaging, and recent changes in the evolution of biomedical engineering as a field. PMID:23234267

The development of biomedical engineering as experienced by one biomedical engineer.

PubMed

Newell, Jonathan C

2012-12-12

This personal essay described the development of the field of Biomedical Engineering from its early days, from the perspective of one who lived through that development. It describes the making of a major invention using data that had been rejected by other scientists, the re-discovery of an obscure fact of physiology and its use in developing a major medical instrument, the development of a new medical imaging modality, and the near-death rescue of a research project. The essay concludes with comments about the development and present status of impedance imaging, and recent changes in the evolution of biomedical engineering as a field.

Quantum Cascade Lasers in Biomedical Infrared Imaging.

PubMed

Bird, Benjamin; Baker, Matthew J

2015-10-01

Technological advances, namely the integration of quantum cascade lasers (QCLs) within an infrared (IR) microscope, are enabling the development of valuable label-free biomedical-imaging tools capable of targeting and detecting salient chemical species within practical clinical timeframes. Copyright © 2015 Elsevier Ltd. All rights reserved.

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

Ultra-wideband sensors for improved magnetic resonance imaging, cardiovascular monitoring and tumour diagnostics.

PubMed

Thiel, Florian; Kosch, Olaf; Seifert, Frank

2010-01-01

The specific advantages of ultra-wideband electromagnetic remote sensing (UWB radar) make it a particularly attractive technique for biomedical applications. We partially review our activities in utilizing this novel approach for the benefit of high and ultra-high field magnetic resonance imaging (MRI) and other applications, e.g., for intensive care medicine and biomedical research. We could show that our approach is beneficial for applications like motion tracking for high resolution brain imaging due to the non-contact acquisition of involuntary head motions with high spatial resolution, navigation for cardiac MRI due to our interpretation of the detected physiological mechanical contraction of the heart muscle and for MR safety, since we have investigated the influence of high static magnetic fields on myocardial mechanics. From our findings we could conclude, that UWB radar can serve as a navigator technique for high and ultra-high field magnetic resonance imaging and can be beneficial preserving the high resolution capability of this imaging modality. Furthermore it can potentially be used to support standard ECG analysis by complementary information where sole ECG analysis fails. Further analytical investigations have proven the feasibility of this method for intracranial displacements detection and the rendition of a tumour's contrast agent based perfusion dynamic. Beside these analytical approaches we have carried out FDTD simulations of a complex arrangement mimicking the illumination of a human torso model incorporating the geometry of the antennas applied.

On the importance of mathematical methods for analysis of MALDI-imaging mass spectrometry data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-21

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 10⁸ to 10⁹ intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-01

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

Biomedical photoacoustics: fundamentals, instrumentation and perspectives on nanomedicine

PubMed Central

Zou, Chunpeng; Wu, Beibei; Dong, Yanyan; Song, Zhangwei; Zhao, Yaping; Ni, Xianwei; Yang, Yan; Liu, Zhe

2017-01-01

Photoacoustic imaging (PAI) is an integrated biomedical imaging modality which combines the advantages of acoustic deep penetration and optical high sensitivity. It can provide functional and structural images with satisfactory resolution and contrast which could provide abundant pathological information for disease-oriented diagnosis. Therefore, it has found vast applications so far and become a powerful tool of precision nanomedicine. However, the investigation of PAI-based imaging nanomaterials is still in its infancy. This perspective article aims to summarize the developments in photoacoustic technologies and instrumentations in the past years, and more importantly, present a bright outlook for advanced PAI-based imaging nanomaterials as well as their emerging biomedical applications in nanomedicine. Current challenges and bottleneck issues have also been discussed and elucidated in this article to bring them to the attention of the readership. PMID:28053532

Tissue polarimetry: concepts, challenges, applications, and outlook.

PubMed

Ghosh, Nirmalya; Vitkin, I Alex

2011-11-01

Polarimetry has a long and successful history in various forms of clear media. Driven by their biomedical potential, the use of the polarimetric approaches for biological tissue assessment has also recently received considerable attention. Specifically, polarization can be used as an effective tool to discriminate against multiply scattered light (acting as a gating mechanism) in order to enhance contrast and to improve tissue imaging resolution. Moreover, the intrinsic tissue polarimetry characteristics contain a wealth of morphological and functional information of potential biomedical importance. However, in a complex random medium-like tissue, numerous complexities due to multiple scattering and simultaneous occurrences of many scattering and polarization events present formidable challenges both in terms of accurate measurements and in terms of analysis of the tissue polarimetry signal. In order to realize the potential of the polarimetric approaches for tissue imaging and characterization/diagnosis, a number of researchers are thus pursuing innovative solutions to these challenges. In this review paper, we summarize these and other issues pertinent to the polarized light methodologies in tissues. Specifically, we discuss polarized light basics, Stokes-Muller formalism, methods of polarization measurements, polarized light modeling in turbid media, applications to tissue imaging, inverse analysis for polarimetric results quantification, applications to quantitative tissue assessment, etc.

78 FR 76634 - National Institute of Biomedical Imaging and Bioengineering; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-18

... such as patentable material, and personal information concerning individuals associated with the grant... of personal privacy. Name of Committee: National Advisory Council for Biomedical Imaging and..., Bethesda, MD 20892. Any interested person may file written comments with the committee by forwarding the...

75 FR 76019 - National Institute of Biomedical Imaging and Bioengineering; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-07

... strategic plan. Place: Bethesda Marriott Suites, 6711 Democracy Boulevard, Independence Room (2nd Level... proposals. Place: Bethesda Marriott Suites, 6711 Democracy Boulevard, Independence Room (2nd Level), Bethesda, MD 20817. Contact Person: Anthony Demsey, PhD, Director, National Institute of Biomedical Imaging...

Semiconductor Quantum Dots for Bioimaging and Biodiagnostic Applications

NASA Astrophysics Data System (ADS)

Kairdolf, Brad A.; Smith, Andrew M.; Stokes, Todd H.; Wang, May D.; Young, Andrew N.; Nie, Shuming

2013-06-01

Semiconductor quantum dots (QDs) are light-emitting particles on the nanometer scale that have emerged as a new class of fluorescent labels for chemical analysis, molecular imaging, and biomedical diagnostics. Compared with traditional fluorescent probes, QDs have unique optical and electronic properties such as size-tunable light emission, narrow and symmetric emission spectra, and broad absorption spectra that enable the simultaneous excitation of multiple fluorescence colors. QDs are also considerably brighter and more resistant to photobleaching than are organic dyes and fluorescent proteins. These properties are well suited for dynamic imaging at the single-molecule level and for multiplexed biomedical diagnostics at ultrahigh sensitivity. Here, we discuss the fundamental properties of QDs; the development of next-generation QDs; and their applications in bioanalytical chemistry, dynamic cellular imaging, and medical diagnostics. For in vivo and clinical imaging, the potential toxicity of QDs remains a major concern. However, the toxic nature of cadmium-containing QDs is no longer a factor for in vitro diagnostics, so the use of multicolor QDs for molecular diagnostics and pathology is probably the most important and clinically relevant application for semiconductor QDs in the immediate future.

Semiconductor quantum dots for bioimaging and biodiagnostic applications.

PubMed

Kairdolf, Brad A; Smith, Andrew M; Stokes, Todd H; Wang, May D; Young, Andrew N; Nie, Shuming

2013-01-01

Semiconductor quantum dots (QDs) are light-emitting particles on the nanometer scale that have emerged as a new class of fluorescent labels for chemical analysis, molecular imaging, and biomedical diagnostics. Compared with traditional fluorescent probes, QDs have unique optical and electronic properties such as size-tunable light emission, narrow and symmetric emission spectra, and broad absorption spectra that enable the simultaneous excitation of multiple fluorescence colors. QDs are also considerably brighter and more resistant to photobleaching than are organic dyes and fluorescent proteins. These properties are well suited for dynamic imaging at the single-molecule level and for multiplexed biomedical diagnostics at ultrahigh sensitivity. Here, we discuss the fundamental properties of QDs; the development of next-generation QDs; and their applications in bioanalytical chemistry, dynamic cellular imaging, and medical diagnostics. For in vivo and clinical imaging, the potential toxicity of QDs remains a major concern. However, the toxic nature of cadmium-containing QDs is no longer a factor for in vitro diagnostics, so the use of multicolor QDs for molecular diagnostics and pathology is probably the most important and clinically relevant application for semiconductor QDs in the immediate future.

Semiconductor Quantum Dots for Bioimaging and Biodiagnostic Applications

PubMed Central

Kairdolf, Brad A.; Smith, Andrew M.; Stokes, Todd H.; Wang, May D.; Young, Andrew N.; Nie, Shuming

2013-01-01

Semiconductor quantum dots (QDs) are light-emitting particles on the nanometer scale that have emerged as a new class of fluorescent labels for chemical analysis, molecular imaging, and biomedical diagnostics. Compared with traditional fluorescent probes, QDs have unique optical and electronic properties such as size-tunable light emission, narrow and symmetric emission spectra, and broad absorption spectra that enable the simultaneous excitation of multiple fluorescence colors. QDs are also considerably brighter and more resistant to photobleaching than are organic dyes and fluorescent proteins. These properties are well suited for dynamic imaging at the single-molecule level and for multiplexed biomedical diagnostics at ultrahigh sensitivity. Here, we discuss the fundamental properties of QDs; the development of next-generation QDs; and their applications in bioanalytical chemistry, dynamic cellular imaging, and medical diagnostics. For in vivo and clinical imaging, the potential toxicity of QDs remains a major concern. However, the toxic nature of cadmium-containing QDs is no longer a factor for in vitro diagnostics, so the use of multicolor QDs for molecular diagnostics and pathology is probably the most important and clinically relevant application for semiconductor QDs in the immediate future. PMID:23527547

Structure-Preserving Smoothing of Biomedical Images

NASA Astrophysics Data System (ADS)

Gil, Debora; Hernàndez-Sabaté, Aura; Burnat, Mireia; Jansen, Steven; Martínez-Villalta, Jordi

Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood.

Non-Contact Optical Ultrasound Concept for Biomedical Imaging

DTIC Science & Technology

2016-11-03

Non -Contact Optical Ultrasound Concept for Biomedical Imaging Robert Haupt1, Charles Wynn1, Jonathan Fincke2, Shawn Zhang2, Brian Anthony2...results. Lastly, we present imaging capabilities using a non -contact laser ultrasound proof-of-concept system. Two and three dimensional time... non -contact, standoff optical ultrasound has the potential to provide a fixed reference measurement capability that minimizes operator variability as

Potential medical applications of TAE

NASA Technical Reports Server (NTRS)

Fahy, J. Ben; Kaucic, Robert; Kim, Yongmin

1986-01-01

In cooperation with scientists in the University of Washington Medical School, a microcomputer-based image processing system for quantitative microscopy, called DMD1 (Digital Microdensitometer 1) was constructed. In order to make DMD1 transportable to different hosts and image processors, we have been investigating the possibility of rewriting the lower level portions of DMD1 software using Transportable Applications Executive (TAE) libraries and subsystems. If successful, we hope to produce a newer version of DMD1, called DMD2, running on an IBM PC/AT under the SCO XENIX System 5 operating system, using any of seven target image processors available in our laboratory. Following this implementation, copies of the system will be transferred to other laboratories with biomedical imaging applications. By integrating those applications into DMD2, we hope to eventually expand our system into a low-cost general purpose biomedical imaging workstation. This workstation will be useful not only as a self-contained instrument for clinical or research applications, but also as part of a large scale Digital Imaging Network and Picture Archiving and Communication System, (DIN/PACS). Widespread application of these TAE-based image processing and analysis systems should facilitate software exchange and scientific cooperation not only within the medical community, but between the medical and remote sensing communities as well.

Biomedical terahertz imaging with a quantum cascade laser

NASA Astrophysics Data System (ADS)

Kim, Seongsin M.; Hatami, Fariba; Harris, James S.; Kurian, Allison W.; Ford, James; King, Douglas; Scalari, Giacomo; Giovannini, Marcella; Hoyler, Nicolas; Faist, Jerome; Harris, Geoff

2006-04-01

We present biomedical imaging using a single frequency terahertz imaging system based on a low threshold quantum cascade laser emitting at 3.7THz (λ=81μm). With a peak output power of 4mW, coherent terahertz radiation and detection provide a relatively large dynamic range and high spatial resolution. We study image contrast based on water/fat content ratios in different tissues. Terahertz transmission imaging demonstrates a distinct anatomy in a rat brain slice. We also demonstrate malignant tissue contrast in an image of a mouse liver with developed tumors, indicating potential use of terahertz imaging for probing cancerous tissues.

Synthesis and Ligand-Exchange Reactions of a Tri-Tungsten Cluster with Applications in Biomedical Imaging

ERIC Educational Resources Information Center

Noey, Elizabeth; Curtis, Jeff C.; Tam, Sylvia; Pham, David M.; Jones, Ella F.

2011-01-01

In this experiment students are exposed to concepts in inorganic synthesis and various spectroscopies as applied to a tri-tungsten cluster with applications in biomedical imaging. The tungsten-acetate cluster, Na[W[superscript 3](mu-O)[subscript 2](CH[superscript 3]COO)[superscript 9

Biomedical imaging and sensing using flatbed scanners.

PubMed

Göröcs, Zoltán; Ozcan, Aydogan

2014-09-07

In this Review, we provide an overview of flatbed scanner based biomedical imaging and sensing techniques. The extremely large imaging field-of-view (e.g., ~600-700 cm(2)) of these devices coupled with their cost-effectiveness provide unique opportunities for digital imaging of samples that are too large for regular optical microscopes, and for collection of large amounts of statistical data in various automated imaging or sensing tasks. Here we give a short introduction to the basic features of flatbed scanners also highlighting the key parameters for designing scientific experiments using these devices, followed by a discussion of some of the significant examples, where scanner-based systems were constructed to conduct various biomedical imaging and/or sensing experiments. Along with mobile phones and other emerging consumer electronics devices, flatbed scanners and their use in advanced imaging and sensing experiments might help us transform current practices of medicine, engineering and sciences through democratization of measurement science and empowerment of citizen scientists, science educators and researchers in resource limited settings.

Biomedical Imaging and Sensing using Flatbed Scanners

PubMed Central

Göröcs, Zoltán; Ozcan, Aydogan

2014-01-01

In this Review, we provide an overview of flatbed scanner based biomedical imaging and sensing techniques. The extremely large imaging field-of-view (e.g., ~600–700 cm2) of these devices coupled with their cost-effectiveness provide unique opportunities for digital imaging of samples that are too large for regular optical microscopes, and for collection of large amounts of statistical data in various automated imaging or sensing tasks. Here we give a short introduction to the basic features of flatbed scanners also highlighting the key parameters for designing scientific experiments using these devices, followed by a discussion of some of the significant examples, where scanner-based systems were constructed to conduct various biomedical imaging and/or sensing experiments. Along with mobile phones and other emerging consumer electronics devices, flatbed scanners and their use in advanced imaging and sensing experiments might help us transform current practices of medicine, engineering and sciences through democratization of measurement science and empowerment of citizen scientists, science educators and researchers in resource limited settings. PMID:24965011

Finding and Accessing Diagrams in Biomedical Publications

PubMed Central

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar and line charts by the shape and relative location of the different image elements that make up the charts. With recall and precisions of close to 90% for the detection of relevant figures, we discuss the use of this technology in an existing biomedical image search engine, and outline how it enables new forms of literature queries over biomedical relationships that are represented in these charts. PMID:23304318

Biomedical signal and image processing.

PubMed

Cerutti, Sergio; Baselli, Giuseppe; Bianchi, Anna; Caiani, Enrico; Contini, Davide; Cubeddu, Rinaldo; Dercole, Fabio; Rienzo, Luca; Liberati, Diego; Mainardi, Luca; Ravazzani, Paolo; Rinaldi, Sergio; Signorini, Maria; Torricelli, Alessandro

2011-01-01

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.

Biomedical Applications of Carbon Nanotubes: A Critical Review.

PubMed

Sharma, Priyanka; Mehra, Neelesh Kumar; Jain, Keerti; Jain, N K

2016-08-01

The convergence of nano and biotechnology is enabling scientific and technical knowledge for improving human well being. Carbon nanotubes have become most fascinating material to be studied and unveil new avenues in the field of nanobiotechnology. The nanometer size and high aspect ratio of the CNTs are the two distinct features, which have contributed to diverse biomedical applications. They have captured the attention as nanoscale materials due to their nanometric structure and remarkable list of superlative and extravagant properties that encouraged their exploitation for promising applications. Significant progress has been made in order to overcome some of the major hurdles towards biomedical application of nanomaterials, especially on issues regarding the aqueous solubility/dispersion and safety of CNTs. Functionalized CNTs have been used in drug targeting, imaging, and in the efficient delivery of gene and nucleic acids. CNTs have also demonstrated great potential in diverse biomedical uses like drug targeting, imaging, cancer treatment, tissue regeneration, diagnostics, biosensing, genetic engineering and so forth. The present review highlights the possible potential of CNTs in diagnostics, imaging and targeted delivery of bioactives and also outlines the future opportunities for biomedical applications.

Molecular Imaging in Synthetic Biology, and Synthetic Biology in Molecular Imaging.

PubMed

Gilad, Assaf A; Shapiro, Mikhail G

2017-06-01

Biomedical synthetic biology is an emerging field in which cells are engineered at the genetic level to carry out novel functions with relevance to biomedical and industrial applications. This approach promises new treatments, imaging tools, and diagnostics for diseases ranging from gastrointestinal inflammatory syndromes to cancer, diabetes, and neurodegeneration. As these cellular technologies undergo pre-clinical and clinical development, it is becoming essential to monitor their location and function in vivo, necessitating appropriate molecular imaging strategies, and therefore, we have created an interest group within the World Molecular Imaging Society focusing on synthetic biology and reporter gene technologies. Here, we highlight recent advances in biomedical synthetic biology, including bacterial therapy, immunotherapy, and regenerative medicine. We then discuss emerging molecular imaging approaches to facilitate in vivo applications, focusing on reporter genes for noninvasive modalities such as magnetic resonance, ultrasound, photoacoustic imaging, bioluminescence, and radionuclear imaging. Because reporter genes can be incorporated directly into engineered genetic circuits, they are particularly well suited to imaging synthetic biological constructs, and developing them provides opportunities for creative molecular and genetic engineering.

Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip.

PubMed

Heo, Young Jin; Lee, Donghyeon; Kang, Junsu; Lee, Keondo; Chung, Wan Kyun

2017-09-14

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.

A game-based platform for crowd-sourcing biomedical image diagnosis and standardized remote training and education of diagnosticians

NASA Astrophysics Data System (ADS)

Feng, Steve; Woo, Minjae; Chandramouli, Krithika; Ozcan, Aydogan

2015-03-01

Over the past decade, crowd-sourcing complex image analysis tasks to a human crowd has emerged as an alternative to energy-inefficient and difficult-to-implement computational approaches. Following this trend, we have developed a mathematical framework for statistically combining human crowd-sourcing of biomedical image analysis and diagnosis through games. Using a web-based smart game (BioGames), we demonstrated this platform's effectiveness for telediagnosis of malaria from microscopic images of individual red blood cells (RBCs). After public release in early 2012 (http://biogames.ee.ucla.edu), more than 3000 gamers (experts and non-experts) used this BioGames platform to diagnose over 2800 distinct RBC images, marking them as positive (infected) or negative (non-infected). Furthermore, we asked expert diagnosticians to tag the same set of cells with labels of positive, negative, or questionable (insufficient information for a reliable diagnosis) and statistically combined their decisions to generate a gold standard malaria image library. Our framework utilized minimally trained gamers' diagnoses to generate a set of statistical labels with an accuracy that is within 98% of our gold standard image library, demonstrating the "wisdom of the crowd". Using the same image library, we have recently launched a web-based malaria training and educational game allowing diagnosticians to compare their performance with their peers. After diagnosing a set of ~500 cells per game, diagnosticians can compare their quantified scores against a leaderboard and view their misdiagnosed cells. Using this platform, we aim to expand our gold standard library with new RBC images and provide a quantified digital tool for measuring and improving diagnostician training globally.

Annotating image ROIs with text descriptions for multimodal biomedical document retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Regions of interest (ROIs) that are pointed to by overlaid markers (arrows, asterisks, etc.) in biomedical images are expected to contain more important and relevant information than other regions for biomedical article indexing and retrieval. We have developed several algorithms that localize and extract the ROIs by recognizing markers on images. Cropped ROIs then need to be annotated with contents describing them best. In most cases accurate textual descriptions of the ROIs can be found from figure captions, and these need to be combined with image ROIs for annotation. The annotated ROIs can then be used to, for example, train classifiers that separate ROIs into known categories (medical concepts), or to build visual ontologies, for indexing and retrieval of biomedical articles. We propose an algorithm that pairs visual and textual ROIs that are extracted from images and figure captions, respectively. This algorithm based on dynamic time warping (DTW) clusters recognized pointers into groups, each of which contains pointers with identical visual properties (shape, size, color, etc.). Then a rule-based matching algorithm finds the best matching group for each textual ROI mention. Our method yields a precision and recall of 96% and 79%, respectively, when ground truth textual ROI data is used.

Microbubble Compositions, Properties and Biomedical Applications

PubMed Central

Sirsi, Shashank

2010-01-01

Over the last decade, there has been significant progress towards the development of microbubbles as theranostics for a wide variety of biomedical applications. The unique ability of microbubbles to respond to ultrasound makes them useful agents for contrast ultrasound imaging, molecular imaging, and targeted drug and gene delivery. The general composition of a microbubble is a gas core stabilized by a shell comprised of proteins, lipids or polymers. Each type of microbubble has its own unique advantages and can be tailored for specialized functions. In this review, different microbubbles compositions and physiochemical properties are discussed in the context of current progress towards developing novel constructs for biomedical applications, with specific emphasis on molecular imaging and targeted drug/gene delivery. PMID:20574549

Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

NASA Astrophysics Data System (ADS)

Alvarenga de Moura Meneses, Anderson; Giusti, Alessandro; de Almeida, André Pereira; Parreira Nogueira, Liebert; Braz, Delson; Cely Barroso, Regina; deAlmeida, Carlos Eduardo

2011-12-01

Synchrotron Radiation (SR) X-ray micro-Computed Tomography (μCT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-μCT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-μCT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-μCT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

To boldly glow ... applications of laser scanning confocal microscopy in developmental biology.

PubMed

Paddock, S W

1994-05-01

The laser scanning confocal microscope (LSCM) is now established as an invaluable tool in developmental biology for improved light microscope imaging of fluorescently labelled eggs, embryos and developing tissues. The universal application of the LSCM in biomedical research has stimulated improvements to the microscopes themselves and the synthesis of novel probes for imaging biological structures and physiological processes. Moreover the ability of the LSCM to produce an optical series in perfect register has made computer 3-D reconstruction and analysis of light microscope images a practical option.

75 FR 35820 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-23

... Imaging and Bioengineering Special Emphasis Panel Decision Support Systems and Comparative Effectiveness Research (ARRA). Date: July 23, 2010. Time: 12 p.m. to 5 p.m. Agenda: To review and evaluate grant... Assistance Program Nos. 93.701, ARRA Related Biomedical Research and Research Support Awards, National...

Development of image mappers for hyperspectral biomedical imaging applications

PubMed Central

Kester, Robert T.; Gao, Liang; Tkaczyk, Tomasz S.

2010-01-01

A new design and fabrication method is presented for creating large-format (>100 mirror facets) image mappers for a snapshot hyperspectral biomedical imaging system called an image mapping spectrometer (IMS). To verify this approach a 250 facet image mapper with 25 multiple-tilt angles is designed for a compact IMS that groups the 25 subpupils in a 5 × 5 matrix residing within a single collecting objective's pupil. The image mapper is fabricated by precision diamond raster fly cutting using surface-shaped tools. The individual mirror facets have minimal edge eating, tilt errors of <1 mrad, and an average roughness of 5.4 nm. PMID:20357875

Measurements and analysis in imaging for biomedical applications

NASA Astrophysics Data System (ADS)

Hoeller, Timothy L.

2009-02-01

A Total Quality Management (TQM) approach can be used to analyze data from biomedical optical and imaging platforms of tissues. A shift from individuals to teams, partnerships, and total participation are necessary from health care groups for improved prognostics using measurement analysis. Proprietary measurement analysis software is available for calibrated, pixel-to-pixel measurements of angles and distances in digital images. Feature size, count, and color are determinable on an absolute and comparative basis. Although changes in images of histomics are based on complex and numerous factors, the variation of changes in imaging analysis to correlations of time, extent, and progression of illness can be derived. Statistical methods are preferred. Applications of the proprietary measurement software are available for any imaging platform. Quantification of results provides improved categorization of illness towards better health. As health care practitioners try to use quantified measurement data for patient diagnosis, the techniques reported can be used to track and isolate causes better. Comparisons, norms, and trends are available from processing of measurement data which is obtained easily and quickly from Scientific Software and methods. Example results for the class actions of Preventative and Corrective Care in Ophthalmology and Dermatology, respectively, are provided. Improved and quantified diagnosis can lead to better health and lower costs associated with health care. Systems support improvements towards Lean and Six Sigma affecting all branches of biology and medicine. As an example for use of statistics, the major types of variation involving a study of Bone Mineral Density (BMD) are examined. Typically, special causes in medicine relate to illness and activities; whereas, common causes are known to be associated with gender, race, size, and genetic make-up. Such a strategy of Continuous Process Improvement (CPI) involves comparison of patient results to baseline data using F-statistics. Self-parings over time are also useful. Special and common causes are identified apart from aging in applying the statistical methods. In the future, implementation of imaging measurement methods by research staff, doctors, and concerned patient partners result in improved health diagnosis, reporting, and cause determination. The long-term prospects for quantified measurements are better quality in imaging analysis with applications of higher utility for heath care providers.

Liquid Crystals, PIV and IR-Photography in Selected Technical and Biomedical Applications

NASA Astrophysics Data System (ADS)

Stasiek, Jan; Jewartowski, Marcin

2017-10-01

Thermochromic liquid crystals (TLC), Particle Image Velocimetry (PIV), Infrared Imaging Themography (IR) and True-Colour Digital Image Processing (TDIP) have been successfully used in non-intrusive technical, industrial and biomedical studies and applications. These four tools (based on the desktop computers) have come together during the past two decades to produce a powerful advanced experimental technique as a judgment of quality of information that cannot be obtained from any other imaging procedure. The brief summary of the history of this technique is reviewed, principal methods and tools are described and some examples are presented. With this objective, a new experimental technique have been developed and applied to the study of heat and mass transfer and for biomedical diagnosis. Automated evaluation allows determining the heat and flow visualisation and locate the area of suspicious tissue of human body.

Ultra-Wideband Sensors for Improved Magnetic Resonance Imaging, Cardiovascular Monitoring and Tumour Diagnostics

PubMed Central

Thiel, Florian; Kosch, Olaf; Seifert, Frank

2010-01-01

The specific advantages of ultra-wideband electromagnetic remote sensing (UWB radar) make it a particularly attractive technique for biomedical applications. We partially review our activities in utilizing this novel approach for the benefit of high and ultra-high field magnetic resonance imaging (MRI) and other applications, e.g., for intensive care medicine and biomedical research. We could show that our approach is beneficial for applications like motion tracking for high resolution brain imaging due to the non-contact acquisition of involuntary head motions with high spatial resolution, navigation for cardiac MRI due to our interpretation of the detected physiological mechanical contraction of the heart muscle and for MR safety, since we have investigated the influence of high static magnetic fields on myocardial mechanics. From our findings we could conclude, that UWB radar can serve as a navigator technique for high and ultra-high field magnetic resonance imaging and can be beneficial preserving the high resolution capability of this imaging modality. Furthermore it can potentially be used to support standard ECG analysis by complementary information where sole ECG analysis fails. Further analytical investigations have proven the feasibility of this method for intracranial displacements detection and the rendition of a tumour’s contrast agent based perfusion dynamic. Beside these analytical approaches we have carried out FDTD simulations of a complex arrangement mimicking the illumination of a human torso model incorporating the geometry of the antennas applied. PMID:22163498

Plasmonic nanoparticle-generated photothermal bubbles and their biomedical applications

PubMed Central

Lapotko, Dmitri

2009-01-01

This article is focused on the optical generation and detection of photothermal vapor bubbles around plasmonic nanoparticles. We report physical properties of such plasmonic nanobubbles and their biomedical applications as cellular probes. Our experimental studies of gold nanoparticle-generated photothermal bubbles demonstrated the selectivity of photothermal bubble generation, amplification of optical scattering and thermal insulation effect, all realized at the nanoscale. The generation and imaging of photothermal bubbles in living cells (leukemia and carcinoma culture and primary cancerous cells), and tissues (atherosclerotic plaque and solid tumor in animal) demonstrated a noninvasive highly sensitive imaging of target cells by small photothermal bubbles and a selective mechanical, nonthermal damage to the individual target cells by bigger photothermal bubbles due to a rapid disruption of cellular membranes. The analysis of the plasmonic nanobubbles suggests them as theranostic probes, which can be tuned and optically guided at cell level from diagnosis to delivery and therapy during one fast process. PMID:19839816

76 FR 66735 - Announcement of Requirements and Registration for the NIBIB DEsign by Biomedical Undergraduate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-27

...., implants, biomaterials, surgical tools, tissue engineering, drug and gene delivery; (c) Technology to Aid... health information, and other programs with respect to biomedical imaging and engineering and associated... ceremony: October 2012, Biomedical Engineering Society Conference (exact date to be determined). FOR...

Subcellular object quantification with Squassh3C and SquasshAnalyst.

PubMed

Rizk, Aurélien; Mansouri, Maysam; Ballmer-Hofer, Kurt; Berger, Philipp

2015-11-01

Quantitative image analysis plays an important role in contemporary biomedical research. Squassh is a method for automatic detection, segmentation, and quantification of subcellular structures and analysis of their colocalization. Here we present the applications Squassh3C and SquasshAnalyst. Squassh3C extends the functionality of Squassh to three fluorescence channels and live-cell movie analysis. SquasshAnalyst is an interactive web interface for the analysis of Squassh3C object data. It provides segmentation image overview and data exploration, figure generation, object and image filtering, and a statistical significance test in an easy-to-use interface. The overall procedure combines the Squassh3C plug-in for the free biological image processing program ImageJ and a web application working in conjunction with the free statistical environment R, and it is compatible with Linux, MacOS X, or Microsoft Windows. Squassh3C and SquasshAnalyst are available for download at www.psi.ch/lbr/SquasshAnalystEN/SquasshAnalyst.zip.

Extraction and labeling high-resolution images from PDF documents

NASA Astrophysics Data System (ADS)

Chachra, Suchet K.; Xue, Zhiyun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Accuracy of content-based image retrieval is affected by image resolution among other factors. Higher resolution images enable extraction of image features that more accurately represent the image content. In order to improve the relevance of search results for our biomedical image search engine, Open-I, we have developed techniques to extract and label high-resolution versions of figures from biomedical articles supplied in the PDF format. Open-I uses the open-access subset of biomedical articles from the PubMed Central repository hosted by the National Library of Medicine. Articles are available in XML and in publisher supplied PDF formats. As these PDF documents contain little or no meta-data to identify the embedded images, the task includes labeling images according to their figure number in the article after they have been successfully extracted. For this purpose we use the labeled small size images provided with the XML web version of the article. This paper describes the image extraction process and two alternative approaches to perform image labeling that measure the similarity between two images based upon the image intensity projection on the coordinate axes and similarity based upon the normalized cross-correlation between the intensities of two images. Using image identification based on image intensity projection, we were able to achieve a precision of 92.84% and a recall of 82.18% in labeling of the extracted images.

Endovascular Device Testing with Particle Image Velocimetry Enhances Undergraduate Biomedical Engineering Education

ERIC Educational Resources Information Center

Nair, Priya; Ankeny, Casey J.; Ryan, Justin; Okcay, Murat; Frakes, David H.

2016-01-01

We investigated the use of a new system, HemoFlow™, which utilizes state of the art technologies such as particle image velocimetry to test endovascular devices as part of an undergraduate biomedical engineering curriculum. Students deployed an endovascular stent into an anatomical model of a cerebral aneurysm and measured intra-aneurysmal flow…

Supporting Imagers' VOICE: A National Training Program in Comparative Effectiveness Research and Big Data Analytics.

PubMed

Kang, Stella K; Rawson, James V; Recht, Michael P

2017-12-05

Provided methodologic training, more imagers can contribute to the evidence basis on improved health outcomes and value in diagnostic imaging. The Value of Imaging Through Comparative Effectiveness Research Program was developed to provide hands-on, practical training in five core areas for comparative effectiveness and big biomedical data research: decision analysis, cost-effectiveness analysis, evidence synthesis, big data principles, and applications of big data analytics. The program's mixed format consists of web-based modules for asynchronous learning as well as in-person sessions for practical skills and group discussion. Seven diagnostic radiology subspecialties and cardiology are represented in the first group of program participants, showing the collective potential for greater depth of comparative effectiveness research in the imaging community. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Time of flight imaging through scattering environments (Conference Presentation)

NASA Astrophysics Data System (ADS)

Le, Toan H.; Breitbach, Eric C.; Jackson, Jonathan A.; Velten, Andreas

2017-02-01

Light scattering is a primary obstacle to imaging in many environments. On small scales in biomedical microscopy and diffuse tomography scenarios scattering is caused by tissue. On larger scales scattering from dust and fog provide challenges to vision systems for self driving cars and naval remote imaging systems. We are developing scale models for scattering environments and investigation methods for improved imaging particularly using time of flight transient information. With the emergence of Single Photon Avalanche Diode detectors and fast semiconductor lasers, illumination and capture on picosecond timescales are becoming possible in inexpensive, compact, and robust devices. This opens up opportunities for new computational imaging techniques that make use of photon time of flight. Time of flight or range information is used in remote imaging scenarios in gated viewing and in biomedical imaging in time resolved diffuse tomography. In addition spatial filtering is popular in biomedical scenarios with structured illumination and confocal microscopy. We are presenting a combination analytical, computational, and experimental models that allow us develop and test imaging methods across scattering scenarios and scales. This framework will be used for proof of concept experiments to evaluate new computational imaging methods.

Melanin-Based Contrast Agents for Biomedical Optoacoustic Imaging and Theranostic Applications.

PubMed

Longo, Dario Livio; Stefania, Rachele; Aime, Silvio; Oraevsky, Alexander

2017-08-07

Optoacoustic imaging emerged in early 1990s as a new biomedical imaging technology that generates images by illuminating tissues with short laser pulses and detecting resulting ultrasound waves. This technique takes advantage of the spectroscopic approach to molecular imaging, and delivers high-resolution images in the depth of tissue. Resolution of the optoacoustic imaging is scalable, so that biomedical systems from cellular organelles to large organs can be visualized and, more importantly, characterized based on their optical absorption coefficient, which is proportional to the concentration of absorbing chromophores. Optoacoustic imaging was shown to be useful in both preclinical research using small animal models and in clinical applications. Applications in the field of molecular imaging offer abundant opportunities for the development of highly specific and effective contrast agents for quantitative optoacoustic imaging. Recent efforts are being made in the direction of nontoxic biodegradable contrast agents (such as nanoparticles made of melanin) that are potentially applicable in clinical optoacoustic imaging. In order to increase the efficiency and specificity of contrast agents and probes, they need to be made smart and capable of controlled accumulation in the target cells. This review was written in recognition of the potential breakthroughs in medical optoacoustic imaging that can be enabled by efficient and nontoxic melanin-based optoacoustic contrast agents.

Melanin-Based Contrast Agents for Biomedical Optoacoustic Imaging and Theranostic Applications

PubMed Central

Longo, Dario Livio; Aime, Silvio

2017-01-01

Optoacoustic imaging emerged in early 1990s as a new biomedical imaging technology that generates images by illuminating tissues with short laser pulses and detecting resulting ultrasound waves. This technique takes advantage of the spectroscopic approach to molecular imaging, and delivers high-resolution images in the depth of tissue. Resolution of the optoacoustic imaging is scalable, so that biomedical systems from cellular organelles to large organs can be visualized and, more importantly, characterized based on their optical absorption coefficient, which is proportional to the concentration of absorbing chromophores. Optoacoustic imaging was shown to be useful in both preclinical research using small animal models and in clinical applications. Applications in the field of molecular imaging offer abundant opportunities for the development of highly specific and effective contrast agents for quantitative optoacoustic imaging. Recent efforts are being made in the direction of nontoxic biodegradable contrast agents (such as nanoparticles made of melanin) that are potentially applicable in clinical optoacoustic imaging. In order to increase the efficiency and specificity of contrast agents and probes, they need to be made smart and capable of controlled accumulation in the target cells. This review was written in recognition of the potential breakthroughs in medical optoacoustic imaging that can be enabled by efficient and nontoxic melanin-based optoacoustic contrast agents. PMID:28783106

Using image mapping towards biomedical and biological data sharing

PubMed Central

2013-01-01

Image-based data integration in eHealth and life sciences is typically concerned with the method used for anatomical space mapping, needed to retrieve, compare and analyse large volumes of biomedical data. In mapping one image onto another image, a mechanism is used to match and find the corresponding spatial regions which have the same meaning between the source and the matching image. Image-based data integration is useful for integrating data of various information structures. Here we discuss a broad range of issues related to data integration of various information structures, review exemplary work on image representation and mapping, and discuss the challenges that these techniques may bring. PMID:24059352

Recent Development of Inorganic Nanoparticles for Biomedical Imaging

PubMed Central

2018-01-01

Inorganic nanoparticle-based biomedical imaging probes have been studied extensively as a potential alternative to conventional molecular imaging probes. Not only can they provide better imaging performance but they can also offer greater versatility of multimodal, stimuli-responsive, and targeted imaging. However, inorganic nanoparticle-based probes are still far from practical use in clinics due to safety concerns and less-optimized efficiency. In this context, it would be valuable to look over the underlying issues. This outlook highlights the recent advances in the development of inorganic nanoparticle-based probes for MRI, CT, and anti-Stokes shift-based optical imaging. Various issues and possibilities regarding the construction of imaging probes are discussed, and future research directions are suggested. PMID:29632878

The Function Biomedical Informatics Research Network Data Repository

PubMed Central

Keator, David B.; van Erp, Theo G.M.; Turner, Jessica A.; Glover, Gary H.; Mueller, Bryon A.; Liu, Thomas T.; Voyvodic, James T.; Rasmussen, Jerod; Calhoun, Vince D.; Lee, Hyo Jong; Toga, Arthur W.; McEwen, Sarah; Ford, Judith M.; Mathalon, Daniel H.; Diaz, Michele; O’Leary, Daniel S.; Bockholt, H. Jeremy; Gadde, Syam; Preda, Adrian; Wible, Cynthia G.; Stern, Hal S.; Belger, Aysenil; McCarthy, Gregory; Ozyurt, Burak; Potkin, Steven G.

2015-01-01

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN’s multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data. PMID:26364863

New frontiers in biomedical science and engineering during 2014-2015.

PubMed

Liu, Feng; Lee, Dong-Hoon; Lagoa, Ricardo; Kumar, Sandeep

2015-01-01

The International Conference on Biomedical Engineering and Biotechnology (ICBEB) is an international meeting held once a year. This, the fourth International Conference on Biomedical Engineering and Biotechnology (ICBEB2015), will be held in Shanghai, China, during August 18th-21st, 2015. This annual conference intends to provide an opportunity for researchers and practitioners at home and abroad to present the most recent frontiers and future challenges in the fields of biomedical science, biomedical engineering, biomaterials, bioinformatics and computational biology, biomedical imaging and signal processing, biomechanical engineering and biotechnology, etc. The papers published in this issue are selected from this Conference, which witness the advances in biomedical engineering and biotechnology during 2014-2015.

Magnetic particle imaging for radiation-free, sensitive and high-contrast vascular imaging and cell tracking.

PubMed

Zhou, Xinyi Y; Tay, Zhi Wei; Chandrasekharan, Prashant; Yu, Elaine Y; Hensley, Daniel W; Orendorff, Ryan; Jeffris, Kenneth E; Mai, David; Zheng, Bo; Goodwill, Patrick W; Conolly, Steven M

2018-05-10

Magnetic particle imaging (MPI) is an emerging ionizing radiation-free biomedical tracer imaging technique that directly images the intense magnetization of superparamagnetic iron oxide nanoparticles (SPIOs). MPI offers ideal image contrast because MPI shows zero signal from background tissues. Moreover, there is zero attenuation of the signal with depth in tissue, allowing for imaging deep inside the body quantitatively at any location. Recent work has demonstrated the potential of MPI for robust, sensitive vascular imaging and cell tracking with high contrast and dose-limited sensitivity comparable to nuclear medicine. To foster future applications in MPI, this new biomedical imaging field is welcoming researchers with expertise in imaging physics, magnetic nanoparticle synthesis and functionalization, nanoscale physics, and small animal imaging applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Terahertz Sensing of Materials

NASA Astrophysics Data System (ADS)

Xuan, G.; Ghosh, S.; Kim, S.; Lv, P.-C.; Buma, T.; Weng, B.; Barner, K.; Kolodzey, J.

2007-06-01

Biomolecules such as DNA and proteins exhibit a wealth of modes in the Terahertz (THz) range from the rotational, vibrational and stretching modes of biomolecules. Many materials such as drywall that are opaque to human eyes are transparent to THz. Therefore, it can be used as a powerful tool for biomolecular sensing, biomedical analysis and through-the-wall imaging. Experiments were carried out to study the absorption of various materials including DNA and see-through imaging of drywall using FTIR spectrometer and Time Domain Spectroscopy (TDS) system.

Biomedical Imaging

DTIC Science & Technology

1994-04-01

distribution unlimited. United States Army Aeromedical Research Laboratory Fort Rucker, Alabama 36362-0577 Qualified recuesters Qualified requesters may...FUNDING NUMBER5 I PROGRAM zfJECT TASK WORK UNIT ELEMENT NO. NO. ACCESSION NO. 62787A 30162787A87$ EA 138 Biomedical Imaging 12. PERSONAL AUTHOR(S...times larger. Usually they are expensive with commercially available units starting at around $100,000. Triangulation sensors are capable of range

Biomedical applications of a real-time terahertz color scanner

PubMed Central

Schirmer, Markus; Fujio, Makoto; Minami, Masaaki; Miura, Jiro; Araki, Tsutomu; Yasui, Takeshi

2010-01-01

A real-time THz color scanner has the potential to further expand the application scope of THz spectral imaging based on its rapid image acquisition rate. We demonstrated three possible applications of a THz color scanner in the biomedical field: imaging of pharmaceutical tablets, human teeth, and human hair. The first application showed the scanner’s potential in total inspection for rapid quality control of pharmaceutical tablets moving on a conveyor belt. The second application demonstrated that the scanner can be used to identify a potential indicator for crystallinity of dental tissue. In the third application, the scanner was successfully used to visualize the drying process of wet hairs. These demonstrations indicated the high potential of the THz color scanner for practical applications in the biomedical field. PMID:21258472

An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

PubMed

Liu, Fei; Zhang, Xi; Jia, Yan

2015-01-01

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

Low-Cost, High-Throughput 3D Pulmonary Imager Using Hyperpolarized Contrast Agents and Low-Field MRI

DTIC Science & Technology

2016-10-01

COMMUNITIES OF INTEREST? ................................................. 8 4. IMPACT...publicize the work performed and also for their exposure to biomedical science. How were the results disseminated to communities of interest? Nothing...biomedical community , expanding the utility of HP methods as a new tool for probing fundamental biomedical questions. Acknowledgments The authors thank

A high-level 3D visualization API for Java and ImageJ.

PubMed

Schmid, Benjamin; Schindelin, Johannes; Cardona, Albert; Longair, Mark; Heisenberg, Martin

2010-05-21

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

PubMed Central

Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

2013-01-01

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

Signal and image processing for early detection of coronary artery diseases: A review

NASA Astrophysics Data System (ADS)

Mobssite, Youness; Samir, B. Belhaouari; Mohamad Hani, Ahmed Fadzil B.

2012-09-01

Today biomedical signals and image based detection are a basic step to diagnose heart diseases, in particular, coronary artery diseases. The goal of this work is to provide non-invasive early detection of Coronary Artery Diseases relying on analyzing images and ECG signals as a combined approach to extract features, further classify and quantify the severity of DCAD by using B-splines method. In an aim of creating a prototype of screening biomedical imaging for coronary arteries to help cardiologists to decide the kind of treatment needed to reduce or control the risk of heart attack.

Rapid development of medical imaging tools with open-source libraries.

PubMed

Caban, Jesus J; Joshi, Alark; Nagy, Paul

2007-11-01

Rapid prototyping is an important element in researching new imaging analysis techniques and developing custom medical applications. In the last ten years, the open source community and the number of open source libraries and freely available frameworks for biomedical research have grown significantly. What they offer are now considered standards in medical image analysis, computer-aided diagnosis, and medical visualization. A cursory review of the peer-reviewed literature in imaging informatics (indeed, in almost any information technology-dependent scientific discipline) indicates the current reliance on open source libraries to accelerate development and validation of processes and techniques. In this survey paper, we review and compare a few of the most successful open source libraries and frameworks for medical application development. Our dual intentions are to provide evidence that these approaches already constitute a vital and essential part of medical image analysis, diagnosis, and visualization and to motivate the reader to use open source libraries and software for rapid prototyping of medical applications and tools.

Recent advances in terahertz technology for biomedical applications.

PubMed

Sun, Qiushuo; He, Yuezhi; Liu, Kai; Fan, Shuting; Parrott, Edward P J; Pickwell-MacPherson, Emma

2017-06-01

Terahertz instrumentation has improved significantly in recent years such that THz imaging systems have become more affordable and easier to use. THz systems can now be operated by non-THz experts greatly facilitating research into many potential applications. Due to the non-ionising nature of THz light and its high sensitivity to soft tissues, there is an increasing interest in biomedical applications including both in vivo and ex vivo studies. Additionally, research continues into understanding the origin of contrast and how to interpret terahertz biomedical images. This short review highlights some of the recent work in these areas and suggests some future research directions.

A portable low-cost long-term live-cell imaging platform for biomedical research and education.

PubMed

Walzik, Maria P; Vollmar, Verena; Lachnit, Theresa; Dietz, Helmut; Haug, Susanne; Bachmann, Holger; Fath, Moritz; Aschenbrenner, Daniel; Abolpour Mofrad, Sepideh; Friedrich, Oliver; Gilbert, Daniel F

2015-02-15

Time-resolved visualization and analysis of slow dynamic processes in living cells has revolutionized many aspects of in vitro cellular studies. However, existing technology applied to time-resolved live-cell microscopy is often immobile, costly and requires a high level of skill to use and maintain. These factors limit its utility to field research and educational purposes. The recent availability of rapid prototyping technology makes it possible to quickly and easily engineer purpose-built alternatives to conventional research infrastructure which are low-cost and user-friendly. In this paper we describe the prototype of a fully automated low-cost, portable live-cell imaging system for time-resolved label-free visualization of dynamic processes in living cells. The device is light-weight (3.6 kg), small (22 × 22 × 22 cm) and extremely low-cost (<€1250). We demonstrate its potential for biomedical use by long-term imaging of recombinant HEK293 cells at varying culture conditions and validate its ability to generate time-resolved data of high quality allowing for analysis of time-dependent processes in living cells. While this work focuses on long-term imaging of mammalian cells, the presented technology could also be adapted for use with other biological specimen and provides a general example of rapidly prototyped low-cost biosensor technology for application in life sciences and education. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Collaborative Initiative in Biomedical Imaging to Study Complex Diseases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Weili; Fiddy, Michael A.

2012-03-31

The work reported addressed these topics: Fluorescence imaging; Optical coherence tomography; X-ray interferometer/phase imaging system; Quantitative imaging from scattered fields, Terahertz imaging and spectroscopy; and Multiphoton and Raman microscopy.

Advances in biomedical engineering and biotechnology during 2013-2014.

PubMed

Liu, Feng; Wang, Ying; Burkhart, Timothy A; González Penedo, Manuel Francisco; Ma, Shaodong

2014-01-01

The 3rd International Conference on Biomedical Engineering and Biotechnology (iCBEB 2014), held in Beijing from the 25th to the 28th of September 2014, is an annual conference that intends to provide an opportunity for researchers and practitioners around the world to present the most recent advances and future challenges in the fields of biomedical engineering, biomaterials, bioinformatics and computational biology, biomedical imaging and signal processing, biomechanical engineering and biotechnology, amongst others. The papers published in this issue are selected from this conference, which witnesses the advances in biomedical engineering and biotechnology during 2013-2014.

The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research

PubMed Central

Goscinski, Wojtek J.; McIntosh, Paul; Felzmann, Ulrich; Maksimenko, Anton; Hall, Christopher J.; Gureyev, Timur; Thompson, Darren; Janke, Andrew; Galloway, Graham; Killeen, Neil E. B.; Raniga, Parnesh; Kaluza, Owen; Ng, Amanda; Poudel, Govinda; Barnes, David G.; Nguyen, Toan; Bonnington, Paul; Egan, Gary F.

2014-01-01

The Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) is a national imaging and visualization facility established by Monash University, the Australian Synchrotron, the Commonwealth Scientific Industrial Research Organization (CSIRO), and the Victorian Partnership for Advanced Computing (VPAC), with funding from the National Computational Infrastructure and the Victorian Government. The MASSIVE facility provides hardware, software, and expertise to drive research in the biomedical sciences, particularly advanced brain imaging research using synchrotron x-ray and infrared imaging, functional and structural magnetic resonance imaging (MRI), x-ray computer tomography (CT), electron microscopy and optical microscopy. The development of MASSIVE has been based on best practice in system integration methodologies, frameworks, and architectures. The facility has: (i) integrated multiple different neuroimaging analysis software components, (ii) enabled cross-platform and cross-modality integration of neuroinformatics tools, and (iii) brought together neuroimaging databases and analysis workflows. MASSIVE is now operational as a nationally distributed and integrated facility for neuroinfomatics and brain imaging research. PMID:24734019

Reconstruction of color biomedical images by means of quaternion generic Jacobi-Fourier moments in the framework of polar pixels

PubMed Central

Camacho-Bello, César; Padilla-Vivanco, Alfonso; Toxqui-Quitl, Carina; Báez-Rojas, José Javier

2016-01-01

Abstract. A detailed analysis of the quaternion generic Jacobi-Fourier moments (QGJFMs) for color image description is presented. In order to reach numerical stability, a recursive approach is used during the computation of the generic Jacobi radial polynomials. Moreover, a search criterion is performed to establish the best values for the parameters α and β of the radial Jacobi polynomial families. Additionally, a polar pixel approach is taken into account to increase the numerical accuracy in the calculation of the QGJFMs. To prove the mathematical theory, some color images from optical microscopy and human retina are used. Experiments and results about color image reconstruction are presented. PMID:27014716

Computational optical palpation: a finite-element approach to micro-scale tactile imaging using a compliant sensor

PubMed Central

Sampson, David D.; Kennedy, Brendan F.

2017-01-01

High-resolution tactile imaging, superior to the sense of touch, has potential for future biomedical applications such as robotic surgery. In this paper, we propose a tactile imaging method, termed computational optical palpation, based on measuring the change in thickness of a thin, compliant layer with optical coherence tomography and calculating tactile stress using finite-element analysis. We demonstrate our method on test targets and on freshly excised human breast fibroadenoma, demonstrating a resolution of up to 15–25 µm and a field of view of up to 7 mm. Our method is open source and readily adaptable to other imaging modalities, such as ultrasonography and confocal microscopy. PMID:28250098

Novel snapshot hyperspectral imager for fluorescence imaging

NASA Astrophysics Data System (ADS)

Chandler, Lynn; Chandler, Andrea; Periasamy, Ammasi

2018-02-01

Hyperspectral imaging has emerged as a new technique for the identification and classification of biological tissue1. Benefitting recent developments in sensor technology, the new class of hyperspectral imagers can capture entire hypercubes with single shot operation and it shows great potential for real-time imaging in biomedical sciences. This paper explores the use of a SnapShot imager in fluorescence imaging via microscope for the very first time. Utilizing the latest imaging sensor, the Snapshot imager is both compact and attachable via C-mount to any commercially available light microscope. Using this setup, fluorescence hypercubes of several cells were generated, containing both spatial and spectral information. The fluorescence images were acquired with one shot operation for all the emission range from visible to near infrared (VIS-IR). The paper will present the hypercubes obtained images from example tissues (475-630nm). This study demonstrates the potential of application in cell biology or biomedical applications for real time monitoring.

Performance of a novel wafer scale CMOS active pixel sensor for bio-medical imaging.

PubMed

Esposito, M; Anaxagoras, T; Konstantinidis, A C; Zheng, Y; Speller, R D; Evans, P M; Allinson, N M; Wells, K

2014-07-07

Recently CMOS active pixels sensors (APSs) have become a valuable alternative to amorphous silicon and selenium flat panel imagers (FPIs) in bio-medical imaging applications. CMOS APSs can now be scaled up to the standard 20 cm diameter wafer size by means of a reticle stitching block process. However, despite wafer scale CMOS APS being monolithic, sources of non-uniformity of response and regional variations can persist representing a significant challenge for wafer scale sensor response. Non-uniformity of stitched sensors can arise from a number of factors related to the manufacturing process, including variation of amplification, variation between readout components, wafer defects and process variations across the wafer due to manufacturing processes. This paper reports on an investigation into the spatial non-uniformity and regional variations of a wafer scale stitched CMOS APS. For the first time a per-pixel analysis of the electro-optical performance of a wafer CMOS APS is presented, to address inhomogeneity issues arising from the stitching techniques used to manufacture wafer scale sensors. A complete model of the signal generation in the pixel array has been provided and proved capable of accounting for noise and gain variations across the pixel array. This novel analysis leads to readout noise and conversion gain being evaluated at pixel level, stitching block level and in regions of interest, resulting in a coefficient of variation ⩽1.9%. The uniformity of the image quality performance has been further investigated in a typical x-ray application, i.e. mammography, showing a uniformity in terms of CNR among the highest when compared with mammography detectors commonly used in clinical practice. Finally, in order to compare the detection capability of this novel APS with the technology currently used (i.e. FPIs), theoretical evaluation of the detection quantum efficiency (DQE) at zero-frequency has been performed, resulting in a higher DQE for this detector compared to FPIs. Optical characterization, x-ray contrast measurements and theoretical DQE evaluation suggest that a trade off can be found between the need of a large imaging area and the requirement of a uniform imaging performance, making the DynAMITe large area CMOS APS suitable for a range of bio-medical applications.

CognitionMaster: an object-based image analysis framework

PubMed Central

2013-01-01

Background Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired. Results In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose “analysis pipelines“ that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept. Conclusions We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis. PMID:23445542

Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.

PubMed

Phan, John H; Hoffman, Ryan; Kothari, Sonal; Wu, Po-Yen; Wang, May D

2016-02-01

The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation of multi-modal data integration by combining histopathological image and RNA-seq data. We compared the performances of two integrative prediction methods: majority vote and stacked generalization. Results indicate that integration of multiple data modalities improves prediction of cancer grade and outcome. Specifically, stacked generalization, a method that integrates multiple data modalities to produce a single prediction result, outperforms both single-data-modality prediction and majority vote. Moreover, stacked generalization reveals the contribution of each data modality (and specific features within each data modality) to the final prediction result and may provide biological insights to explain prediction performance.

Image analysis and machine learning for detecting malaria.

PubMed

Poostchi, Mahdieh; Silamut, Kamolrat; Maude, Richard J; Jaeger, Stefan; Thoma, George

2018-04-01

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis. Published by Elsevier Inc.

Book Review: Reiner Salzer and Heinz W. Siesler (Eds.): Infrared and Raman spectroscopic imaging, 2nd ed.

DOE PAGES

Moore, David Steven

2015-05-10

This second edition of "Infrared and Raman Spectroscopic Imaging" propels practitioners in that wide-ranging field, as well as other readers, to the current state of the art in a well-produced and full-color, completely revised and updated, volume. This new edition chronicles the expanded application of vibrational spectroscopic imaging from yesterday's time-consuming point-by-point buildup of a hyperspectral image cube, through the improvements afforded by the addition of focal plane arrays and line scan imaging, to methods applicable beyond the diffraction limit, instructs the reader on the improved instrumentation and image and data analysis methods, and expounds on their application to fundamentalmore » biomedical knowledge, food and agricultural surveys, materials science, process and quality control, and many others.« less

Design of a normal incidence multilayer imaging X-ray microscope

NASA Astrophysics Data System (ADS)

Shealy, David L.; Gabardi, David R.; Hoover, Richard B.; Walker, Arthur B. C., Jr.; Lindblom, Joakim F.

Normal incidence multilayer Cassegrain X-ray telescopes were flown on the Stanford/MSFC Rocket X-ray Spectroheliograph. These instruments produced high spatial resolution images of the sun and conclusively demonstrated that doubly reflecting multilayer X-ray optical systems are feasible. The images indicated that aplanatic imaging soft X-ray/EUV microscopes should be achievable using multilayer optics technology. A doubly reflecting normal incidence multilayer imaging X-ray microscope based on the Schwarzschild configuration has been designed. The design of the microscope and the results of the optical system ray trace analysis are discussed. High resolution aplanatic imaging X-ray microscopes using normal incidence multilayer X-ray mirrors should have many important applications in advanced X-ray astronomical instrumentation, X-ray lithography, biological, biomedical, metallurgical, and laser fusion research.

Multiple energy synchrotron biomedical imaging system

NASA Astrophysics Data System (ADS)

Bassey, B.; Martinson, M.; Samadi, N.; Belev, G.; Karanfil, C.; Qi, P.; Chapman, D.

2016-12-01

A multiple energy imaging system that can extract multiple endogenous or induced contrast materials as well as water and bone images would be ideal for imaging of biological subjects. The continuous spectrum available from synchrotron light facilities provides a nearly perfect source for multiple energy x-ray imaging. A novel multiple energy x-ray imaging system, which prepares a horizontally focused polychromatic x-ray beam, has been developed at the BioMedical Imaging and Therapy bend magnet beamline at the Canadian Light Source. The imaging system is made up of a cylindrically bent Laue single silicon (5,1,1) crystal monochromator, scanning and positioning stages for the subjects, flat panel (area) detector, and a data acquisition and control system. Depending on the crystal’s bent radius, reflection type, and the horizontal beam width of the filtered synchrotron radiation (20-50 keV) used, the size and spectral energy range of the focused beam prepared varied. For example, with a bent radius of 95 cm, a (1,1,1) type reflection and a 50 mm wide beam, a 0.5 mm wide focused beam of spectral energy range 27 keV-43 keV was obtained. This spectral energy range covers the K-edges of iodine (33.17 keV), xenon (34.56 keV), cesium (35.99 keV), and barium (37.44 keV) some of these elements are used as biomedical and clinical contrast agents. Using the developed imaging system, a test subject composed of iodine, xenon, cesium, and barium along with water and bone were imaged and their projected concentrations successfully extracted. The estimated dose rate to test subjects imaged at a ring current of 200 mA is 8.7 mGy s-1, corresponding to a cumulative dose of 1.3 Gy and a dose of 26.1 mGy per image. Potential biomedical applications of the imaging system will include projection imaging that requires any of the extracted elements as a contrast agent and multi-contrast K-edge imaging.

Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond.

PubMed

Segars, W Paul; Tsui, B M W; Cai, Jing; Yin, Fang-Fang; Fung, George S K; Samei, Ehsan

2018-03-01

The four-dimensional (4-D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT's design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques, and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community.

Iodine-131 radiolabeling of poly ethylene glycol-coated gold nanorods for in vivo imaging.

PubMed

Eskandari, Najmeh; Yavari, Kamal; Outokesh, Mohammad; Sadjadi, Sodeh; Ahmadi, Seyed Javad

2013-01-01

Gold nanorods (GNRs) can be used in various biomedical applications; however, very little is known about their in vivo tissue distribution by radiolabeling. Here, we have developed a rapid and simple method with high yield and without disturbing their optical properties for radiolabeling of gold rods with iodine-131 in order to track in vivo tissue uptake of GNRs after systemic administration by biodistribution analysis and γ-imaging. Following intravenous injection into rat, PEGylated GNRs have much longer blood circulation times. Copyright © 2012 John Wiley & Sons, Ltd.

Stochastic HKMDHE: A multi-objective contrast enhancement algorithm

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Maity, Srideep; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

This contribution proposes a novel extension of the existing `Hyper Kurtosis based Modified Duo-Histogram Equalization' (HKMDHE) algorithm, for multi-objective contrast enhancement of biomedical images. A novel modified objective function has been formulated by joint optimization of the individual histogram equalization objectives. The optimal adequacy of the proposed methodology with respect to image quality metrics such as brightness preserving abilities, peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM) and universal image quality metric has been experimentally validated. The performance analysis of the proposed Stochastic HKMDHE with existing histogram equalization methodologies like Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) has been given for comparative evaluation.

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

PubMed

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

The Function Biomedical Informatics Research Network Data Repository.

PubMed

Keator, David B; van Erp, Theo G M; Turner, Jessica A; Glover, Gary H; Mueller, Bryon A; Liu, Thomas T; Voyvodic, James T; Rasmussen, Jerod; Calhoun, Vince D; Lee, Hyo Jong; Toga, Arthur W; McEwen, Sarah; Ford, Judith M; Mathalon, Daniel H; Diaz, Michele; O'Leary, Daniel S; Jeremy Bockholt, H; Gadde, Syam; Preda, Adrian; Wible, Cynthia G; Stern, Hal S; Belger, Aysenil; McCarthy, Gregory; Ozyurt, Burak; Potkin, Steven G

2016-01-01

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data. Copyright © 2015 Elsevier Inc. All rights reserved.

Combined photoacoustic and magneto-acoustic imaging.

PubMed

Qu, Min; Mallidi, Srivalleesha; Mehrmohammadi, Mohammad; Ma, Li Leo; Johnston, Keith P; Sokolov, Konstantin; Emelianov, Stanislav

2009-01-01

Ultrasound is a widely used modality with excellent spatial resolution, low cost, portability, reliability and safety. In clinical practice and in the biomedical field, molecular ultrasound-based imaging techniques are desired to visualize tissue pathologies, such as cancer. In this paper, we present an advanced imaging technique - combined photoacoustic and magneto-acoustic imaging - capable of visualizing the anatomical, functional and biomechanical properties of tissues or organs. The experiments to test the combined imaging technique were performed using dual, nanoparticle-based contrast agents that exhibit the desired optical and magnetic properties. The results of our study demonstrate the feasibility of the combined photoacoustic and magneto-acoustic imaging that takes the advantages of each imaging techniques and provides high sensitivity, reliable contrast and good penetrating depth. Therefore, the developed imaging technique can be used in wide range of biomedical and clinical application.

Histopathology mapping of biochemical changes in myocardial infarction by Fourier transform infrared spectral imaging.

PubMed

Yang, Tian T; Weng, Shi F; Zheng, Na; Pan, Qing H; Cao, Hong L; Liu, Liang; Zhang, Hai D; Mu, Da W

2011-04-15

Fourier transform infrared (FTIR) imaging and microspectroscopy have been extensively applied in the identification and investigation of both healthy and diseased tissues. FTIR imaging can be used to determine the biodistribution of several molecules of interest (carbohydrates, lipids, proteins) for tissue analysis, without the need for prior staining of these tissues. Molecular structure data, such as protein secondary structure and collagen triple helix exhibits, can also be obtained from the same analysis. Thus, several histopathological lesions, for example myocardial infarction, can be identified from FTIR-analyzed tissue images, the latter which can allow for more accurate discrimination between healthy tissues and pathological lesions. Accordingly, we propose FTIR imaging as a new tool integrating both molecular and histopathological assessment to investigate the degree of pathological changes in tissues. In this study, myocardial infarction is presented as an illustrative example of the wide potential of FTIR imaging for biomedical applications. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Distortion correction and cross-talk compensation algorithm for use with an imaging spectrometer based spatially resolved diffuse reflectance system

NASA Astrophysics Data System (ADS)

Cappon, Derek J.; Farrell, Thomas J.; Fang, Qiyin; Hayward, Joseph E.

2016-12-01

Optical spectroscopy of human tissue has been widely applied within the field of biomedical optics to allow rapid, in vivo characterization and analysis of the tissue. When designing an instrument of this type, an imaging spectrometer is often employed to allow for simultaneous analysis of distinct signals. This is especially important when performing spatially resolved diffuse reflectance spectroscopy. In this article, an algorithm is presented that allows for the automated processing of 2-dimensional images acquired from an imaging spectrometer. The algorithm automatically defines distinct spectrometer tracks and adaptively compensates for distortion introduced by optical components in the imaging chain. Crosstalk resulting from the overlap of adjacent spectrometer tracks in the image is detected and subtracted from each signal. The algorithm's performance is demonstrated in the processing of spatially resolved diffuse reflectance spectra recovered from an Intralipid and ink liquid phantom and is shown to increase the range of wavelengths over which usable data can be recovered.

DotLens smartphone microscopy for biological and biomedical applications (Conference Presentation)

NASA Astrophysics Data System (ADS)

Sung, Yu-Lung; Zhao, Fusheng; Shih, Wei-Chuan

2017-02-01

Recent advances in inkjet-printed optics have created a new class of lens fabrication technique. Lenses with a tunable geometry, magnification, and focal length can be fabricated by dispensing controlled amounts of liquid polymer onto a heated surface. This fabrication technique is highly cost-effective, and can achieve optically smooth surface finish. Dubbed DotLens, a single of which weighs less than 50 mg and occupies a volume less than 50 μL. DotLens can be attached onto any smartphone camera akin to a contact lens, and enable smartphones to obtain image resolution as fine as 1 µm. The surface curvature modifies the optical path of light to the image sensor, and enables the camera to focus as close as 2 mm. This enables microscopic imaging on a smartphone without any additional attachments, and has shown great potential in mobile point-of-care diagnostic systems, particularly for histology of tissue sections and cytology of blood cells. DotLens Smartphone Microscopy represents an innovative approach fundamentally different from other smartphone microscopes. In this paper, we describe the application and performance of DotLens smartphone microscopy in biological and biomedical research. In particular, we show recent results from images collected from pathology tissue slides with cancer features. In addition, we show performance in cytological analysis of blood smear. This tool has empowered Citizen Science investigators to collect microscopic images from various interesting objects.

Snapshot Mueller polarimetry for biomedical diagnostic related to human liver fibrosis: evaluation of the method for biomedical assessments

NASA Astrophysics Data System (ADS)

Babilotte, P.; Dubreuil, M.; Rivet, S.; Lijour, Y.; Sevrain, D.; Martin, L.; Le Brun, G.; Le Grand, Y.; Le Jeune, B.

2011-10-01

Human liver biopsy samples, consisting into a 16 μm thickness biomaterial chemically fixed into a formaldehyde matrix, and stained by red picrosirius dye, are analysed for different states of fibrosis degeneration. Polarimetric methods, and specially Mueller polarimetry based on wavelength coding, have been qualified as an efficient tool to describe many different biological aspects. The polarimetric characteristics of the media, extracted from a Lu and Chipman decomposition1, 2 of their Mueller Matrix (MM), are correlated with the degeneracy level of tissue. Different works and results linked to the clinical analysis will be presented and compared to previous performed works.3 Polarimetric imaging will be presented and compared with SHG measurements. A statistical analysis of the distribution of polarimetric parameters (such as the retardance R and depolarisation Pd) will be presented too, in order to characterise the liver fibrosis level into the biomaterial under study.

Localization of tumors in various organs, using edge detection algorithms

NASA Astrophysics Data System (ADS)

López Vélez, Felipe

2015-09-01

The edge of an image is a set of points organized in a curved line, where in each of these points the brightness of the image changes abruptly, or has discontinuities, in order to find these edges there will be five different mathematical methods to be used and later on compared with its peers, this is with the aim of finding which of the methods is the one that can find the edges of any given image. In this paper these five methods will be used for medical purposes in order to find which one is capable of finding the edges of a scanned image more accurately than the others. The problem consists in analyzing the following two biomedicals images. One of them represents a brain tumor and the other one a liver tumor. These images will be analyzed with the help of the five methods described and the results will be compared in order to determine the best method to be used. It was decided to use different algorithms of edge detection in order to obtain the results shown below; Bessel algorithm, Morse algorithm, Hermite algorithm, Weibull algorithm and Sobel algorithm. After analyzing the appliance of each of the methods to both images it's impossible to determine the most accurate method for tumor detection due to the fact that in each case the best method changed, i.e., for the brain tumor image it can be noticed that the Morse method was the best at finding the edges of the image but for the liver tumor image it was the Hermite method. Making further observations it is found that Hermite and Morse have for these two cases the lowest standard deviations, concluding that these two are the most accurate method to find the edges in analysis of biomedical images.

Toxicity of inorganic nanomaterials in biomedical imaging.

PubMed

Li, Jinxia; Chang, Xueling; Chen, Xiaoxia; Gu, Zhanjun; Zhao, Feng; Chai, Zhifang; Zhao, Yuliang

2014-01-01

Inorganic nanoparticles have shown promising potentials as novel biomedical imaging agents with high sensitivity, high spatial and temporal resolution. To translate the laboratory innovations into clinical applications, their potential toxicities are highly concerned and have to be evaluated comprehensively both in vitro and in vivo before their clinical applications. In this review, we first summarized the in vivo and in vitro toxicities of the representative inorganic nanoparticles used in biomedical imagings. Then we further discuss the origin of nanotoxicity of inorganic nanomaterials, including ROS generation and oxidative stress, chemical instability, chemical composition, the surface modification, dissolution of nanoparticles to release excess free ions of metals, metal redox state, and left-over chemicals from synthesis, etc. We intend to provide the readers a better understanding of the toxicology aspects of inorganic nanomaterials and knowledge for achieving optimized designs of safer inorganic nanomaterials for clinical applications. Copyright © 2014 Elsevier Inc. All rights reserved.

Surface engineering of graphene-based nanomaterials for biomedical applications.

PubMed

Shi, Sixiang; Chen, Feng; Ehlerding, Emily B; Cai, Weibo

2014-09-17

Graphene-based nanomaterials have attracted tremendous interest over the past decade due to their unique electronic, optical, mechanical, and chemical properties. However, the biomedical applications of these intriguing nanomaterials are still limited due to their suboptimal solubility/biocompatibility, potential toxicity, and difficulties in achieving active tumor targeting, just to name a few. In this Topical Review, we will discuss in detail the important role of surface engineering (i.e., bioconjugation) in improving the in vitro/in vivo stability and enriching the functionality of graphene-based nanomaterials, which can enable single/multimodality imaging (e.g., optical imaging, positron emission tomography, magnetic resonance imaging) and therapy (e.g., photothermal therapy, photodynamic therapy, and drug/gene delivery) of cancer. Current challenges and future research directions are also discussed and we believe that graphene-based nanomaterials are attractive nanoplatforms for a broad array of future biomedical applications.

Biomedical Applications of Nanodiamonds: An Overview.

PubMed

Passeri, D; Rinaldi, F; Ingallina, C; Carafa, M; Rossi, M; Terranova, M L; Marianecci, C

2015-02-01

Nanodiamonds are a novel class of nanomaterials which have raised much attention for application in biomedical field, as they combine the possibility of being produced on large scale using relatively inexpensive synthetic processes, of being fluorescent as a consequence of the presence of nitrogen vacancies, of having their surfaces functionalized, and of having good biocompatibility. Among other applications, we mainly focus on drug delivery, including cell interaction, targeting, cancer therapy, gene and protein delivery. In addition, nanodiamonds for bone and dental implants and for antibacterial use is discussed. Techniques for detection and imaging of nanodiamonds in biological tissues are also reviewed, including electron microscopy, fluorescence microscopy, Raman mapping, atomic force microscopy, thermal imaging, magnetic resonance imaging, and positron emission tomography, either in vitro, in vivo, or ex vivo. Toxicological aspects related to the use of nanodiamonds are also discussed. Finally, patents, preclinical and clinical trials based on the use of nanodiamonds for biomedical applications are reviewed.

Surface Engineering of Graphene-Based Nanomaterials for Biomedical Applications

PubMed Central

2015-01-01

Graphene-based nanomaterials have attracted tremendous interest over the past decade due to their unique electronic, optical, mechanical, and chemical properties. However, the biomedical applications of these intriguing nanomaterials are still limited due to their suboptimal solubility/biocompatibility, potential toxicity, and difficulties in achieving active tumor targeting, just to name a few. In this Topical Review, we will discuss in detail the important role of surface engineering (i.e., bioconjugation) in improving the in vitro/in vivo stability and enriching the functionality of graphene-based nanomaterials, which can enable single/multimodality imaging (e.g., optical imaging, positron emission tomography, magnetic resonance imaging) and therapy (e.g., photothermal therapy, photodynamic therapy, and drug/gene delivery) of cancer. Current challenges and future research directions are also discussed and we believe that graphene-based nanomaterials are attractive nanoplatforms for a broad array of future biomedical applications. PMID:25117569

Self-assembled nanomaterials for photoacoustic imaging

NASA Astrophysics Data System (ADS)

Wang, Lei; Yang, Pei-Pei; Zhao, Xiao-Xiao; Wang, Hao

2016-01-01

In recent years, extensive endeavors have been paid to construct functional self-assembled nanomaterials for various applications such as catalysis, separation, energy and biomedicines. To date, different strategies have been developed for preparing nanomaterials with diversified structures and functionalities via fine tuning of self-assembled building blocks. In terms of biomedical applications, bioimaging technologies are urgently calling for high-efficient probes/contrast agents for high-performance bioimaging. Photoacoustic (PA) imaging is an emerging whole-body imaging modality offering high spatial resolution, deep penetration and high contrast in vivo. The self-assembled nanomaterials show high stability in vivo, specific tolerance to sterilization and prolonged half-life stability and desirable targeting properties, which is a kind of promising PA contrast agents for biomedical imaging. Herein, we focus on summarizing recent advances in smart self-assembled nanomaterials with NIR absorption as PA contrast agents for biomedical imaging. According to the preparation strategy of the contrast agents, the self-assembled nanomaterials are categorized into two groups, i.e., the ex situ and in situ self-assembled nanomaterials. The driving forces, assembly modes and regulation of PA properties of self-assembled nanomaterials and their applications for long-term imaging, enzyme activity detection and aggregation-induced retention (AIR) effect for diagnosis and therapy are emphasized. Finally, we conclude with an outlook towards future developments of self-assembled nanomaterials for PA imaging.

Self-assembled nanomaterials for photoacoustic imaging.

PubMed

Wang, Lei; Yang, Pei-Pei; Zhao, Xiao-Xiao; Wang, Hao

2016-02-07

In recent years, extensive endeavors have been paid to construct functional self-assembled nanomaterials for various applications such as catalysis, separation, energy and biomedicines. To date, different strategies have been developed for preparing nanomaterials with diversified structures and functionalities via fine tuning of self-assembled building blocks. In terms of biomedical applications, bioimaging technologies are urgently calling for high-efficient probes/contrast agents for high-performance bioimaging. Photoacoustic (PA) imaging is an emerging whole-body imaging modality offering high spatial resolution, deep penetration and high contrast in vivo. The self-assembled nanomaterials show high stability in vivo, specific tolerance to sterilization and prolonged half-life stability and desirable targeting properties, which is a kind of promising PA contrast agents for biomedical imaging. Herein, we focus on summarizing recent advances in smart self-assembled nanomaterials with NIR absorption as PA contrast agents for biomedical imaging. According to the preparation strategy of the contrast agents, the self-assembled nanomaterials are categorized into two groups, i.e., the ex situ and in situ self-assembled nanomaterials. The driving forces, assembly modes and regulation of PA properties of self-assembled nanomaterials and their applications for long-term imaging, enzyme activity detection and aggregation-induced retention (AIR) effect for diagnosis and therapy are emphasized. Finally, we conclude with an outlook towards future developments of self-assembled nanomaterials for PA imaging.

Imaging resolution and properties analysis of super resolution microscopy with parallel detection under different noise, detector and image restoration conditions

NASA Astrophysics Data System (ADS)

Yu, Zhongzhi; Liu, Shaocong; Sun, Shiyi; Kuang, Cuifang; Liu, Xu

2018-06-01

Parallel detection, which can use the additional information of a pinhole plane image taken at every excitation scan position, could be an efficient method to enhance the resolution of a confocal laser scanning microscope. In this paper, we discuss images obtained under different conditions and using different image restoration methods with parallel detection to quantitatively compare the imaging quality. The conditions include different noise levels and different detector array settings. The image restoration methods include linear deconvolution and pixel reassignment with Richard-Lucy deconvolution and with maximum-likelihood estimation deconvolution. The results show that the linear deconvolution share properties such as high-efficiency and the best performance under all different conditions, and is therefore expected to be of use for future biomedical routine research.

Signal and noise modeling in confocal laser scanning fluorescence microscopy.

PubMed

Herberich, Gerlind; Windoffer, Reinhard; Leube, Rudolf E; Aach, Til

2012-01-01

Fluorescence confocal laser scanning microscopy (CLSM) has revolutionized imaging of subcellular structures in biomedical research by enabling the acquisition of 3D time-series of fluorescently-tagged proteins in living cells, hence forming the basis for an automated quantification of their morphological and dynamic characteristics. Due to the inherently weak fluorescence, CLSM images exhibit a low SNR. We present a novel model for the transfer of signal and noise in CLSM that is both theoretically sound as well as corroborated by a rigorous analysis of the pixel intensity statistics via measurement of the 3D noise power spectra, signal-dependence and distribution. Our model provides a better fit to the data than previously proposed models. Further, it forms the basis for (i) the simulation of the CLSM imaging process indispensable for the quantitative evaluation of CLSM image analysis algorithms, (ii) the application of Poisson denoising algorithms and (iii) the reconstruction of the fluorescence signal.

Image acquisition context: procedure description attributes for clinically relevant indexing and selective retrieval of biomedical images.

PubMed

Bidgood, W D; Bray, B; Brown, N; Mori, A R; Spackman, K A; Golichowski, A; Jones, R H; Korman, L; Dove, B; Hildebrand, L; Berg, M

1999-01-01

To support clinically relevant indexing of biomedical images and image-related information based on the attributes of image acquisition procedures and the judgments (observations) expressed by observers in the process of image interpretation. The authors introduce the notion of "image acquisition context," the set of attributes that describe image acquisition procedures, and present a standards-based strategy for utilizing the attributes of image acquisition context as indexing and retrieval keys for digital image libraries. The authors' indexing strategy is based on an interdependent message/terminology architecture that combines the Digital Imaging and Communication in Medicine (DICOM) standard, the SNOMED (Systematized Nomenclature of Human and Veterinary Medicine) vocabulary, and the SNOMED DICOM microglossary. The SNOMED DICOM microglossary provides context-dependent mapping of terminology to DICOM data elements. The capability of embedding standard coded descriptors in DICOM image headers and image-interpretation reports improves the potential for selective retrieval of image-related information. This favorably affects information management in digital libraries.

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

PubMed Central

Di Ruberto, Cecilia; Kocher, Michel

2018-01-01

Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images. PMID:29419781

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology.

PubMed

Loddo, Andrea; Di Ruberto, Cecilia; Kocher, Michel

2018-02-08

Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images.

[Scientometrics and bibliometrics of biomedical engineering periodicals and papers].

PubMed

Zhao, Ping; Xu, Ping; Li, Bingyan; Wang, Zhengrong

2003-09-01

This investigation was made to reveal the current status, research trend and research level of biomedical engineering in Chinese mainland by means of scientometrics and to assess the quality of the four domestic publications by bibliometrics. We identified all articles of four related publications by searching Chinese and foreign databases from 1997 to 2001. All articles collected or cited by these databases were searched and statistically analyzed for finding out the relevant distributions, including databases, years, authors, institutions, subject headings and subheadings. The source of sustentation funds and the related articles were analyzed too. The results showed that two journals were cited by two foreign databases and five Chinese databases simultaneously. The output of Journal of Biomedical Engineering was the highest. Its quantity of original papers cited by EI, CA and the totality of papers sponsored by funds were higher than those of the others, but the quantity and percentage per year of biomedical articles cited by EI were decreased in all. Inland core authors and institutions had come into being in the field of biomedical engineering. Their research topics were mainly concentrated on ten subject headings which included biocompatible materials, computer-assisted signal processing, electrocardiography, computer-assisted image processing, biomechanics, algorithms, electroencephalography, automatic data processing, mechanical stress, hemodynamics, mathematical computing, microcomputers, theoretical models, etc. The main subheadings were concentrated on instrumentation, physiopathology, diagnosis, therapy, ultrasonography, physiology, analysis, surgery, pathology, method, etc.

Imaging System and Method for Biomedical Analysis

DTIC Science & Technology

2013-03-11

biological particles and items of interest. Broadly, Padmanabhan et al. utilize the diffraction of a laser light source in flow cytometry to count...spread of light from multiple LED devices over the entire sample surface. Preferably, light source 308 projects a full spectrum white light. Light...for example, red blood cells, white blood cells (which may include lymphocytes which are relatively large and easily detectable), T-helper cells

Microscopy and Image Analysis.

PubMed

McNamara, George; Difilippantonio, Michael; Ried, Thomas; Bieber, Frederick R

2017-07-11

This unit provides an overview of light microscopy, including objectives, light sources, filters, film, and color photography for fluorescence microscopy and fluorescence in situ hybridization (FISH). We believe there are excellent opportunities for cytogeneticists, pathologists, and other biomedical readers, to take advantage of specimen optical clearing techniques and expansion microscopy-we briefly point to these new opportunities. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

The Mechanisms and Biomedical Applications of an NIR BODIPY-Based Switchable Fluorescent Probe

PubMed Central

Cheng, Bingbing; Bandi, Venugopal; Yu, Shuai; D’Souza, Francis; Nguyen, Kytai T.; Hong, Yi; Tang, Liping; Yuan, Baohong

2017-01-01

Highly environment-sensitive fluorophores have been desired for many biomedical applications. Because of the noninvasive operation, high sensitivity, and high specificity to the microenvironment change, they can be used as excellent probes for fluorescence sensing/imaging, cell tracking/imaging, molecular imaging for cancer, and so on (i.e., polarity, viscosity, temperature, or pH measurement). In this work, investigations of the switching mechanism of a recently reported near-infrared environment-sensitive fluorophore, ADP(CA)2, were conducted. Besides, multiple potential biomedical applications of this switchable fluorescent probe have been demonstrated, including wash-free live-cell fluorescence imaging, in vivo tissue fluorescence imaging, temperature sensing, and ultrasound-switchable fluorescence (USF) imaging. The fluorescence of the ADP(CA)2 is extremely sensitive to the microenvironment, especially polarity and viscosity. Our investigations showed that the fluorescence of ADP(CA)2 can be switched on by low polarity, high viscosity, or the presence of protein and surfactants. In wash-free live-cell imaging, the fluorescence of ADP(CA)2 inside cells was found much brighter than the dye-containing medium and was retained for at least two days. In all of the fluorescence imaging applications conducted in this study, high target-to-noise (>5-fold) was achieved. In addition, a high temperature sensitivity (73-fold per Celsius degree) of ADP(CA)2-based temperature probes was found in temperature sensing. PMID:28208666

In vivo optical imaging and dynamic contrast methods for biomedical research

PubMed Central

Hillman, Elizabeth M. C.; Amoozegar, Cyrus B.; Wang, Tracy; McCaslin, Addason F. H.; Bouchard, Matthew B.; Mansfield, James; Levenson, Richard M.

2011-01-01

This paper provides an overview of optical imaging methods commonly applied to basic research applications. Optical imaging is well suited for non-clinical use, since it can exploit an enormous range of endogenous and exogenous forms of contrast that provide information about the structure and function of tissues ranging from single cells to entire organisms. An additional benefit of optical imaging that is often under-exploited is its ability to acquire data at high speeds; a feature that enables it to not only observe static distributions of contrast, but to probe and characterize dynamic events related to physiology, disease progression and acute interventions in real time. The benefits and limitations of in vivo optical imaging for biomedical research applications are described, followed by a perspective on future applications of optical imaging for basic research centred on a recently introduced real-time imaging technique called dynamic contrast-enhanced small animal molecular imaging (DyCE). PMID:22006910

Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions.

PubMed

Choi, Hongyoon

2018-04-01

Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt this technique. In this regard, it is expected that deep learning will potentially affect the roles of molecular imaging experts as well as clinical decision making. This review firstly offers a basic overview of deep learning particularly for image data analysis to give knowledge to nuclear medicine physicians and researchers. Because of the unique characteristics and distinctive aims of various types of molecular imaging, deep learning applications can be different from other fields. In this context, the review deals with current perspectives of deep learning in molecular imaging particularly in terms of development of biomarkers. Finally, future challenges of deep learning application for molecular imaging and future roles of experts in molecular imaging will be discussed.

A philosophy for CNS radiotracer design.

PubMed

Van de Bittner, Genevieve C; Ricq, Emily L; Hooker, Jacob M

2014-10-21

Decades after its discovery, positron emission tomography (PET) remains the premier tool for imaging neurochemistry in living humans. Technological improvements in radiolabeling methods, camera design, and image analysis have kept PET in the forefront. In addition, the use of PET imaging has expanded because researchers have developed new radiotracers that visualize receptors, transporters, enzymes, and other molecular targets within the human brain. However, of the thousands of proteins in the central nervous system (CNS), researchers have successfully imaged fewer than 40 human proteins. To address the critical need for new radiotracers, this Account expounds on the decisions, strategies, and pitfalls of CNS radiotracer development based on our current experience in this area. We discuss the five key components of radiotracer development for human imaging: choosing a biomedical question, selection of a biological target, design of the radiotracer chemical structure, evaluation of candidate radiotracers, and analysis of preclinical imaging. It is particularly important to analyze the market of scientists or companies who might use a new radiotracer and carefully select a relevant biomedical question(s) for that audience. In the selection of a specific biological target, we emphasize how target localization and identity can constrain this process and discuss the optimal target density and affinity ratios needed for binding-based radiotracers. In addition, we discuss various PET test-retest variability requirements for monitoring changes in density, occupancy, or functionality for new radiotracers. In the synthesis of new radiotracer structures, high-throughput, modular syntheses have proved valuable, and these processes provide compounds with sites for late-stage radioisotope installation. As a result, researchers can manage the time constraints associated with the limited half-lives of isotopes. In order to evaluate brain uptake, a number of methods are available to predict bioavailability, blood-brain barrier (BBB) permeability, and the associated issues of nonspecific binding and metabolic stability. To evaluate the synthesized chemical library, researchers need to consider high-throughput affinity assays, the analysis of specific binding, and the importance of fast binding kinetics. Finally, we describe how we initially assess preclinical radiotracer imaging, using brain uptake, specific binding, and preliminary kinetic analysis to identify promising radiotracers that may be useful for human brain imaging. Although we discuss these five design components separately and linearly in this Account, in practice we develop new PET-based radiotracers using these design components nonlinearly and iteratively to develop new compounds in the most efficient way possible.

Recommending images of user interests from the biomedical literature

NASA Astrophysics Data System (ADS)

Clukey, Steven; Xu, Songhua

2013-03-01

Every year hundreds of thousands of biomedical images are published in journals and conferences. Consequently, finding images relevant to one's interests becomes an ever daunting task. This vast amount of literature creates a need for intelligent and easy-to-use tools that can help researchers effectively navigate through the content corpus and conveniently locate materials of their interests. Traditionally, literature search tools allow users to query content using topic keywords. However, manual query composition is often time and energy consuming. A better system would be one that can automatically deliver relevant content to a researcher without having the end user manually manifest one's search intent and interests via search queries. Such a computer-aided assistance for information access can be provided by a system that first determines a researcher's interests automatically and then recommends images relevant to the person's interests accordingly. The technology can greatly improve a researcher's ability to stay up to date in their fields of study by allowing them to efficiently browse images and documents matching their needs and interests among the vast amount of the biomedical literature. A prototype system implementation of the technology can be accessed via http://www.smartdataware.com.

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals.

PubMed

Betts, Aislinn M; McGoldrick, Matthew T; Dethlefs, Christopher R; Piotrowicz, Justin; Van Avermaete, Tony; Maki, Jeff; Gerstler, Steve; Leevy, W M

2017-04-25

Biomedical imaging modalities like computed tomography (CT) and magnetic resonance (MR) provide excellent platforms for collecting three-dimensional data sets of patient or specimen anatomy in clinical or preclinical settings. However, the use of a virtual, on-screen display limits the ability of these tomographic images to fully convey the anatomical information embedded within. One solution is to interface a biomedical imaging data set with 3D printing technology to generate a physical replica. Here we detail a complementary method to visualize tomographic imaging data with a hand-held model: Sub Surface Laser Engraving (SSLE) of crystal glass. SSLE offers several unique benefits including: the facile ability to include anatomical labels, as well as a scale bar; streamlined multipart assembly of complex structures in one medium; high resolution in the X, Y, and Z planes; and semi-transparent shells for visualization of internal anatomical substructures. Here we demonstrate the process of SSLE with CT data sets derived from pre-clinical and clinical sources. This protocol will serve as a powerful and inexpensive new tool with which to visualize complex anatomical structures for scientists and students in a number of educational and research settings.

On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

PubMed Central

Thiyagalingam, Jeyarajan; Goodman, Daniel; Schnabel, Julia A.; Trefethen, Anne; Grau, Vicente

2011-01-01

Images are ubiquitous in biomedical applications from basic research to clinical practice. With the rapid increase in resolution, dimensionality of the images and the need for real-time performance in many applications, computational requirements demand proper exploitation of multicore architectures. Towards this, GPU-specific implementations of image analysis algorithms are particularly promising. In this paper, we investigate the mapping of an enhanced motion estimation algorithm to novel GPU-specific architectures, the resulting challenges and benefits therein. Using a database of three-dimensional image sequences, we show that the mapping leads to substantial performance gains, up to a factor of 60, and can provide near-real-time experience. We also show how architectural peculiarities of these devices can be best exploited in the benefit of algorithms, most specifically for addressing the challenges related to their access patterns and different memory configurations. Finally, we evaluate the performance of the algorithm on three different GPU architectures and perform a comprehensive analysis of the results. PMID:21869880

Creating a classification of image types in the medical literature for visual categorization

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Demner-Fushman, Dina; Antani, Sameer

2012-02-01

Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation Forum) and NLM's (National Library of Medicine) OpenI. Furtheron, mappings to NLM's MeSH (Medical Subject Headings), RSNA's RadLex (Radiological Society of North America, Radiology Lexicon), and the IRMA code are also attempted for relevant image types. Advantages derived from such hierarchical classification for medical image retrieval are being evaluated through benchmarks such as imageCLEF, and R&D systems such as NLM's OpenI. The goal is to extend this hierarchy progressively and (through adding image types occurring in the biomedical literature) to have a terminology for visual image classification based on image types distinguishable by visual means and occurring in the medical open access literature.

The Impact Factor of Radiological Journals: Associations with Journal Content and Other Characteristics Over a Recent 12-Year Period.

PubMed

Rosenkrantz, Andrew B; Ayoola, Abimbola

2016-06-01

The aim of this study was to evaluate the trends in the impact factor (IF) of radiological journals over a recent 12-year period, including associations between IF and journal topic. Journal Citation Reports (JCR) was used to identify all biomedical journals and all radiological journals (assigned a JCR category of "Radiology, Nuclear Medicine, & Medical Imaging"), along with journal IF, in 2003 and 2014. Radiological journals were manually classified by topic. Trends in median IF (mIF) were assessed. The number of radiological journals increased from 83 (2003) to 125 (2014) (all biomedical journals: 5907 to 8718, respectively). mIF of radiological journals increased from 1.42 (2003) to 1.75 (2014) (all biomedical journals: 0.93 to 1.46, respectively). The most common topic among new radiological journals was general (nonspecialized) radiology (8). Five new radiological journals in 2014 were in topics (cancer imaging and molecular imaging) having no journals in 2003. mIF of general radiological journals was 1.49. Topics having highest mIF were cardiac imaging (2.94), optics (2.86), molecular imaging (2.77), radiation oncology (2.60), and neuroradiology (2.25). Topics with lowest mIF were ultrasound (1.19) and interventional radiology (1.44). Topics with the largest increase in mIF were cardiac imaging (from 1.17 to 2.94) and neuroradiology (from 1.07 to 2.25). Radiological journals exhibited higher mIF than biomedical journals overall. Among radiological journals, subspecialty journals had highest mIF. While a considerable number of new radiological journals since 2003 were general radiology journals having relatively low IF, there were also new journal topics representing emerging areas of subspecialized radiological research. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

PubMed Central

Lahmiri, Salim; Boukadoum, Mounir

2013-01-01

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Synthesis and size classification of metal oxide nanoparticles for biomedical applications

NASA Astrophysics Data System (ADS)

Atsumi, Takashi; Jeyadevan, Balachandran; Sato, Yoshinori; Tamura, Kazuchika; Aiba, Setsuya; Tohji, Kazuyuki

2004-12-01

Magnetic nanoparticles are considered for biomedical applications, such as the medium in magnetic resonance imaging, hyperthermia, drug delivery, and for the purification or classification of DNA or virus. The performance of magnetic nanoparticles in biomedical application such as hyperthermia depends very much on the magnetic properties, size and size distribution. We briefly described the basic idea behind their use in drug delivery, magnetic separation and hyperthermia and discussed the prerequisite properties magnetic particles for biomedical applications. Finally reported the synthesis and classification scheme to prepare magnetite (Fe3O4) nanoparticles with narrow size distribution for magnetic fluid hyperthermia.

Wireless image-data transmission from an implanted image sensor through a living mouse brain by intra body communication

NASA Astrophysics Data System (ADS)

Hayami, Hajime; Takehara, Hiroaki; Nagata, Kengo; Haruta, Makito; Noda, Toshihiko; Sasagawa, Kiyotaka; Tokuda, Takashi; Ohta, Jun

2016-04-01

Intra body communication technology allows the fabrication of compact implantable biomedical sensors compared with RF wireless technology. In this paper, we report the fabrication of an implantable image sensor of 625 µm width and 830 µm length and the demonstration of wireless image-data transmission through a brain tissue of a living mouse. The sensor was designed to transmit output signals of pixel values by pulse width modulation (PWM). The PWM signals from the sensor transmitted through a brain tissue were detected by a receiver electrode. Wireless data transmission of a two-dimensional image was successfully demonstrated in a living mouse brain. The technique reported here is expected to provide useful methods of data transmission using micro sized implantable biomedical sensors.

Performance of a gaseous detector based energy dispersive X-ray fluorescence imaging system: Analysis of human teeth treated with dental amalgam

NASA Astrophysics Data System (ADS)

Silva, A. L. M.; Figueroa, R.; Jaramillo, A.; Carvalho, M. L.; Veloso, J. F. C. A.

2013-08-01

Energy dispersive X-ray fluorescence (EDXRF) imaging systems are of great interest in many applications of different areas, once they allow us to get images of the spatial elemental distribution in the samples. The detector system used in this study is based on a micro patterned gas detector, named Micro-Hole and Strip Plate. The full field of view system, with an active area of 28 × 28 mm2 presents some important features for EDXRF imaging applications, such as a position resolution below 125 μm, an intrinsic energy resolution of about 14% full width at half maximum for 5.9 keV X-rays, and a counting rate capability of 0.5 MHz. In this work, analysis of human teeth treated by dental amalgam was performed by using the EDXRF imaging system mentioned above. The goal of the analysis is to evaluate the system capabilities in the biomedical field by measuring the drift of the major constituents of a dental amalgam, Zn and Hg, throughout the tooth structures. The elemental distribution pattern of these elements obtained during the analysis suggests diffusion of these elements from the amalgam to teeth tissues.

The role of image registration in brain mapping

PubMed Central

Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

Simulation of bright-field microscopy images depicting pap-smear specimen

PubMed Central

Malm, Patrik; Brun, Anders; Bengtsson, Ewert

2015-01-01

As digital imaging is becoming a fundamental part of medical and biomedical research, the demand for computer-based evaluation using advanced image analysis is becoming an integral part of many research projects. A common problem when developing new image analysis algorithms is the need of large datasets with ground truth on which the algorithms can be tested and optimized. Generating such datasets is often tedious and introduces subjectivity and interindividual and intraindividual variations. An alternative to manually created ground-truth data is to generate synthetic images where the ground truth is known. The challenge then is to make the images sufficiently similar to the real ones to be useful in algorithm development. One of the first and most widely studied medical image analysis tasks is to automate screening for cervical cancer through Pap-smear analysis. As part of an effort to develop a new generation cervical cancer screening system, we have developed a framework for the creation of realistic synthetic bright-field microscopy images that can be used for algorithm development and benchmarking. The resulting framework has been assessed through a visual evaluation by experts with extensive experience of Pap-smear images. The results show that images produced using our described methods are realistic enough to be mistaken for real microscopy images. The developed simulation framework is very flexible and can be modified to mimic many other types of bright-field microscopy images. © 2015 The Authors. Published by Wiley Periodicals, Inc. on behalf of ISAC PMID:25573002

Biomedical informatics and translational medicine.

PubMed

Sarkar, Indra Neil

2010-02-26

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

Structure and properties of clinical coralline implants measured via 3D imaging and analysis.

PubMed

Knackstedt, Mark Alexander; Arns, Christoph H; Senden, Tim J; Gross, Karlis

2006-05-01

The development and design of advanced porous materials for biomedical applications requires a thorough understanding of how material structure impacts on mechanical and transport properties. This paper illustrates a 3D imaging and analysis study of two clinically proven coral bone graft samples (Porites and Goniopora). Images are obtained from X-ray micro-computed tomography (micro-CT) at a resolution of 16.8 microm. A visual comparison of the two images shows very different structure; Porites has a homogeneous structure and consistent pore size while Goniopora has a bimodal pore size and a strongly disordered structure. A number of 3D structural characteristics are measured directly on the images including pore volume-to-surface-area, pore and solid size distributions, chord length measurements and tortuosity. Computational results made directly on the digitized tomographic images are presented for the permeability, diffusivity and elastic modulus of the coral samples. The results allow one to quantify differences between the two samples. 3D digital analysis can provide a more thorough assessment of biomaterial structure including the pore wall thickness, local flow, mechanical properties and diffusion pathways. We discuss the implications of these results to the development of optimal scaffold design for tissue ingrowth.

Lipid-polymer hybrid nanoparticle-mediated therapeutics delivery: advances and challenges.

PubMed

Bose, Rajendran J C; Ravikumar, Rramaswamy; Karuppagounder, Vengadeshprabu; Bennet, Devasier; Rangasamy, Sabarinathan; Thandavarayan, Rajarajan A

2017-08-01

With rapid advances in nanomedicine, lipid-polymer hybrid nanoparticles (LPHNPs) have emerged as promising nanocarriers for several biomedical applications, including therapeutics delivery and biomedical imaging. Significant research has been dedicated to biomimetic or targeting functionalization, as well as controlled and image-guided drug-release capabilities. Despite this research, the clinical translation of LPHNP-mediated therapeutics delivery has progressed incrementally. In this review, we discuss the recent advances in and challenges to the development and application of LPHNPs, present examples to demonstrate the advantages of LPHNPs in therapeutics delivery and imaging applications, and discuss the translational obstacles to LPHNP technology. Copyright © 2017. Published by Elsevier Ltd.

Application and Miniaturization of Linear and Nonlinear Raman Microscopy for Biomedical Imaging

NASA Astrophysics Data System (ADS)

Mittal, Richa

Current diagnostics for several disorders rely on surgical biopsy or evaluation of ex vivo bodily fluids, which have numerous drawbacks. We evaluated the potential for vibrational techniques (both linear and nonlinear Raman) as a reliable and noninvasive diagnostic tool. Raman spectroscopy is an optical technique for molecular analysis that has been used extensively in various biomedical applications. Based on demonstrated capabilities of Raman spectroscopy we evaluated the potential of the technique for providing a noninvasive diagnosis of mucopolysaccharidosis (MPS). These studies show that Raman spectroscopy can detect subtle changes in tissue biochemistry. In applications where sub-micrometer visualization of tissue compositional change is required, a transition from spectroscopy to high quality imaging is necessary. Nonlinear vibrational microscopy is sensitive to the same molecular vibrations as linear Raman, but features fast imaging capabilities. Coherent Raman scattering when combined with other nonlinear optical (NLO) techniques (like two-photon excited fluorescence and second harmonic generation) forms a collection of advanced optical techniques that provide noninvasive chemical contrast at submicron resolution. This capability to examine tissues without external molecular agents is driving the NLO approach towards clinical applications. However, the unique imaging capabilities of NLO microscopy are accompanied by complex instrument requirements. Clinical examination requires portable imaging systems for rapid inspection of tissues. Optical components utilized in NLO microscopy would then need substantial miniaturization and optimization to enable in vivo use. The challenges in designing compact microscope objective lenses and laser beam scanning mechanisms are discussed. The development of multimodal NLO probes for imaging oral cavity tissue is presented. Our prototype has been examined for ex vivo tissue imaging based on intrinsic fluorescence and SHG contrast. These studies show a potential for multiphoton compact probes to be used for real time imaging in the clinic.

Adaptive photoacoustic imaging quality optimization with EMD and reconstruction

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.

2016-10-01

Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.

Lensfree Computational Microscopy Tools and their Biomedical Applications

NASA Astrophysics Data System (ADS)

Sencan, Ikbal

Conventional microscopy has been a revolutionary tool for biomedical applications since its invention several centuries ago. Ability to non-destructively observe very fine details of biological objects in real time enabled to answer many important questions about their structures and functions. Unfortunately, most of these advance microscopes are complex, bulky, expensive, and/or hard to operate, so they could not reach beyond the walls of well-equipped laboratories. Recent improvements in optoelectronic components and computational methods allow creating imaging systems that better fulfill the specific needs of clinics or research related biomedical applications. In this respect, lensfree computational microscopy aims to replace bulky and expensive optical components with compact and cost-effective alternatives through the use of computation, which can be particularly useful for lab-on-a-chip platforms as well as imaging applications in low-resource settings. Several high-throughput on-chip platforms are built with this approach for applications including, but not limited to, cytometry, micro-array imaging, rare cell analysis, telemedicine, and water quality screening. The lack of optical complexity in these lensfree on-chip imaging platforms is compensated by using computational techniques. These computational methods are utilized for various purposes in coherent, incoherent and fluorescent on-chip imaging platforms e.g. improving the spatial resolution, to undo the light diffraction without using lenses, localization of objects in a large volume and retrieval of the phase or the color/spectral content of the objects. For instance, pixel super resolution approaches based on source shifting are used in lensfree imaging platforms to prevent under sampling, Bayer pattern, and aliasing artifacts. Another method, iterative phase retrieval, is utilized to compensate the lack of lenses by undoing the diffraction and removing the twin image noise of in-line holograms. This technique enables recovering the complex optical field from its intensity measurement(s) by using additional constraints in iterations, such as spatial boundaries and other known properties of objects. Another computational tool employed in lensfree imaging is compressive sensing (or decoding), which is a novel method taking advantage of the fact that natural signals/objects are mostly sparse or compressible in known bases. This inherent property of objects enables better signal recovery when the number of measurement is low, even below the Nyquist rate, and increases the additive noise immunity of the system.

76 FR 69748 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-09

... Grossman, DDS, Scientific Review Officer, National Institute of Biomedical Imagin and Bioengineering... Bethesda Metro Station, Bethesda, MD 20814. Contact Person: Ruth Grossman, DDS, Scientific Review Officer...Continental Chicago, 505 North Michigan Avenue, Chicago, IL. Contact Person: Ruth Grossman, DDS, Scientific...

Realistic tissue visualization using photoacoustic image

NASA Astrophysics Data System (ADS)

Cho, Seonghee; Managuli, Ravi; Jeon, Seungwan; Kim, Jeesu; Kim, Chulhong

2018-02-01

Visualization methods are very important in biomedical imaging. As a technology that understands life, biomedical imaging has the unique advantage of providing the most intuitive information in the image. This advantage of biomedical imaging can be greatly improved by choosing a special visualization method. This is more complicated in volumetric data. Volume data has the advantage of containing 3D spatial information. Unfortunately, the data itself cannot directly represent the potential value. Because images are always displayed in 2D space, visualization is the key and creates the real value of volume data. However, image processing of 3D data requires complicated algorithms for visualization and high computational burden. Therefore, specialized algorithms and computing optimization are important issues in volume data. Photoacoustic-imaging is a unique imaging modality that can visualize the optical properties of deep tissue. Because the color of the organism is mainly determined by its light absorbing component, photoacoustic data can provide color information of tissue, which is closer to real tissue color. In this research, we developed realistic tissue visualization using acoustic-resolution photoacoustic volume data. To achieve realistic visualization, we designed specialized color transfer function, which depends on the depth of the tissue from the skin. We used direct ray casting method and processed color during computing shader parameter. In the rendering results, we succeeded in obtaining similar texture results from photoacoustic data. The surface reflected rays were visualized in white, and the reflected color from the deep tissue was visualized red like skin tissue. We also implemented the CUDA algorithm in an OpenGL environment for real-time interactive imaging.

Biostatistical analysis of quantitative immunofluorescence microscopy images.

PubMed

Giles, C; Albrecht, M A; Lam, V; Takechi, R; Mamo, J C

2016-12-01

Semiquantitative immunofluorescence microscopy has become a key methodology in biomedical research. Typical statistical workflows are considered in the context of avoiding pseudo-replication and marginalising experimental error. However, immunofluorescence microscopy naturally generates hierarchically structured data that can be leveraged to improve statistical power and enrich biological interpretation. Herein, we describe a robust distribution fitting procedure and compare several statistical tests, outlining their potential advantages/disadvantages in the context of biological interpretation. Further, we describe tractable procedures for power analysis that incorporates the underlying distribution, sample size and number of images captured per sample. The procedures outlined have significant potential for increasing understanding of biological processes and decreasing both ethical and financial burden through experimental optimization. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Optimizing Ti:Sapphire laser for quantitative biomedical imaging

NASA Astrophysics Data System (ADS)

James, Jeemol; Thomsen, Hanna; Hanstorp, Dag; Alemán Hérnandez, Felipe Ademir; Rothe, Sebastian; Enger, Jonas; Ericson, Marica B.

2018-02-01

Ti:Sapphire lasers are powerful tools in the field of scientific research and industry for a wide range of applications such as spectroscopic studies and microscopic imaging where tunable near-infrared light is required. To push the limits of the applicability of Ti:Sapphire lasers, fundamental understanding of the construction and operation is required. This paper presents two projects, (i) dealing with the building and characterization of custom built tunable narrow linewidth Ti:Sapphire laser for fundamental spectroscopy studies; and the second project (ii) the implementation of a fs-pulsed commercial Ti:Sapphire laser in an experimental multiphoton microscopy platform. For the narrow linewidth laser, a gold-plated diffraction grating with a Littrow geometry was implemented for highresolution wavelength selection. We demonstrate that the laser is tunable between 700 to 950 nm, operating in a pulsed mode with a repetition rate of 1 kHz and maximum average output power around 350 mW. The output linewidth was reduced from 6 GHz to 1.5 GHz by inserting an additional 6 mm thick etalon. The bandwidth was measured by means of a scanning Fabry Perot interferometer. Future work will focus on using a fs-pulsed commercial Ti:Sapphire laser (Tsunami, Spectra physics), operating at 80 MHz and maximum average output power around 1 W, for implementation in an experimental multiphoton microscopy set up dedicated for biomedical applications. Special focus will be on controlling pulse duration and dispersion in the optical components and biological tissue using pulse compression. Furthermore, time correlated analysis of the biological samples will be performed with the help of time correlated single photon counting module (SPCM, Becker&Hickl) which will give a novel dimension in quantitative biomedical imaging.

Blind source separation of ex-vivo aorta tissue multispectral images

PubMed Central

Galeano, July; Perez, Sandra; Montoya, Yonatan; Botina, Deivid; Garzón, Johnson

2015-01-01

Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method’s performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue. PMID:26137366

A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

PubMed Central

Rahman, M. M.; Antani, S. K.; Thoma, G. R.

2011-01-01

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. PMID:21822350

Carnegie Mellon University bioimaging day 2014: Challenges and opportunities in digital pathology

PubMed Central

Rohde, Gustavo K.; Ozolek, John A.; Parwani, Anil V.; Pantanowitz, Liron

2014-01-01

Recent advances in digital imaging is impacting the practice of pathology. One of the key enabling technologies that is leading the way towards this transformation is the use of whole slide imaging (WSI) which allows glass slides to be converted into large image files that can be shared, stored, and analyzed rapidly. Many applications around this novel technology have evolved in the last decade including education, research and clinical applications. This publication highlights a collection of abstracts, each corresponding to a talk given at Carnegie Mellon University's (CMU) Bioimaging Day 2014 co-sponsored by the Biomedical Engineering and Lane Center for Computational Biology Departments at CMU. Topics related specifically to digital pathology are presented in this collection of abstracts. These include topics related to digital workflow implementation, imaging and artifacts, storage demands, and automated image analysis algorithms. PMID:25250190

Insight into efficient image registration techniques and the demons algorithm.

PubMed

Vercauteren, Tom; Pennec, Xavier; Malis, Ezio; Perchant, Aymeric; Ayache, Nicholas

2007-01-01

As image registration becomes more and more central to many biomedical imaging applications, the efficiency of the algorithms becomes a key issue. Image registration is classically performed by optimizing a similarity criterion over a given spatial transformation space. Even if this problem is considered as almost solved for linear registration, we show in this paper that some tools that have recently been developed in the field of vision-based robot control can outperform classical solutions. The adequacy of these tools for linear image registration leads us to revisit non-linear registration and allows us to provide interesting theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage to the symmetric forces variant of the demons algorithm. We show that, on controlled experiments, this advantage is confirmed, and yields a faster convergence.

Carnegie Mellon University bioimaging day 2014: Challenges and opportunities in digital pathology.

PubMed

Rohde, Gustavo K; Ozolek, John A; Parwani, Anil V; Pantanowitz, Liron

2014-01-01

Recent advances in digital imaging is impacting the practice of pathology. One of the key enabling technologies that is leading the way towards this transformation is the use of whole slide imaging (WSI) which allows glass slides to be converted into large image files that can be shared, stored, and analyzed rapidly. Many applications around this novel technology have evolved in the last decade including education, research and clinical applications. This publication highlights a collection of abstracts, each corresponding to a talk given at Carnegie Mellon University's (CMU) Bioimaging Day 2014 co-sponsored by the Biomedical Engineering and Lane Center for Computational Biology Departments at CMU. Topics related specifically to digital pathology are presented in this collection of abstracts. These include topics related to digital workflow implementation, imaging and artifacts, storage demands, and automated image analysis algorithms.

HaloTag Technology: A Versatile Platform for Biomedical Applications

PubMed Central

2015-01-01

Exploration of protein function and interaction is critical for discovering links among genomics, proteomics, and disease state; yet, the immense complexity of proteomics found in biological systems currently limits our investigational capacity. Although affinity and autofluorescent tags are widely employed for protein analysis, these methods have been met with limited success because they lack specificity and require multiple fusion tags and genetic constructs. As an alternative approach, the innovative HaloTag protein fusion platform allows protein function and interaction to be comprehensively analyzed using a single genetic construct with multiple capabilities. This is accomplished using a simplified process, in which a variable HaloTag ligand binds rapidly to the HaloTag protein (usually linked to the protein of interest) with high affinity and specificity. In this review, we examine all current applications of the HaloTag technology platform for biomedical applications, such as the study of protein isolation and purification, protein function, protein–protein and protein–DNA interactions, biological assays, in vitro cellular imaging, and in vivo molecular imaging. In addition, novel uses of the HaloTag platform are briefly discussed along with potential future applications. PMID:25974629

Efficient processing of fluorescence images using directional multiscale representations.

PubMed

Labate, D; Laezza, F; Negi, P; Ozcan, B; Papadakis, M

2014-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.

Efficient processing of fluorescence images using directional multiscale representations

PubMed Central

Labate, D.; Laezza, F.; Negi, P.; Ozcan, B.; Papadakis, M.

2017-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data. PMID:28804225

Acquiring 3-D information about thick objects from differential interference contrast images using texture extraction

NASA Astrophysics Data System (ADS)

Sierra, Heidy; Brooks, Dana; Dimarzio, Charles

2010-07-01

The extraction of 3-D morphological information about thick objects is explored in this work. We extract this information from 3-D differential interference contrast (DIC) images by applying a texture detection method. Texture extraction methods have been successfully used in different applications to study biological samples. A 3-D texture image is obtained by applying a local entropy-based texture extraction method. The use of this method to detect regions of blastocyst mouse embryos that are used in assisted reproduction techniques such as in vitro fertilization is presented as an example. Results demonstrate the potential of using texture detection methods to improve morphological analysis of thick samples, which is relevant to many biomedical and biological studies. Fluorescence and optical quadrature microscope phase images are used for validation.

Laser scanning confocal microscopy: history, applications, and related optical sectioning techniques.

PubMed

Paddock, Stephen W; Eliceiri, Kevin W

2014-01-01

Confocal microscopy is an established light microscopical technique for imaging fluorescently labeled specimens with significant three-dimensional structure. Applications of confocal microscopy in the biomedical sciences include the imaging of the spatial distribution of macromolecules in either fixed or living cells, the automated collection of 3D data, the imaging of multiple labeled specimens and the measurement of physiological events in living cells. The laser scanning confocal microscope continues to be chosen for most routine work although a number of instruments have been developed for more specific applications. Significant improvements have been made to all areas of the confocal approach, not only to the instruments themselves, but also to the protocols of specimen preparation, to the analysis, the display, the reproduction, sharing and management of confocal images using bioinformatics techniques.

Seeing through walls at the nanoscale: Microwave microscopy of enclosed objects and processes in liquids

DOE PAGES

Velmurugan, Jeyavel; Kalinin, Sergei V.; Kolmakov, Andrei; ...

2016-02-11

Here, noninvasive in situ nanoscale imaging in liquid environments is a current imperative in the analysis of delicate biomedical objects and electrochemical processes at reactive liquid–solid interfaces. Microwaves of a few gigahertz frequencies offer photons with energies of ≈10 μeV, which can affect neither electronic states nor chemical bonds in condensed matter. Here, we describe an implementation of scanning near-field microwave microscopy for imaging in liquids using ultrathin molecular impermeable membranes separating scanning probes from samples enclosed in environmental cells. We imaged a model electroplating reaction as well as individual live cells. Through a side-by-side comparison of the microwave imagingmore » with scanning electron microscopy, we demonstrate the advantage of microwaves for artifact-free imaging.« less

Deep-tissue focal fluorescence imaging with digitally time-reversed ultrasound-encoded light

PubMed Central

Wang, Ying Min; Judkewitz, Benjamin; DiMarzio, Charles A.; Yang, Changhuei

2012-01-01

Fluorescence imaging is one of the most important research tools in biomedical sciences. However, scattering of light severely impedes imaging of thick biological samples beyond the ballistic regime. Here we directly show focusing and high-resolution fluorescence imaging deep inside biological tissues by digitally time-reversing ultrasound-tagged light with high optical gain (~5×105). We confirm the presence of a time-reversed optical focus along with a diffuse background—a corollary of partial phase conjugation—and develop an approach for dynamic background cancellation. To illustrate the potential of our method, we image complex fluorescent objects and tumour microtissues at an unprecedented depth of 2.5 mm in biological tissues at a lateral resolution of 36 μm×52 μm and an axial resolution of 657 μm. Our results set the stage for a range of deep-tissue imaging applications in biomedical research and medical diagnostics. PMID:22735456

Medical imaging education in biomedical engineering curriculum: courseware development and application through a hybrid teaching model.

PubMed

Zhao, Weizhao; Li, Xiping; Chen, Hairong; Manns, Fabrice

2012-01-01

Medical Imaging is a key training component in Biomedical Engineering programs. Medical imaging education is interdisciplinary training, involving physics, mathematics, chemistry, electrical engineering, computer engineering, and applications in biology and medicine. Seeking an efficient teaching method for instructors and an effective learning environment for students has long been a goal for medical imaging education. By the support of NSF grants, we developed the medical imaging teaching software (MITS) and associated dynamic assessment tracking system (DATS). The MITS/DATS system has been applied to junior and senior medical imaging classes through a hybrid teaching model. The results show that student's learning gain improved, particularly in concept understanding and simulation project completion. The results also indicate disparities in subjective perception between junior and senior classes. Three institutions are collaborating to expand the courseware system and plan to apply it to different class settings.

Precise diagnosis in different scenarios using photoacoustic and fluorescence imaging with dual-modality nanoparticles

NASA Astrophysics Data System (ADS)

Peng, Dong; Du, Yang; Shi, Yiwen; Mao, Duo; Jia, Xiaohua; Li, Hui; Zhu, Yukun; Wang, Kun; Tian, Jie

2016-07-01

Photoacoustic imaging and fluorescence molecular imaging are emerging as important research tools for biomedical studies. Photoacoustic imaging offers both strong optical absorption contrast and high ultrasonic resolution, and fluorescence molecular imaging provides excellent superficial resolution, high sensitivity, high throughput, and the ability for real-time imaging. Therefore, combining the imaging information of both modalities can provide comprehensive in vivo physiological and pathological information. However, currently there are limited probes available that can realize both fluorescence and photoacoustic imaging, and advanced biomedical applications for applying this dual-modality imaging approach remain underexplored. In this study, we developed a dual-modality photoacoustic-fluorescence imaging nanoprobe, ICG-loaded Au@SiO2, which was uniquely designed, consisting of gold nanorod cores and indocyanine green with silica shell spacer layers to overcome fluorophore quenching. This nanoprobe was examined by both PAI and FMI for in vivo imaging on tumor and ischemia mouse models. Our results demonstrated that the nanoparticles can specifically accumulate at the tumor and ischemic areas and be detected by both imaging modalities. Moreover, this dual-modality imaging strategy exhibited superior advantages for a precise diagnosis in different scenarios. The new nanoprobe with the dual-modality imaging approach holds great potential for diagnosis and stage classification of tumor and ischemia related diseases.Photoacoustic imaging and fluorescence molecular imaging are emerging as important research tools for biomedical studies. Photoacoustic imaging offers both strong optical absorption contrast and high ultrasonic resolution, and fluorescence molecular imaging provides excellent superficial resolution, high sensitivity, high throughput, and the ability for real-time imaging. Therefore, combining the imaging information of both modalities can provide comprehensive in vivo physiological and pathological information. However, currently there are limited probes available that can realize both fluorescence and photoacoustic imaging, and advanced biomedical applications for applying this dual-modality imaging approach remain underexplored. In this study, we developed a dual-modality photoacoustic-fluorescence imaging nanoprobe, ICG-loaded Au@SiO2, which was uniquely designed, consisting of gold nanorod cores and indocyanine green with silica shell spacer layers to overcome fluorophore quenching. This nanoprobe was examined by both PAI and FMI for in vivo imaging on tumor and ischemia mouse models. Our results demonstrated that the nanoparticles can specifically accumulate at the tumor and ischemic areas and be detected by both imaging modalities. Moreover, this dual-modality imaging strategy exhibited superior advantages for a precise diagnosis in different scenarios. The new nanoprobe with the dual-modality imaging approach holds great potential for diagnosis and stage classification of tumor and ischemia related diseases. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr03809c

Integrated Imaging and Vision Techniques for Industrial Inspection: A Special Issue on Machine Vision and Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Zheng; Ukida, H.; Ramuhalli, Pradeep

2010-06-05

Imaging- and vision-based techniques play an important role in industrial inspection. The sophistication of the techniques assures high- quality performance of the manufacturing process through precise positioning, online monitoring, and real-time classification. Advanced systems incorporating multiple imaging and/or vision modalities provide robust solutions to complex situations and problems in industrial applications. A diverse range of industries, including aerospace, automotive, electronics, pharmaceutical, biomedical, semiconductor, and food/beverage, etc., have benefited from recent advances in multi-modal imaging, data fusion, and computer vision technologies. Many of the open problems in this context are in the general area of image analysis methodologies (preferably in anmore » automated fashion). This editorial article introduces a special issue of this journal highlighting recent advances and demonstrating the successful applications of integrated imaging and vision technologies in industrial inspection.« less

Anatomy for Biomedical Engineers

ERIC Educational Resources Information Center

Carmichael, Stephen W.; Robb, Richard A.

2008-01-01

There is a perceived need for anatomy instruction for graduate students enrolled in a biomedical engineering program. This appeared especially important for students interested in and using medical images. These students typically did not have a strong background in biology. The authors arranged for students to dissect regions of the body that…

Bond-selective photoacoustic imaging by converting molecular vibration into acoustic waves

PubMed Central

Hui, Jie; Li, Rui; Phillips, Evan H.; Goergen, Craig J.; Sturek, Michael; Cheng, Ji-Xin

2016-01-01

The quantized vibration of chemical bonds provides a way of detecting specific molecules in a complex tissue environment. Unlike pure optical methods, for which imaging depth is limited to a few hundred micrometers by significant optical scattering, photoacoustic detection of vibrational absorption breaks through the optical diffusion limit by taking advantage of diffused photons and weak acoustic scattering. Key features of this method include both high scalability of imaging depth from a few millimeters to a few centimeters and chemical bond selectivity as a novel contrast mechanism for photoacoustic imaging. Its biomedical applications spans detection of white matter loss and regeneration, assessment of breast tumor margins, and diagnosis of vulnerable atherosclerotic plaques. This review provides an overview of the recent advances made in vibration-based photoacoustic imaging and various biomedical applications enabled by this new technology. PMID:27069873

Micro/Nanostructured Films and Adhesives for Biomedical Applications.

PubMed

Lee, Jungkyu K; Kang, Sung Min; Yang, Sung Ho; Cho, Woo Kyung

2015-12-01

The advanced technologies available for micro/nanofabrication have opened new avenues for interdisciplinary approaches to solve the unmet medical needs of regenerative medicine and biomedical devices. This review highlights the recent developments in micro/nanostructured adhesives and films for biomedical applications, including waterproof seals for wounds or surgery sites, drug delivery, sensing human body signals, and optical imaging of human tissues. We describe in detail the fabrication processes required to prepare the adhesives and films, such as tape-based adhesives, nanofilms, and flexible and stretchable film-based electronic devices. We also discuss their biomedical functions, performance in vitro and in vivo, and the future research needed to improve the current systems.

Gram-Schmidt orthonormalization for retrieval of amplitude images under sinusoidal patterns of illumination

USDA-ARS?s Scientific Manuscript database

Structured illumination using sinusoidal patterns has been utilized for optical imaging of biological tissues in biomedical research and, of horticultural products. Implementation of structured-illumination imaging relies on retrieval of amplitude images, which is conventionally achieved by a phase-...

K-edge subtraction synchrotron X-ray imaging in bio-medical research.

PubMed

Thomlinson, W; Elleaume, H; Porra, L; Suortti, P

2018-05-01

High contrast in X-ray medical imaging, while maintaining acceptable radiation dose levels to the patient, has long been a goal. One of the most promising methods is that of K-edge subtraction imaging. This technique, first advanced as long ago as 1953 by B. Jacobson, uses the large difference in the absorption coefficient of elements at energies above and below the K-edge. Two images, one taken above the edge and one below the edge, are subtracted leaving, ideally, only the image of the distribution of the target element. This paper reviews the development of the KES techniques and technology as applied to bio-medical imaging from the early low-power tube sources of X-rays to the latest high-power synchrotron sources. Applications to coronary angiography, functional lung imaging and bone growth are highlighted. A vision of possible imaging with new compact sources is presented. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals

PubMed Central

Dethlefs, Christopher R.; Piotrowicz, Justin; Van Avermaete, Tony; Maki, Jeff; Gerstler, Steve; Leevy, W. M.

2017-01-01

Biomedical imaging modalities like computed tomography (CT) and magnetic resonance (MR) provide excellent platforms for collecting three-dimensional data sets of patient or specimen anatomy in clinical or preclinical settings. However, the use of a virtual, on-screen display limits the ability of these tomographic images to fully convey the anatomical information embedded within. One solution is to interface a biomedical imaging data set with 3D printing technology to generate a physical replica. Here we detail a complementary method to visualize tomographic imaging data with a hand-held model: Sub Surface Laser Engraving (SSLE) of crystal glass. SSLE offers several unique benefits including: the facile ability to include anatomical labels, as well as a scale bar; streamlined multipart assembly of complex structures in one medium; high resolution in the X, Y, and Z planes; and semi-transparent shells for visualization of internal anatomical substructures. Here we demonstrate the process of SSLE with CT data sets derived from pre-clinical and clinical sources. This protocol will serve as a powerful and inexpensive new tool with which to visualize complex anatomical structures for scientists and students in a number of educational and research settings. PMID:28518066

Are we studying what matters? Health priorities and NIH-funded biomedical engineering research.

PubMed

Rubin, Jessica B; Paltiel, A David; Saltzman, W Mark

2010-07-01

With the founding of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) in 1999, the National Institutes of Health (NIH) made explicit its dedication to expanding research in biomedical engineering. Ten years later, we sought to examine how closely federal funding for biomedical engineering aligns with U.S. health priorities. Using a publicly accessible database of research projects funded by the NIH in 2008, we identified 641 grants focused on biomedical engineering, 48% of which targeted specific diseases. Overall, we found that these disease-specific NIH-funded biomedical engineering research projects align with national health priorities, as quantified by three commonly utilized measures of disease burden: cause of death, disability-adjusted survival losses, and expenditures. However, we also found some illnesses (e.g., cancer and heart disease) for which the number of research projects funded deviated from our expectations, given their disease burden. Our findings suggest several possibilities for future studies that would serve to further inform the allocation of limited research dollars within the field of biomedical engineering.

Perspective on nanoparticle technology for biomedical use

PubMed Central

Raliya, Ramesh; Chadha, Tandeep Singh; Hadad, Kelsey; Biswas, Pratim

2016-01-01

This review gives a short overview on the widespread use of nanostructured and nanocomposite materials for disease diagnostics, drug delivery, imaging and biomedical sensing applications. Nanoparticle interaction with a biological matrix/entity is greatly influenced by its morphology, crystal phase, surface chemistry, functionalization, physicochemical and electronic properties of the particle. Various nanoparticle synthesis routes, characteristization, and functionalization methodologies to be used for biomedical applications ranging from drug delivery to molecular probing of underlying mechanisms and concepts are described with several examples (150 references). PMID:26951098

Image Decoding of Photonic Crystal Beads Array in the Microfluidic Chip for Multiplex Assays

PubMed Central

Yuan, Junjie; Zhao, Xiangwei; Wang, Xiaoxia; Gu, Zhongze

2014-01-01

Along with the miniaturization and intellectualization of biomedical instruments, the increasing demand of health monitoring at anywhere and anytime elevates the need for the development of point of care testing (POCT). Photonic crystal beads (PCBs) as one kind of good encoded microcarriers can be integrated with microfluidic chips in order to realize cost-effective and high sensitive multiplex bioassays. However, there are difficulties in analyzing them towards automated analysis due to the characters of the PCBs and the unique detection manner. In this paper, we propose a strategy to take advantage of automated image processing for the color decoding of the PCBs array in the microfluidic chip for multiplex assays. By processing and alignment of two modal images of epi-fluorescence and epi-white light, every intact bead in the image is accurately extracted and decoded by PC colors, which stand for the target species. This method, which shows high robustness and accuracy under various configurations, eliminates the high hardware requirement of spectroscopy analysis and user-interaction software, and provides adequate supports for the general automated analysis of POCT based on PCBs array. PMID:25341876

Blackboard architecture for medical image interpretation

NASA Astrophysics Data System (ADS)

Davis, Darryl N.; Taylor, Christopher J.

1991-06-01

There is a growing interest in using sophisticated knowledge-based systems for biomedical image interpretation. We present a principled attempt to use artificial intelligence methodologies in interpreting lateral skull x-ray images. Such radiographs are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error prone and previous attempts to automate this analysis typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and image quality. An integrated model-based system has been developed which makes use of a blackboard architecture and multiple knowledge sources. A model definition interface allows quantitative models, of feature appearance and location, to be built from examples as well as more qualitative modelling constructs. Visual task definition and blackboard control modules allow task-specific knowledge sources to act on information available to the blackboard in a hypothesise and test reasoning cycle. Further knowledge-based modules include object selection, location hypothesis, intelligent segmentation, and constraint propagation systems. Alternative solutions to given tasks are permitted.

Multi-Atlas Segmentation of Biomedical Images: A Survey

PubMed Central

Iglesias, Juan Eugenio; Sabuncu, Mert R.

2015-01-01

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation. PMID:26201875

Smart Cancer Cell Targeting Imaging and Drug Delivery System by Systematically Engineering Periodic Mesoporous Organosilica Nanoparticles.

PubMed

Lu, Nan; Tian, Ying; Tian, Wei; Huang, Peng; Liu, Ying; Tang, Yuxia; Wang, Chunyan; Wang, Shouju; Su, Yunyan; Zhang, Yunlei; Pan, Jing; Teng, Zhaogang; Lu, Guangming

2016-02-10

The integration of diagnosis and therapy into one nanoplatform, known as theranostics, has attracted increasing attention in the biomedical areas. Herein, we first present a cancer cell targeting imaging and drug delivery system based on engineered thioether-bridged periodic mesoporous organosilica nanoparticles (PMOs). The PMOs are stably and selectively conjugated with near-infrared fluorescence (NIRF) dye Cyanine 5.5 (Cy5.5) and anti-Her2 affibody on the outer surfaces to endow them with excellent NIRF imaging and cancer targeting properties. Also, taking the advantage of the thioether-group-incorporated mesopores, the release of chemotherapy drug doxorubicin (DOX) loaded in the PMOs is responsive to the tumor-related molecule glutathione (GSH). The drug release percentage reaches 84.8% in 10 mM of GSH solution within 24 h, which is more than 2-fold higher than that without GSH. In addition, the drug release also exhibits pH-responsive, which reaches 53.6% at pH 5 and 31.7% at pH 7.4 within 24 h. Confocal laser scanning microscopy and flow cytometry analysis demonstrate that the PMOs-based theranostic platforms can efficiently target to and enter Her2 positive tumor cells. Thus, the smart imaging and drug delivery nanoplatforms induce high tumor cell growth inhibition. Meanwhile, the Cy5.5 conjugated PMOs perform great NIRF imaging ability, which could monitor the intracellular distribution, delivery and release of the chemotherapy drug. In addition, cell viability and histological assessments show the engineered PMOs have good biocompatibility, further encouraging the following biomedical applications. Over all, the systemically engineered PMOs can serve as a novel cancer cell targeting imaging and drug delivery platform with NIRF imaging, GSH and pH dual-responsive drug release, and high tumor cell targeting ability.

Increased transverse relaxivity in ultrasmall superparamagnetic iron oxide nanoparticles used as MRI contrast agent for biomedical imaging.

PubMed

Mishra, Sushanta Kumar; Kumar, B S Hemanth; Khushu, Subash; Tripathi, Rajendra P; Gangenahalli, Gurudutta

2016-09-01

Synthesis of a contrast agent for biomedical imaging is of great interest where magnetic nanoparticles are concerned, because of the strong influence of particle size on transverse relaxivity. In the present study, biocompatible magnetic iron oxide nanoparticles were synthesized by co-precipitation of Fe 2+ and Fe 3+ salts, followed by surface adsorption with reduced dextran. The synthesized nanoparticles were spherical in shape, and 12 ± 2 nm in size as measured using transmission electron microscopy; this was corroborated with results from X-ray diffraction and dynamic light scattering studies. The nanoparticles exhibited superparamagnetic behavior, superior T 2 relaxation rate and high relaxivities (r 1  = 18.4 ± 0.3, r 2  = 90.5 ± 0.8 s -1 mM -1 , at 7 T). MR image analysis of animals before and after magnetic nanoparticle administration revealed that the signal intensity of tumor imaging, specific organ imaging and whole body imaging can be clearly distinguished, due to the strong relaxation properties of these nanoparticles. Very low concentrations (3.0 mg Fe/kg body weight) of iron oxides are sufficient for early detection of tumors, and also have a clear distinction in pre- and post-enhancement of contrast in organs and body imaging. Many investigators have demonstrated high relaxivities of magnetic nanoparticles at superparamagnetic iron oxide level above 50 nm, but this investigation presents a satisfactory, ultrasmall, superparamagnetic and high transverse relaxivity negative contrast agent for diagnosis in pre-clinical studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Design and applications of a multimodality image data warehouse framework.

PubMed

Wong, Stephen T C; Hoo, Kent Soo; Knowlton, Robert C; Laxer, Kenneth D; Cao, Xinhau; Hawkins, Randall A; Dillon, William P; Arenson, Ronald L

2002-01-01

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications--namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.

Design and Applications of a Multimodality Image Data Warehouse Framework

PubMed Central

Wong, Stephen T.C.; Hoo, Kent Soo; Knowlton, Robert C.; Laxer, Kenneth D.; Cao, Xinhau; Hawkins, Randall A.; Dillon, William P.; Arenson, Ronald L.

2002-01-01

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications—namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains. PMID:11971885

In vitro and ex vivo evaluation of silica-coated super paramagnetic iron oxide nanoparticles (SPION) as biomedical photoacoustic contrast agent

NASA Astrophysics Data System (ADS)

Alwi, Rudolf; Telenkov, Sergey A.; Mandelis, Andreas; Leshuk, Timothy; Gu, Frank; Oladepo, Sulayman; Michaelian, Kirk; Dickie, Kristopher

2013-03-01

The employment of contrast agents in photoacoustic imaging has gained significant attention within the past few years for their biomedical applications. In this study, the use of silica-coated superparamagnetic iron oxide (Fe3O4) nanoparticles (SPION) was investigated as a contrast agent in biomedical photoacoustic imaging. SPIONs have been widely used as Food-and-Drug-Administration (FDA)-approved contrast agents for magnetic resonance imaging (MRI) and are known to have an excellent safety profile. Using our frequency-domain photoacoustic correlation technique ("the photoacoustic radar") with modulated laser excitation, we examined the effects of nanoparticle size, concentration and biological medium (e.g. serum, sheep blood) on its photoacoustic response in turbid media (intralipid solution). Maximum detection depth and minimum measurable SPION concentration were determined experimentally. The detection was performed using a single element transducer. The nanoparticle-induced optical contrast ex vivo in dense muscular tissues (avian pectus) was evaluated using a phased array photoacoustic probe and the strong potential of silicacoated SPION as a possible photoacoustic contrast agent was demonstrated. This study opens the way for future clinical applications of nanoparticle-enhanced photoacoustic imaging in cancer therapy.

National Institute of Biomedical Imaging and Bioengineering Point-of-Care Technology Research Network: Advancing Precision Medicine

PubMed Central

Ford Carleton, Penny; Parrish, John A.; Collins, John M.; Crocker, J. Benjamin; Dixon, Ronald F.; Edgman-Levitan, Susan; Lewandrowski, Kent B.; Stahl, James E.; Klapperich, Catherine; Cabodi, Mario; Gaydos, Charlotte A.; Rompalo, Anne M.; Manabe, Yukari; Wang, Tza-Huei; Rothman, Richard; Geddes, Chris D.; Widdice, Lea; Jackman, Joany; Mathura, Rishi A.; Lash, Tiffani Bailey

2016-01-01

To advance the development of point-of-care technology (POCT), the National Institute of Biomedical Imaging and Bioengineering established the POCT Research Network (POCTRN), comprised of Centers that emphasize multidisciplinary partnerships and close facilitation to move technologies from an early stage of development into clinical testing and patient use. This paper describes the POCTRN and the three currently funded Centers as examples of academic-based organizations that support collaborations across disciplines, institutions, and geographic regions to successfully drive innovative solutions from concept to patient care. PMID:27730014

Web tools for large-scale 3D biological images and atlases

PubMed Central

2012-01-01

Background Large-scale volumetric biomedical image data of three or more dimensions are a significant challenge for distributed browsing and visualisation. Many images now exceed 10GB which for most users is too large to handle in terms of computer RAM and network bandwidth. This is aggravated when users need to access tens or hundreds of such images from an archive. Here we solve the problem for 2D section views through archive data delivering compressed tiled images enabling users to browse through very-large volume data in the context of a standard web-browser. The system provides an interactive visualisation for grey-level and colour 3D images including multiple image layers and spatial-data overlay. Results The standard Internet Imaging Protocol (IIP) has been extended to enable arbitrary 2D sectioning of 3D data as well a multi-layered images and indexed overlays. The extended protocol is termed IIP3D and we have implemented a matching server to deliver the protocol and a series of Ajax/Javascript client codes that will run in an Internet browser. We have tested the server software on a low-cost linux-based server for image volumes up to 135GB and 64 simultaneous users. The section views are delivered with response times independent of scale and orientation. The exemplar client provided multi-layer image views with user-controlled colour-filtering and overlays. Conclusions Interactive browsing of arbitrary sections through large biomedical-image volumes is made possible by use of an extended internet protocol and efficient server-based image tiling. The tools open the possibility of enabling fast access to large image archives without the requirement of whole image download and client computers with very large memory configurations. The system was demonstrated using a range of medical and biomedical image data extending up to 135GB for a single image volume. PMID:22676296

Mechanisms of Intraductal Tumor Spread

DTIC Science & Technology

2004-08-01

59 (2): 119-127, 2002. "• "A geometric model for image analysis in cytology" Ortiz de Solorzano C., R . Malladi , Lockett S. In: Geometric methods in...gland tissue sections". Fernandez-Gonzalez R ., Deschamps T., Idica A.K., Malladi R ., Ortiz de Solorzano C. Journal of Biomedical Optics 9(3):445-453...normal and neoplastic mammary gland tissue sections". Fernandez-Gonzalez R ., Deschamps T., Idica A.K., Malladi R ., Ortiz de Solorzano C., Proceedings

Image stacking approach to increase sensitivity of fluorescence detection using a low cost complementary metal-oxide-semiconductor (CMOS) webcam.

PubMed

Balsam, Joshua; Bruck, Hugh Alan; Kostov, Yordan; Rasooly, Avraham

2012-01-01

Optical technologies are important for biological analysis. Current biomedical optical analyses rely on high-cost, high-sensitivity optical detectors such as photomultipliers, avalanched photodiodes or cooled CCD cameras. In contrast, Webcams, mobile phones and other popular consumer electronics use lower-sensitivity, lower-cost optical components such as photodiodes or CMOS sensors. In order for consumer electronics devices, such as webcams, to be useful for biomedical analysis, they must have increased sensitivity. We combined two strategies to increase the sensitivity of CMOS-based fluorescence detector. We captured hundreds of low sensitivity images using a Webcam in video mode, instead of a single image typically used in cooled CCD devices.We then used a computational approach consisting of an image stacking algorithm to remove the noise by combining all of the images into a single image. While video mode is widely used for dynamic scene imaging (e.g. movies or time-lapse photography), it is not used to capture a single static image, which removes noise and increases sensitivity by more than thirty fold. The portable, battery-operated Webcam-based fluorometer system developed here consists of five modules: (1) a low cost CMOS Webcam to monitor light emission, (2) a plate to perform assays, (3) filters and multi-wavelength LED illuminator for fluorophore excitation, (4) a portable computer to acquire and analyze images, and (5) image stacking software for image enhancement. The samples consisted of various concentrations of fluorescein, ranging from 30 μM to 1000 μM, in a 36-well miniature plate. In the single frame mode, the fluorometer's limit-of-detection (LOD) for fluorescein is ∼1000 μM, which is relatively insensitive. However, when used in video mode combined with image stacking enhancement, the LOD is dramatically reduced to 30 μM, sensitivity which is similar to that of state-of-the-art ELISA plate photomultiplier-based readers. Numerous medical diagnostics assays rely on optical and fluorescence readers. Our novel combination of detection technologies, which is new to biodetection may enable the development of new low cost optical detectors based on an inexpensive Webcam (<$10). It has the potential to form the basis for high sensitivity, low cost medical diagnostics in resource-poor settings.

Image stacking approach to increase sensitivity of fluorescence detection using a low cost complementary metal-oxide-semiconductor (CMOS) webcam

PubMed Central

Balsam, Joshua; Bruck, Hugh Alan; Kostov, Yordan; Rasooly, Avraham

2013-01-01

Optical technologies are important for biological analysis. Current biomedical optical analyses rely on high-cost, high-sensitivity optical detectors such as photomultipliers, avalanched photodiodes or cooled CCD cameras. In contrast, Webcams, mobile phones and other popular consumer electronics use lower-sensitivity, lower-cost optical components such as photodiodes or CMOS sensors. In order for consumer electronics devices, such as webcams, to be useful for biomedical analysis, they must have increased sensitivity. We combined two strategies to increase the sensitivity of CMOS-based fluorescence detector. We captured hundreds of low sensitivity images using a Webcam in video mode, instead of a single image typically used in cooled CCD devices.We then used a computational approach consisting of an image stacking algorithm to remove the noise by combining all of the images into a single image. While video mode is widely used for dynamic scene imaging (e.g. movies or time-lapse photography), it is not used to capture a single static image, which removes noise and increases sensitivity by more than thirty fold. The portable, battery-operated Webcam-based fluorometer system developed here consists of five modules: (1) a low cost CMOS Webcam to monitor light emission, (2) a plate to perform assays, (3) filters and multi-wavelength LED illuminator for fluorophore excitation, (4) a portable computer to acquire and analyze images, and (5) image stacking software for image enhancement. The samples consisted of various concentrations of fluorescein, ranging from 30 μM to 1000 μM, in a 36-well miniature plate. In the single frame mode, the fluorometer's limit-of-detection (LOD) for fluorescein is ∼1000 μM, which is relatively insensitive. However, when used in video mode combined with image stacking enhancement, the LOD is dramatically reduced to 30 μM, sensitivity which is similar to that of state-of-the-art ELISA plate photomultiplier-based readers. Numerous medical diagnostics assays rely on optical and fluorescence readers. Our novel combination of detection technologies, which is new to biodetection may enable the development of new low cost optical detectors based on an inexpensive Webcam (<$10). It has the potential to form the basis for high sensitivity, low cost medical diagnostics in resource-poor settings. PMID:23990697

Biomedical image segmentation using geometric deformable models and metaheuristics.

PubMed

Mesejo, Pablo; Valsecchi, Andrea; Marrakchi-Kacem, Linda; Cagnoni, Stefano; Damas, Sergio

2015-07-01

This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities. Copyright © 2013 Elsevier Ltd. All rights reserved.

From Mars to man - Biomedical research at the Jet Propulsion Laboratory

NASA Technical Reports Server (NTRS)

Beckenbach, E. S.

1984-01-01

In the course of the unmanned exploration of the solar system, which the California Institute of Technology's Jet Propulsion Laboratory has managed for NASA, major advances in computerized image processing, materials research, and miniature electronics design have been accomplished. This presentation shows some of the imaging results from space exploration missions, as well as biomedical research tasks based in these technologies. Among other topics, the use of polymeric microspheres in cancer therapy is discussed. Also included are ceramic applications to prosthesis development, laser applications in the treatment of coronary artery disease, multispectral imaging as used in the diagnosis of thermal burn injury, and some examples of telemetry systems as they can be involved in biological systems.

Multifunctional fluorescent and magnetic nanoparticles for biomedical applications

NASA Astrophysics Data System (ADS)

Selvan, Subramanian T.

2012-03-01

Hybrid multifunctional nanoparticles (NPs) are emerging as useful probes for magnetic based targeting, delivery, cell separation, magnetic resonance imaging (MRI), and fluorescence-based bio-labeling applications. Assessing from the literature, the development of multifunctional NPs for multimodality imaging is still in its infancy state. This report focuses on our recent work on quantum dots (QDs), magnetic NPs (MNPs) and bi-functional NPs (composed of either QDs or rare-earth NPs, and magnetic NPs - iron oxide or gadolinium oxide) for multimodality imaging based biomedical applications. The combination of MRI and fluorescence would ally each other in improving the sensitivity and resolution, resulting in improved and early diagnosis of the disease. The challenges in this area are discussed.

NCI Visuals Online

Cancer.gov

NCI Visuals Online contains images from the collections of the National Cancer Institute's Office of Communications and Public Liaison, including general biomedical and science-related images, cancer-specific scientific and patient care-related images, and portraits of directors and staff of the National Cancer Institute.

Biomedical Applications of Zinc Oxide Nanomaterials

PubMed Central

Zhang, Yin; Nayak, Tapas R.; Hong, Hao; Cai, Weibo

2013-01-01

Nanotechnology has witnessed tremendous advancement over the last several decades. Zinc oxide (ZnO), which can exhibit a wide variety of nanostructures, possesses unique semiconducting, optical, and piezoelectric properties hence has been investigated for a wide variety of applications. One of the most important features of ZnO nanomaterials is low toxicity and biodegradability. Zn2+ is an indispensable trace element for adults (~10 mg of Zn2+ per day is recommended) and it is involved in various aspects of metabolism. Chemically, the surface of ZnO is rich in -OH groups, which can be readily functionalized by various surface decorating molecules. In this review article, we summarized the current status of the use of ZnO nanomaterials for biomedical applications, such as biomedical imaging (which includes fluorescence, magnetic resonance, positron emission tomography, as well as dual-modality imaging), drug delivery, gene delivery, and biosensing of a wide array of molecules of interest. Research in biomedical applications of ZnO nanomaterials will continue to flourish over the next decade, and much research effort will be needed to develop biocompatible/biodegradable ZnO nanoplatforms for potential clinical translation. PMID:24206130

The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications

PubMed Central

Casadevall, Arturo; Fang, Ferric C.

2016-01-01

ABSTRACT Inaccurate data in scientific papers can result from honest error or intentional falsification. This study attempted to determine the percentage of published papers that contain inappropriate image duplication, a specific type of inaccurate data. The images from a total of 20,621 papers published in 40 scientific journals from 1995 to 2014 were visually screened. Overall, 3.8% of published papers contained problematic figures, with at least half exhibiting features suggestive of deliberate manipulation. The prevalence of papers with problematic images has risen markedly during the past decade. Additional papers written by authors of papers with problematic images had an increased likelihood of containing problematic images as well. As this analysis focused only on one type of data, it is likely that the actual prevalence of inaccurate data in the published literature is higher. The marked variation in the frequency of problematic images among journals suggests that journal practices, such as prepublication image screening, influence the quality of the scientific literature. PMID:27273827

Figure Text Extraction in Biomedical Literature

PubMed Central

Kim, Daehyun; Yu, Hong

2011-01-01

Background Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. Methodology We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. Results/Conclusions The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search. PMID:21249186

Figure text extraction in biomedical literature.

PubMed

Kim, Daehyun; Yu, Hong

2011-01-13

Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search.

A philosophy for CNS radiotracer design

DOE PAGES

Van de Bittner, Genevieve C.; Ricq, Emily L.; Hooker, Jacob M.

2014-10-01

Decades after its discovery, positron emission tomography (PET) remains the premier tool for imaging neurochemistry in living humans. Technological improvements in radiolabeling methods, camera design, and image analysis have kept PET in the forefront. In addition, the use of PET imaging has expanded because researchers have developed new radiotracers that visualize receptors, transporters, enzymes, and other molecular targets within the human brain. However, of the thousands of proteins in the central nervous system (CNS), researchers have successfully imaged fewer than 40 human proteins. To address the critical need for new radiotracers, this Account expounds on the decisions, strategies, and pitfallsmore » of CNS radiotracer development based on our current experience in this area. We discuss the five key components of radiotracer development for human imaging: choosing a biomedical question, selection of a biological target, design of the radiotracer chemical structure, evaluation of candidate radiotracers, and analysis of preclinical imaging. It is particularly important to analyze the market of scientists or companies who might use a new radiotracer and carefully select a relevant biomedical question(s) for that audience. In the selection of a specific biological target, we emphasize how target localization and identity can constrain this process and discuss the optimal target density and affinity ratios needed for binding-based radiotracers. In addition, we discuss various PET test–retest variability requirements for monitoring changes in density, occupancy, or functionality for new radiotracers. In the synthesis of new radiotracer structures, high-throughput, modular syntheses have proved valuable, and these processes provide compounds with sites for late-stage radioisotope installation. As a result, researchers can manage the time constraints associated with the limited half-lives of isotopes. In order to evaluate brain uptake, a number of methods are available to predict bioavailability, blood–brain barrier (BBB) permeability, and the associated issues of nonspecific binding and metabolic stability. To evaluate the synthesized chemical library, researchers need to consider high-throughput affinity assays, the analysis of specific binding, and the importance of fast binding kinetics. Lastly, we describe how we initially assess preclinical radiotracer imaging, using brain uptake, specific binding, and preliminary kinetic analysis to identify promising radiotracers that may be useful for human brain imaging. Although we discuss these five design components separately and linearly in this Account, in practice we develop new PET-based radiotracers using these design components nonlinearly and iteratively to develop new compounds in the most efficient way possible.« less

A Philosophy for CNS Radiotracer Design

PubMed Central

2015-01-01

Conspectus Decades after its discovery, positron emission tomography (PET) remains the premier tool for imaging neurochemistry in living humans. Technological improvements in radiolabeling methods, camera design, and image analysis have kept PET in the forefront. In addition, the use of PET imaging has expanded because researchers have developed new radiotracers that visualize receptors, transporters, enzymes, and other molecular targets within the human brain. However, of the thousands of proteins in the central nervous system (CNS), researchers have successfully imaged fewer than 40 human proteins. To address the critical need for new radiotracers, this Account expounds on the decisions, strategies, and pitfalls of CNS radiotracer development based on our current experience in this area. We discuss the five key components of radiotracer development for human imaging: choosing a biomedical question, selection of a biological target, design of the radiotracer chemical structure, evaluation of candidate radiotracers, and analysis of preclinical imaging. It is particularly important to analyze the market of scientists or companies who might use a new radiotracer and carefully select a relevant biomedical question(s) for that audience. In the selection of a specific biological target, we emphasize how target localization and identity can constrain this process and discuss the optimal target density and affinity ratios needed for binding-based radiotracers. In addition, we discuss various PET test–retest variability requirements for monitoring changes in density, occupancy, or functionality for new radiotracers. In the synthesis of new radiotracer structures, high-throughput, modular syntheses have proved valuable, and these processes provide compounds with sites for late-stage radioisotope installation. As a result, researchers can manage the time constraints associated with the limited half-lives of isotopes. In order to evaluate brain uptake, a number of methods are available to predict bioavailability, blood–brain barrier (BBB) permeability, and the associated issues of nonspecific binding and metabolic stability. To evaluate the synthesized chemical library, researchers need to consider high-throughput affinity assays, the analysis of specific binding, and the importance of fast binding kinetics. Finally, we describe how we initially assess preclinical radiotracer imaging, using brain uptake, specific binding, and preliminary kinetic analysis to identify promising radiotracers that may be useful for human brain imaging. Although we discuss these five design components separately and linearly in this Account, in practice we develop new PET-based radiotracers using these design components nonlinearly and iteratively to develop new compounds in the most efficient way possible. PMID:25272291

Hot topics in biomedical ultrasound: ultrasound therapy and its integration with ultrasonic imaging

NASA Astrophysics Data System (ADS)

Everbach, E. Carr

2005-09-01

Since the development of biomedical ultrasound imaging from sonar after WWII, there has been a clear divide between ultrasonic imaging and ultrasound therapy. While imaging techniques are designed to cause as little change as possible in the tissues through which ultrasound propagates, ultrasound therapy typically relies upon heating or acoustic cavitation to produce a desirable therapeutic effect. Concerns over the increasingly high acoustic outputs of diagnostic ultrasound scanners prompted the adoption of the Mechanical Index (MI) and Thermal Index (TI) in the early 1990s. Therapeutic applications of ultrasound, meanwhile, have evolved from deep tissue heating in sports medicine to include targeted drug delivery, tumor and plaque ablation, cauterization via high intensity focused ultrasound (HIFU), and accelerated dissolution of blood clots. The integration of ultrasonic imaging and therapy in one device is just beginning, but the promise of improved patient outcomes is balanced by regulatory and practical impediments.

Polysaccharide-Coated Magnetic Nanoparticles for Imaging and Gene Therapy

PubMed Central

Uthaman, Saji; Cherukula, Kondareddy; Cho, Chong-Su; Park, In-Kyu

2015-01-01

Today, nanotechnology plays a vital role in biomedical applications, especially for the diagnosis and treatment of various diseases. Among the many different types of fabricated nanoparticles, magnetic metal oxide nanoparticles stand out as unique and useful tools for biomedical applications, because of their imaging characteristics and therapeutic properties such as drug and gene carriers. Polymer-coated magnetic particles are currently of particular interest to investigators in the fields of nanobiomedicine and fundamental biomaterials. Theranostic magnetic nanoparticles that are encapsulated or coated with polymers not only exhibit imaging properties in response to stimuli, but also can efficiently deliver various drugs and therapeutic genes. Even though a large number of polymer-coated magnetic nanoparticles have been fabricated over the last decade, most of these have only been used for imaging purposes. The focus of this review is on polysaccharide-coated magnetic nanoparticles used for imaging and gene delivery. PMID:26078971

Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

PubMed

Boix, Macarena; Cantó, Begoña

2013-04-01

Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

Investigation of skin structures based on infrared wave parameter indirect microscopic imaging

NASA Astrophysics Data System (ADS)

Zhao, Jun; Liu, Xuefeng; Xiong, Jichuan; Zhou, Lijuan

2017-02-01

Detailed imaging and analysis of skin structures are becoming increasingly important in modern healthcare and clinic diagnosis. Nanometer resolution imaging techniques such as SEM and AFM can cause harmful damage to the sample and cannot measure the whole skin structure from the very surface through epidermis, dermis to subcutaneous. Conventional optical microscopy has the highest imaging efficiency, flexibility in onsite applications and lowest cost in manufacturing and usage, but its image resolution is too low to be accepted for biomedical analysis. Infrared parameter indirect microscopic imaging (PIMI) uses an infrared laser as the light source due to its high transmission in skins. The polarization of optical wave through the skin sample was modulated while the variation of the optical field was observed at the imaging plane. The intensity variation curve of each pixel was fitted to extract the near field polarization parameters to form indirect images. During the through-skin light modulation and image retrieving process, the curve fitting removes the blurring scattering from neighboring pixels and keeps only the field variations related to local skin structures. By using the infrared PIMI, we can break the diffraction limit, bring the wide field optical image resolution to sub-200nm, in the meantime of taking advantage of high transmission of infrared waves in skin structures.

Multicriteria analysis using open-source data and software for the implementation of a centralized biomedical waste management system in a developing country (Guinea, Conakry).

NASA Astrophysics Data System (ADS)

Pérez Peña, José Vicente; Baldó, Mane; Acosta, Yarci; Verschueren, Laurent; Thibaud, Kenmognie; Bilivogui, Pépé; Jean-Paul Ngandu, Alain; Beavogui, Maoro

2017-04-01

In the last decade the increasing interest for public health has promoted specific regulations for the transport, storage, transformation and/or elimination of potentially toxic waste. A special concern should focus on the effective management of biomedical waste, due to the environmental and health risk associated with them. The first stage for the effective management these waste includes the selection of the best sites for the location of facilities for its storage and/or elimination. Best-site selection is accomplished by means of multi-criteria decision analyses (MCDA) that aim to minimize the social and environmental impact, and to maximize management efficiency. In this work we presented a methodology that uses open-source software and data to analyze the best location for the implantation of a centralized waste management system in a developing country (Guinea, Conakry). We applied an analytical hierarchy process (AHP) using different thematic layers such as land use (derived from up-to-date Sentinel 2 remote sensing images), soil type, distance and type of roads, hydrography, distance to dense populated areas, etc. Land-use data were derived from up-to-date Sentinel 2 remote sensing images, whereas roads and hydrography were obtained from the Open Street Map database and latter validated with administrative data. We performed the AHP analysis with the aid of QGIS open-software Geospatial Information System. This methodology is very effective for developing countries as it uses open-source software and data for the MCDA analysis, thus reducing costs in these first stages of the integrated analysis.

Leveraging Engineering of Indocyanine Green-Encapsulated Polymeric Nanocomposites for Biomedical Applications.

PubMed

Han, Ya-Hui; Kankala, Ranjith Kumar; Wang, Shi-Bin; Chen, Ai-Zheng

2018-05-24

In recent times, photo-induced therapeutics have attracted enormous interest from researchers due to such attractive properties as preferential localization, excellent tissue penetration, high therapeutic efficacy, and minimal invasiveness, among others. Numerous photosensitizers have been considered in combination with light to realize significant progress in therapeutics. Along this line, indocyanine green (ICG), a Food and Drug Administration (FDA)-approved near-infrared (NIR, >750 nm) fluorescent dye, has been utilized in various biomedical applications such as drug delivery, imaging, and diagnosis, due to its attractive physicochemical properties, high sensitivity, and better imaging view field. However, ICG still suffers from certain limitations for its utilization as a molecular imaging probe in vivo, such as concentration-dependent aggregation, poor in vitro aqueous stability and photodegradation due to various physicochemical attributes. To overcome these limitations, much research has been dedicated to engineering numerous multifunctional polymeric composites for potential biomedical applications. In this review, we aim to discuss ICG-encapsulated polymeric nanoconstructs, which are of particular interest in various biomedical applications. First, we emphasize some attractive properties of ICG (including physicochemical characteristics, optical properties, metabolic features, and other aspects) and some of its current limitations. Next, we aim to provide a comprehensive overview highlighting recent reports on various polymeric nanoparticles that carry ICG for light-induced therapeutics with a set of examples. Finally, we summarize with perspectives highlighting the significant outcome, and current challenges of these nanocomposites.

Nanoparticles for cancer imaging: The good, the bad, and the promise

PubMed Central

Chapman, Sandra; Dobrovolskaia, Marina; Farahani, Keyvan; Goodwin, Andrew; Joshi, Amit; Lee, Hakho; Meade, Thomas; Pomper, Martin; Ptak, Krzysztof; Rao, Jianghong; Singh, Ravi; Sridhar, Srinivas; Stern, Stephan; Wang, Andrew; Weaver, John B.; Woloschak, Gayle; Yang, Lily

2014-01-01

Summary Recent advances in molecular imaging and nanotechnology are providing new opportunities for biomedical imaging with great promise for the development of novel imaging agents. The unique optical, magnetic, and chemical properties of materials at the scale of nanometers allow the creation of imaging probes with better contrast enhancement, increased sensitivity, controlled biodistribution, better spatial and temporal information, multi-functionality and multi-modal imaging across MRI, PET, SPECT, and ultrasound. These features could ultimately translate to clinical advantages such as earlier detection, real time assessment of disease progression and personalized medicine. However, several years of investigation into the application of these materials to cancer research has revealed challenges that have delayed the successful application of these agents to the field of biomedical imaging. Understanding these challenges is critical to take full advantage of the benefits offered by nano-sized imaging agents. Therefore, this article presents the lessons learned and challenges encountered by a group of leading researchers in this field, and suggests ways forward to develop nanoparticle probes for cancer imaging. Published by Elsevier Ltd. PMID:25419228

Evaluation of illumination system uniformity for wide-field biomedical hyperspectral imaging

NASA Astrophysics Data System (ADS)

Sawyer, Travis W.; Siri Luthman, A.; E Bohndiek, Sarah

2017-04-01

Hyperspectral imaging (HSI) systems collect both spatial (morphological) and spectral (chemical) information from a sample. HSI can provide sensitive analysis for biological and medical applications, for example, simultaneously measuring reflectance and fluorescence properties of a tissue, which together with structural information could improve early cancer detection and tumour characterisation. Illumination uniformity is a critical pre-condition for quantitative data extraction from an HSI system. Non-uniformity can cause glare, specular reflection and unwanted shading, which negatively impact statistical analysis procedures used to extract abundance of different chemical species. Here, we model and evaluate several illumination systems frequently used in wide-field biomedical imaging to test their potential for HSI. We use the software LightTools and FRED. The analysed systems include: a fibre ring light; a light emitting diode (LED) ring; and a diffuse scattering dome. Each system is characterised for spectral, spatial, and angular uniformity, as well as transfer efficiency. Furthermore, an approach to measure uniformity using the Kullback-Leibler divergence (KLD) is introduced. The KLD is generalisable to arbitrary illumination shapes, making it an attractive approach for characterising illumination distributions. Although the systems are quite comparable in their spatial and spectral uniformity, the most uniform angular distribution is achieved using a diffuse scattering dome, yielding a contrast of 0.503 and average deviation of 0.303 over a ±60° field of view with a 3.9% model error in the angular domain. Our results suggest that conventional illumination sources can be applied in HSI, but in the case of low light levels, bespoke illumination sources may offer improved performance.

Nanoparticles for Biomedical Imaging: Fundamentals of Clinical Translation

PubMed Central

Choi, Hak Soo; Frangioni, John V.

2010-01-01

Because of their large size compared to small molecules, and their multi-functionality, nanoparticles (NPs) hold promise as biomedical imaging, diagnostic, and theragnostic agents. However, the key to their success hinges on a detailed understanding of their behavior after administration into the body. NP biodistribution, target binding, and clearance are a complex function of their physicochemical properties in serum, which include hydrodynamic diameter, solubility, stability, shape and flexibility, surface charge, composition, and formulation. Moreover, many materials used to construct NPs have real or potential toxicity, or may interfere with other medical tests. In this review, we discuss the design considerations that mediate NP behavior in the body and the fundamental principles that govern clinical translation. By analyzing those nanomaterials that have already received regulatory approval, most of which are actually therapeutic agents, we attempt to predict which types of NPs hold potential as diagnostic agents for biomedical imaging. Finally, using quantum dots as an example, we provide a framework for deciding whether an NP-based agent is the best choice for a particular clinical application. PMID:21084027

An intelligent monitoring and management system for cross-enterprise biomedical data sharing platform

NASA Astrophysics Data System (ADS)

Wang, Tusheng; Yang, Yuanyuan; Zhang, Jianguo

2013-03-01

In order to enable multiple disciplines of medical researchers, clinical physicians and biomedical engineers working together in a secured, efficient, and transparent cooperative environment, we had designed an e-Science platform for biomedical imaging research and application cross multiple academic institutions and hospitals in Shanghai by using grid-based or cloud-based distributed architecture and presented this work in SPIE Medical Imaging conference held in San Diego in 2012. However, when the platform integrates more and more nodes over different networks, the first challenge is that how to monitor and maintain all the hosts and services operating cross multiple academic institutions and hospitals in the e-Science platform, such as DICOM and Web based image communication services, messaging services and XDS ITI transaction services. In this presentation, we presented a system design and implementation of intelligent monitoring and management which can collect system resource status of every node in real time, alert when node or service failure occurs, and can finally improve the robustness, reliability and service continuity of this e-Science platform.

Expanding Imaging Capabilities for Microfluidics: Applicability of Darkfield Internal Reflection Illumination (DIRI) to Observations in Microfluidics

PubMed Central

Kawano, Yoshihiro; Otsuka, Chino; Sanzo, James; Higgins, Christopher; Nirei, Tatsuo; Schilling, Tobias; Ishikawa, Takuji

2015-01-01

Microfluidics is used increasingly for engineering and biomedical applications due to recent advances in microfabrication technologies. Visualization of bubbles, tracer particles, and cells in a microfluidic device is important for designing a device and analyzing results. However, with conventional methods, it is difficult to observe the channel geometry and such particles simultaneously. To overcome this limitation, we developed a Darkfield Internal Reflection Illumination (DIRI) system that improved the drawbacks of a conventional darkfield illuminator. This study was performed to investigate its utility in the field of microfluidics. The results showed that the developed system could clearly visualize both microbubbles and the channel wall by utilizing brightfield and DIRI illumination simultaneously. The methodology is useful not only for static phenomena, such as clogging, but also for dynamic phenomena, such as the detection of bubbles flowing in a channel. The system was also applied to simultaneous fluorescence and DIRI imaging. Fluorescent tracer beads and channel walls were observed clearly, which may be an advantage for future microparticle image velocimetry (μPIV) analysis, especially near a wall. Two types of cell stained with different colors, and the channel wall, can be recognized using the combined confocal and DIRI system. Whole-slide imaging was also conducted successfully using this system. The tiling function significantly expands the observing area of microfluidics. The developed system will be useful for a wide variety of engineering and biomedical applications for the growing field of microfluidics. PMID:25748425

Expanding imaging capabilities for microfluidics: applicability of darkfield internal reflection illumination (DIRI) to observations in microfluidics.

PubMed

Kawano, Yoshihiro; Otsuka, Chino; Sanzo, James; Higgins, Christopher; Nirei, Tatsuo; Schilling, Tobias; Ishikawa, Takuji

2015-01-01

Microfluidics is used increasingly for engineering and biomedical applications due to recent advances in microfabrication technologies. Visualization of bubbles, tracer particles, and cells in a microfluidic device is important for designing a device and analyzing results. However, with conventional methods, it is difficult to observe the channel geometry and such particles simultaneously. To overcome this limitation, we developed a Darkfield Internal Reflection Illumination (DIRI) system that improved the drawbacks of a conventional darkfield illuminator. This study was performed to investigate its utility in the field of microfluidics. The results showed that the developed system could clearly visualize both microbubbles and the channel wall by utilizing brightfield and DIRI illumination simultaneously. The methodology is useful not only for static phenomena, such as clogging, but also for dynamic phenomena, such as the detection of bubbles flowing in a channel. The system was also applied to simultaneous fluorescence and DIRI imaging. Fluorescent tracer beads and channel walls were observed clearly, which may be an advantage for future microparticle image velocimetry (μPIV) analysis, especially near a wall. Two types of cell stained with different colors, and the channel wall, can be recognized using the combined confocal and DIRI system. Whole-slide imaging was also conducted successfully using this system. The tiling function significantly expands the observing area of microfluidics. The developed system will be useful for a wide variety of engineering and biomedical applications for the growing field of microfluidics.

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD).

PubMed

Jiang, Xiangying; Ringwald, Martin; Blake, Judith; Shatkay, Hagit

2017-01-01

The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. www.informatics.jax.org. © The Author(s) 2017. Published by Oxford University Press.

Diagnostic imaging applications; Proceedings of the Meeting, Amsterdam, Netherlands, October 8, 9, 1984

NASA Technical Reports Server (NTRS)

Beckenbach, E. S. (Editor)

1984-01-01

It is more important than ever that engineers have an understanding of the future needs of clinical and research medicine, and that physicians know somthing about probable future developments in instrumentation capabilities. Only by maintaining such a dialog can the most effective application of technological advances to medicine be achieved. This workshop attempted to provide this kind of information transfer in the limited field of diagnostic imaging. Biomedical research at the Jet Propulsion Laboratory is discussed, taking into account imaging results from space exploration missions, as well as biomedical research tasks based in these technologies. Attention is also given to current and future indications for magnetic resonance in medicine, high speed quantitative digital microscopy, computer processing of radiographic images, computed tomography and its modern applications, position emission tomography, and developments related to medical ultrasound.

Advances in Biomedical Imaging, Bioengineering, and Related Technologies for the Development of Biomarkers of Pancreatic Disease: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases and National Institute of Biomedical Imaging and Bioengineering Workshop

PubMed Central

Kelly, Kimberly A.; Hollingsworth, Michael A.; Brand, Randall E.; Liu, Christina H.; Singh, Vikesh K.; Srivastava, Sudhir; Wasan, Ajay D.; Yadav, Dhiraj; Andersen, Dana K.

2015-01-01

A workshop sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases and the National Institute of Biomedical Imaging and Bioengineering focused on research gaps and opportunities in the development of new biomarkers of pancreatic disease. The session was held on July 22, 2015, and structured into six sessions: 1) introduction and overview, 2) keynote address, 3) new approaches to the diagnosis of chronic pancreatitis, 4) biomarkers of pain and inflammation, 5) new approaches to the detection of pancreatic cancer, and 6) shed exosomes, shed cells, and shed proteins. Recent advances in the fields of pancreatic imaging, functional markers of pancreatic disease, proteomics, molecular and cellular imaging, and detection of circulating cancer cells and exosomes were reviewed. Knowledge gaps and research needs were highlighted. The development of new methods for the non-invasive determination of pancreatic pathology, the use of cellular markers of pancreatic function, inflammation, pain, and malignancy, and the refinement of methods to identify cells and cellular constituents of pancreatic cancer were discussed. The further refinement of sophisticated technical methods, and the need for clinical studies to validate these new approaches in large-scale studies of patients at risk for the development of pancreatic disease was repeatedly emphasized. PMID:26465948

Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies.

PubMed

Keane, Pearse A; Grossi, Carlota M; Foster, Paul J; Yang, Qi; Reisman, Charles A; Chan, Kinpui; Peto, Tunde; Thomas, Dhanes; Patel, Praveen J

2016-01-01

To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging.

Optical Coherence Tomography in the UK Biobank Study – Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies

PubMed Central

Grossi, Carlota M.; Foster, Paul J.; Yang, Qi; Reisman, Charles A.; Chan, Kinpui; Peto, Tunde; Thomas, Dhanes; Patel, Praveen J.

2016-01-01

Purpose To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. Methods In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available “spectral domain” OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. Results 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. Conclusions We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging. PMID:27716837

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Measurement Marker Recognition In A Time Sequence Of Infrared Images For Biomedical Applications

NASA Astrophysics Data System (ADS)

Fiorini, A. R.; Fumero, R.; Marchesi, R.

1986-03-01

In thermographic measurements, quantitative surface temperature evaluation is often uncertain. The main reason is in the lack of available reference points in transient conditions. Reflective markers were used for automatic marker recognition and pixel coordinate computations. An algorithm selects marker icons to match marker references where particular luminance conditions are satisfied. Automatic marker recognition allows luminance compensation and temperature calibration of recorded infrared images. A biomedical application is presented: the dynamic behaviour of the surface temperature distributions is investigated in order to study the performance of two different pumping systems for extracorporeal circulation. Sequences of images are compared and results are discussed. Finally, the algorithm allows to monitor the experimental environment and to alert for the presence of unusual experimental conditions.

Spectroscopic magnetic resonance imaging of the brain: voxel localisation and tissue segmentation in the follow up of brain tumour.

PubMed

Poloni, Guy; Bastianello, S; Vultaggio, Angela; Pozzi, S; Maccabelli, Gloria; Germani, Giancarlo; Chiarati, Patrizia; Pichiecchio, Anna

2008-01-01

The field of application of magnetic resonance spectroscopy (MRS) in biomedical research is expanding all the time and providing opportunities to investigate tissue metabolism and function. The data derived can be integrated with the information on tissue structure gained from conventional and non-conventional magnetic resonance imaging (MRI) techniques. Clinical MRS is also strongly expected to play an important role as a diagnostic tool. Essential for the future success of MRS as a clinical and research tool in biomedical sciences, both in vivo and in vitro, is the development of an accurate, biochemically relevant and physically consistent and reliable data analysis standard. Stable and well established analysis algorithms, in both the time and the frequency domain, are already available, as is free commercial software for implementing them. In this study, we propose an automatic algorithm that takes into account anatomical localisation, relative concentrations of white matter, grey matter, cerebrospinal fluid and signal abnormalities and inter-scan patient movement. The endpoint is the collection of a series of covariates that could be implemented in a multivariate analysis of covariance (MANCOVA) of the MRS data, as a tool for dealing with differences that may be ascribed to the anatomical variability of the subjects, to inaccuracies in the localisation of the voxel or slab, or to movement, rather than to the pathology under investigation. The aim was to develop an analysis procedure that can be consistently and reliably applied in the follow up of brain tumour. In this study, we demonstrate that the inclusion of such variables in the data analysis of quantitative MRS is fundamentally important (especially in view of the reduced accuracy typical of MRS measures compared to other MRI techniques), reducing the occurrence of false positives.

Instrumentation of Molecular Imaging on Site-Specific Targeting Fluorescent Peptide for Early Detection of Breast Cancer

NASA Astrophysics Data System (ADS)

Yu, Ping; Ma, Lixin

2012-02-01

In this work we developed two biomedical imaging techniques for early detection of breast cancer. Both image modalities provide molecular imaging capability to probe site-specific targeting dyes. The first technique, heterodyne CCD fluorescence mediated tomography, is a non-invasive biomedical imaging that uses fluorescent photons from the targeted dye on the tumor cells inside human breast tissue. The technique detects a large volume of tissue (20 cm) with a moderate resolution (1 mm) and provides the high sensitivity. The second technique, dual-band spectral-domain optical coherence tomography, is a high-resolution tissue imaging modality. It uses a low coherence interferometer to detect coherent photons hidden in the incoherent background. Due to the coherence detection, a high resolution (20 microns) is possible. We have finished prototype imaging systems for the development of both image modalities and performed imaging experiments on tumor tissues. The spectroscopic/tomographic images show contrasts of dense tumor tissues and tumor necrotic regions. In order to correlate the findings from our results, a diffusion-weighted magnetic resonance imaging (MRI) of the tumors was performed using a small animal 7-Telsa MRI and demonstrated excellent agreement.

Trends in Biomedical Education.

ERIC Educational Resources Information Center

Peppas, Nicholas A.; Mallinson, Richard G.

1982-01-01

An analysis of trends in biomedical education within chemical education is presented. Data used for the analysis included: type/level of course, subjects taught, and textbook preferences. Results among others of the 1980 survey indicate that 28 out of 79 schools responding offer at least one course in biomedical engineering. (JN)

Segmentation and learning in the quantitative analysis of microscopy images

NASA Astrophysics Data System (ADS)

Ruggiero, Christy; Ross, Amy; Porter, Reid

2015-02-01

In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

77 FR 37684 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-22

... Imaging and Bioengineering Special Emphasis Panel; Center for Multiscale Simulations in the Human....D., Scientific Review Officer, 6707 Democracy Boulevard, Room 959, Bethesda, MD 20892, 301-451- 3398...

Fabrication of luminescent hydroxyapatite nanorods through surface-initiated RAFT polymerization: Characterization, biological imaging and drug delivery applications

NASA Astrophysics Data System (ADS)

Heng, Chunning; Zheng, Xiaoyan; Liu, Meiying; Xu, Dazhuang; Huang, Hongye; Deng, Fengjie; Hui, Junfeng; Zhang, Xiaoyong; Wei, Yen

2016-11-01

Hydroxyapatite nanomaterials as an important class of nanomaterials, have been widely applied for different biomedical applications for their excellent biocompatibility, biodegradation potential and low cost. In this work, hydroxyapatite nanorods with uniform size and morphology were prepared through hydrothermal synthesis. The surfaces of these hydroxyapatite nanorods are covered with hydrophobic oleic acid, making them poor dispersibility in aqueous solution and difficult for biomedical applications. To overcome this issue, a simple surface initiated polymerization strategy has been developed via combination of the surface ligand exchange and reversible addition fragmentation chain transfer (RAFT) polymerization. Hydroxyapatite nanorods were first modified with Riboflavin-5-phosphate sodium (RPSSD) via ligand exchange reaction between the phosphate group of RPSSD and oleic acid. Then hydroxyl group of nHAp-RPSSD was used to immobilize chain transfer agent, which was used as the initiator for surface-initiated RAFT polymerization. The nHAp-RPSSD-poly(IA-PEGMA) nanocomposites were characterized by means of 1H nuclear magnetic resonance, Fourier transform infrared spectroscopy, fluorescence spectroscopy and thermal gravimetric analysis in detailed. The biocompatibility, biological imaging and drug delivery of nHAp-RPSSD-poly(IA-PEGMA) were also investigated. Results showed that nHAp-RPSSD-poly(IA-PEGMA) exhibited excellent water dispersibility, desirable optical properties, good biocompatibility and high drug loading capability, making them promising candidates for biological imaging and controlled drug delivery applications.

Innovations in Nuclear Imaging Instrumentation: Cerenkov Imaging.

PubMed

Tamura, Ryo; Pratt, Edwin C; Grimm, Jan

2018-07-01

Cerenkov luminescence (CL) is blue glow light produced by charged subatomic particles travelling faster than the phase velocity of light in a dielectric medium such as water or tissue. CL was first discovered in 1934, but for biomedical research it was recognized only in 2009 after advances in optical camera sensors brought the required high sensitivity. Recently, applications of CL from clinical radionuclides have been rapidly expanding to include not only preclinical and clinical biomedical imaging but also an approach to therapy. Cerenkov Luminescence Imaging (CLI) utilizes CL generated from clinically relevant radionuclides alongside optical imaging instrumentation. CLI is advantageous over traditional nuclear imaging methods in terms of infrastructure cost, resolution, and imaging time. Furthermore, CLI is a truly multimodal imaging method where the same agent can be detected by two independent modalities, with optical (CL) imaging and with positron emission tomography (PET) imaging. CL has been combined with small molecules, biomolecules and nanoparticles to improve diagnosis and therapy in cancer research. Here, we cover the fundamental breakthroughs and recent advances in reagents and instrumentation methods for CLI as well as therapeutic application of CL. Copyright © 2018 Elsevier Inc. All rights reserved.

Biologically Relevant Heterogeneity: Metrics and Practical Insights.

PubMed

Gough, Albert; Stern, Andrew M; Maier, John; Lezon, Timothy; Shun, Tong-Ying; Chennubhotla, Chakra; Schurdak, Mark E; Haney, Steven A; Taylor, D Lansing

2017-03-01

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy.

PubMed

Das, Nandan K; Dai, Yichuan; Liu, Peng; Hu, Chuanzhen; Tong, Lieshu; Chen, Xiaoya; Smith, Zachary J

2017-07-07

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone. We review Raman combined with several optical and non-optical methods, including fluorescence, elastic scattering, OCT, phase imaging, and mass spectrometry. In each section we highlight the power of each combination along with a brief history and presentation of representative results. Finally, we conclude with a perspective detailing both benefits and challenges for multimodal Raman measurements, and give thoughts on future directions in the field.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy

PubMed Central

Das, Nandan K.; Dai, Yichuan; Liu, Peng; Hu, Chuanzhen; Tong, Lieshu; Chen, Xiaoya

2017-01-01

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone. We review Raman combined with several optical and non-optical methods, including fluorescence, elastic scattering, OCT, phase imaging, and mass spectrometry. In each section we highlight the power of each combination along with a brief history and presentation of representative results. Finally, we conclude with a perspective detailing both benefits and challenges for multimodal Raman measurements, and give thoughts on future directions in the field. PMID:28686212

3D digital image processing for biofilm quantification from confocal laser scanning microscopy: Multidimensional statistical analysis of biofilm modeling

NASA Astrophysics Data System (ADS)

Zielinski, Jerzy S.

The dramatic increase in number and volume of digital images produced in medical diagnostics, and the escalating demand for rapid access to these relevant medical data, along with the need for interpretation and retrieval has become of paramount importance to a modern healthcare system. Therefore, there is an ever growing need for processed, interpreted and saved images of various types. Due to the high cost and unreliability of human-dependent image analysis, it is necessary to develop an automated method for feature extraction, using sophisticated mathematical algorithms and reasoning. This work is focused on digital image signal processing of biological and biomedical data in one- two- and three-dimensional space. Methods and algorithms presented in this work were used to acquire data from genomic sequences, breast cancer, and biofilm images. One-dimensional analysis was applied to DNA sequences which were presented as a non-stationary sequence and modeled by a time-dependent autoregressive moving average (TD-ARMA) model. Two-dimensional analyses used 2D-ARMA model and applied it to detect breast cancer from x-ray mammograms or ultrasound images. Three-dimensional detection and classification techniques were applied to biofilm images acquired using confocal laser scanning microscopy. Modern medical images are geometrically arranged arrays of data. The broadening scope of imaging as a way to organize our observations of the biophysical world has led to a dramatic increase in our ability to apply new processing techniques and to combine multiple channels of data into sophisticated and complex mathematical models of physiological function and dysfunction. With explosion of the amount of data produced in a field of biomedicine, it is crucial to be able to construct accurate mathematical models of the data at hand. Two main purposes of signal modeling are: data size conservation and parameter extraction. Specifically, in biomedical imaging we have four key problems that were addressed in this work: (i) registration, i.e. automated methods of data acquisition and the ability to align multiple data sets with each other; (ii) visualization and reconstruction, i.e. the environment in which registered data sets can be displayed on a plane or in multidimensional space; (iii) segmentation, i.e. automated and semi-automated methods to create models of relevant anatomy from images; (iv) simulation and prediction, i.e. techniques that can be used to simulate growth end evolution of researched phenomenon. Mathematical models can not only be used to verify experimental findings, but also to make qualitative and quantitative predictions, that might serve as guidelines for the future development of technology and/or treatment.

Computation of fluid and particle motion from a time-sequenced image pair: a global outlier identification approach.

PubMed

Ray, Nilanjan

2011-10-01

Fluid motion estimation from time-sequenced images is a significant image analysis task. Its application is widespread in experimental fluidics research and many related areas like biomedical engineering and atmospheric sciences. In this paper, we present a novel flow computation framework to estimate the flow velocity vectors from two consecutive image frames. In an energy minimization-based flow computation, we propose a novel data fidelity term, which: 1) can accommodate various measures, such as cross-correlation or sum of absolute or squared differences of pixel intensities between image patches; 2) has a global mechanism to control the adverse effect of outliers arising out of motion discontinuities, proximity of image borders; and 3) can go hand-in-hand with various spatial smoothness terms. Further, the proposed data term and related regularization schemes are both applicable to dense and sparse flow vector estimations. We validate these claims by numerical experiments on benchmark flow data sets. © 2011 IEEE

MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.

PubMed

Katsigiannis, Stamos; Zacharia, Eleni; Maroulis, Dimitris

2017-05-01

Complementary DNA (cDNA) microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images that often suffer from noise, artifacts, and uneven background. In this study, the MIGS-GPU [Microarray Image Gridding and Segmentation on Graphics Processing Unit (GPU)] software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the GPU by means of the compute unified device architecture (CUDA) in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a user-friendly interface that requires minimum input in order to run.

Publication ethics in biomedical journals from countries in Central and Eastern Europe.

PubMed

Broga, Mindaugas; Mijaljica, Goran; Waligora, Marcin; Keis, Aime; Marusic, Ana

2014-03-01

Publication ethics is an important aspect of both the research and publication enterprises. It is particularly important in the field of biomedical science because published data may directly affect human health. In this article, we examine publication ethics policies in biomedical journals published in Central and Eastern Europe. We were interested in possible differences between East European countries that are members of the European Union (Eastern EU) and South-East European countries (South-East Europe) that are not members of the European Union. The most common ethical issues addressed by all journals in the region were redundant publication, peer review process, and copyright or licensing details. Image manipulation, editors' conflicts of interest and registration of clinical trials were the least common ethical policies. Three aspects were significantly more common in journals published outside the EU: statements on the endorsement of international editorial standards, contributorship policy, and image manipulation. On the other hand, copyright or licensing information were more prevalent in journals published in the Eastern EU. The existence of significant differences among biomedical journals' ethical policies calls for further research and active measures to harmonize policies across journals.

Bio-metals imaging and speciation in cells using proton and synchrotron radiation X-ray microspectroscopy

PubMed Central

Ortega, Richard; Devès, Guillaume; Carmona, Asunción

2009-01-01

The direct detection of biologically relevant metals in single cells and of their speciation is a challenging task that requires sophisticated analytical developments. The aim of this article is to present the recent achievements in the field of cellular chemical element imaging, and direct speciation analysis, using proton and synchrotron radiation X-ray micro- and nano-analysis. The recent improvements in focusing optics for MeV-accelerated particles and keV X-rays allow application to chemical element analysis in subcellular compartments. The imaging and quantification of trace elements in single cells can be obtained using particle-induced X-ray emission (PIXE). The combination of PIXE with backscattering spectrometry and scanning transmission ion microscopy provides a high accuracy in elemental quantification of cellular organelles. On the other hand, synchrotron radiation X-ray fluorescence provides chemical element imaging with less than 100 nm spatial resolution. Moreover, synchrotron radiation offers the unique capability of spatially resolved chemical speciation using micro-X-ray absorption spectroscopy. The potential of these methods in biomedical investigations will be illustrated with examples of application in the fields of cellular toxicology, and pharmacology, bio-metals and metal-based nano-particles. PMID:19605403

Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis

NASA Astrophysics Data System (ADS)

Zhou, Naiyun; Yu, Xiaxia; Zhao, Tianhao; Wen, Si; Wang, Fusheng; Zhu, Wei; Kurc, Tahsin; Tannenbaum, Allen; Saltz, Joel; Gao, Yi

2017-03-01

Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical, biomedical research, and computer vision fields. Among the multiple observable features spanning multiple scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading. As a result it is also the mostly studied target in image computing. Large amount of research papers have devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei, whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more quantitatively validated study for current and future histopathology image analysis field.

Listening to light scattering in turbid media: quantitative optical scattering imaging using photoacoustic measurements with one-wavelength illumination

NASA Astrophysics Data System (ADS)

Yuan, Zhen; Li, Xiaoqi; Xi, Lei

2014-06-01

Biomedical photoacoustic tomography (PAT), as a potential imaging modality, can visualize tissue structure and function with high spatial resolution and excellent optical contrast. It is widely recognized that the ability of quantitatively imaging optical absorption and scattering coefficients from photoacoustic measurements is essential before PAT can become a powerful imaging modality. Existing quantitative PAT (qPAT), while successful, has been focused on recovering absorption coefficient only by assuming scattering coefficient a constant. An effective method for photoacoustically recovering optical scattering coefficient is presently not available. Here we propose and experimentally validate such a method for quantitative scattering coefficient imaging using photoacoustic data from one-wavelength illumination. The reconstruction method developed combines conventional PAT with the photon diffusion equation in a novel way to realize the recovery of scattering coefficient. We demonstrate the method using various objects having scattering contrast only or both absorption and scattering contrasts embedded in turbid media. The listening-to-light-scattering method described will be able to provide high resolution scattering imaging for various biomedical applications ranging from breast to brain imaging.

3-D System-on-System (SoS) Biomedical-Imaging Architecture for Health-Care Applications.

PubMed

Sang-Jin Lee; Kavehei, O; Yoon-Ki Hong; Tae Won Cho; Younggap You; Kyoungrok Cho; Eshraghian, K

2010-12-01

This paper presents the implementation of a 3-D architecture for a biomedical-imaging system based on a multilayered system-on-system structure. The architecture consists of a complementary metal-oxide semiconductor image sensor layer, memory, 3-D discrete wavelet transform (3D-DWT), 3-D Advanced Encryption Standard (3D-AES), and an RF transmitter as an add-on layer. Multilayer silicon (Si) stacking permits fabrication and optimization of individual layers by different processing technology to achieve optimal performance. Utilization of through silicon via scheme can address required low-power operation as well as high-speed performance. Potential benefits of 3-D vertical integration include an improved form factor as well as a reduction in the total wiring length, multifunctionality, power efficiency, and flexible heterogeneous integration. The proposed imaging architecture was simulated by using Cadence Spectre and Synopsys HSPICE while implementation was carried out by Cadence Virtuoso and Mentor Graphic Calibre.

Imaging Strategies for Tissue Engineering Applications

PubMed Central

Nam, Seung Yun; Ricles, Laura M.; Suggs, Laura J.

2015-01-01

Tissue engineering has evolved with multifaceted research being conducted using advanced technologies, and it is progressing toward clinical applications. As tissue engineering technology significantly advances, it proceeds toward increasing sophistication, including nanoscale strategies for material construction and synergetic methods for combining with cells, growth factors, or other macromolecules. Therefore, to assess advanced tissue-engineered constructs, tissue engineers need versatile imaging methods capable of monitoring not only morphological but also functional and molecular information. However, there is no single imaging modality that is suitable for all tissue-engineered constructs. Each imaging method has its own range of applications and provides information based on the specific properties of the imaging technique. Therefore, according to the requirements of the tissue engineering studies, the most appropriate tool should be selected among a variety of imaging modalities. The goal of this review article is to describe available biomedical imaging methods to assess tissue engineering applications and to provide tissue engineers with criteria and insights for determining the best imaging strategies. Commonly used biomedical imaging modalities, including X-ray and computed tomography, positron emission tomography and single photon emission computed tomography, magnetic resonance imaging, ultrasound imaging, optical imaging, and emerging techniques and multimodal imaging, will be discussed, focusing on the latest trends of their applications in recent tissue engineering studies. PMID:25012069

MALDI mass spectrometry imaging in rheumatic diseases.

PubMed

Rocha, Beatriz; Cillero-Pastor, Berta; Blanco, Francisco J; Ruiz-Romero, Cristina

2017-07-01

Mass spectrometry imaging (MSI) is a technique used to visualize the spatial distribution of biomolecules such as peptides, proteins, lipids or other organic compounds by their molecular masses. Among the different MSI strategies, MALDI-MSI provides a sensitive and label-free approach for imaging of a wide variety of protein or peptide biomarkers from the surface of tissue sections, being currently used in an increasing number of biomedical applications such as biomarker discovery and tissue classification. In the field of rheumatology, MALDI-MSI has been applied to date for the analysis of joint tissues such as synovial membrane or cartilage. This review summarizes the studies and key achievements obtained using MALDI-MSI to increase understanding on rheumatic pathologies and to describe potential diagnostic or prognostic biomarkers of these diseases. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

Knowledge representation of motor activity of patients with Parkinson's disease.

PubMed

Kostek, Bożena; Kupryjanow, Adam; Czyżewski, Andrzej

An approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity disorders in patients. Obtained results indicate that it is possible to interpret some selected patient's body movements with a sufficiently high effectiveness.

X-ray vector radiography imaging for biomedical applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Potdevin, Guillaume; Malecki, Andreas; Biernath, Thomas

The non-invasive estimation of fracture risk in osteoporosis remains a challenge in the clinical routine and is mainly based on an assessment of bone density by dual X-ray absorption (DXA) although bone micro-architecture is known to play an important role for bone fragility. Here we report on 'X-ray vector Radiography' measurements able to provide a direct bone microstructure diagnostics on human bone samples, which we compare qualitatively and quantitatively with numerical analysis of high resolution radiographs.

Frequency domain phase-shifted confocal microscopy (FDPCM) with array detection

NASA Astrophysics Data System (ADS)

Ge, Baoliang; Huang, Yujia; Fang, Yue; Kuang, Cuifang; Xiu, Peng; Liu, Xu

2017-09-01

We proposed a novel method to reconstruct images taken by array detected confocal microscopy without prior knowledge about its detector distribution. The proposed frequency domain phase-shifted confocal microscopy (FDPCM) shifts the image from each detection channel to its corresponding place by substituting the phase information in Fourier domain. Theoretical analysis shows that our method could approach the resolution nearly twofold of wide-field microscopy. Simulation and experiment results are also shown to verify the applicability and effectiveness of our method. Compared to Airyscan, our method holds the advantage of simplicity and convenience to be applied to array detectors with different structure, which makes FDPCM have great potential in the application of biomedical observation in the future.

Imaging Mass Spectrometry in Neuroscience

PubMed Central

2013-01-01

Imaging mass spectrometry is an emerging technique of great potential for investigating the chemical architecture in biological matrices. Although the potential for studying neurobiological systems is evident, the relevance of the technique for application in neuroscience is still in its infancy. In the present Review, a principal overview of the different approaches, including matrix assisted laser desorption ionization and secondary ion mass spectrometry, is provided with particular focus on their strengths and limitations for studying different neurochemical species in situ and in vitro. The potential of the various approaches is discussed based on both fundamental and biomedical neuroscience research. This Review aims to serve as a general guide to familiarize the neuroscience community and other biomedical researchers with the technique, highlighting its great potential and suitability for comprehensive and specific chemical imaging. PMID:23530951

Piezoelectric single crystals for ultrasonic transducers in biomedical applications

PubMed Central

Zhou, Qifa; Lam, Kwok Ho; Zheng, Hairong; Qiu, Weibao; Shung, K. Kirk

2014-01-01

Piezoelectric single crystals, which have excellent piezoelectric properties, have extensively been employed for various sensors and actuators applications. In this paper, the state–of–art in piezoelectric single crystals for ultrasonic transducer applications is reviewed. Firstly, the basic principles and design considerations of piezoelectric ultrasonic transducers will be addressed. Then, the popular piezoelectric single crystals used for ultrasonic transducer applications, including LiNbO3 (LN), PMN–PT and PIN–PMN–PT, will be introduced. After describing the preparation and performance of the single crystals, the recent development of both the single–element and array transducers fabricated using the single crystals will be presented. Finally, various biomedical applications including eye imaging, intravascular imaging, blood flow measurement, photoacoustic imaging, and microbeam applications of the single crystal transducers will be discussed. PMID:25386032

Elemental and isotopic imaging of biological samples using NanoSIMS.

PubMed

Kilburn, Matt R; Clode, Peta L

2014-01-01

With its low detection limits and the ability to analyze most of the elements in the periodic table, secondary ion mass spectrometry (SIMS) represents one of the most versatile in situ analytical techniques available, and recent developments have resulted in significant advantages for the use of imaging mass spectrometry in biological and biomedical research. Increases in spatial resolution and sensitivity allow detailed interrogation of samples at relevant scales and chemical concentrations. Advances in dynamic SIMS, specifically with the advent of NanoSIMS, now allow the tracking of stable isotopes within biological systems at subcellular length scales, while static SIMS combines subcellular imaging with molecular identification. In this chapter, we present an introduction to the SIMS technique, with particular reference to NanoSIMS, and discuss its application in biological and biomedical research.

Localizer: fast, accurate, open-source, and modular software package for superresolution microscopy

PubMed Central

Duwé, Sam; Neely, Robert K.; Zhang, Jin

2012-01-01

Abstract. We present Localizer, a freely available and open source software package that implements the computational data processing inherent to several types of superresolution fluorescence imaging, such as localization (PALM/STORM/GSDIM) and fluctuation imaging (SOFI/pcSOFI). Localizer delivers high accuracy and performance and comes with a fully featured and easy-to-use graphical user interface but is also designed to be integrated in higher-level analysis environments. Due to its modular design, Localizer can be readily extended with new algorithms as they become available, while maintaining the same interface and performance. We provide front-ends for running Localizer from Igor Pro, Matlab, or as a stand-alone program. We show that Localizer performs favorably when compared with two existing superresolution packages, and to our knowledge is the only freely available implementation of SOFI/pcSOFI microscopy. By dramatically improving the analysis performance and ensuring the easy addition of current and future enhancements, Localizer strongly improves the usability of superresolution imaging in a variety of biomedical studies. PMID:23208219

Nanoparticles speckled by ready-to-conjugate lanthanide complexes for multimodal imaging

NASA Astrophysics Data System (ADS)

Biju, Vasudevanpillai; Hamada, Morihiko; Ono, Kenji; Sugino, Sakiko; Ohnishi, Takashi; Shibu, Edakkattuparambil Sidharth; Yamamura, Shohei; Sawada, Makoto; Nakanishi, Shunsuke; Shigeri, Yasushi; Wakida, Shin-Ichi

2015-09-01

Multimodal and multifunctional contrast agents receive enormous attention in the biomedical imaging field. Such contrast agents are routinely prepared by the incorporation of organic molecules and inorganic nanoparticles (NPs) into host materials such as gold NPs, silica NPs, polymer NPs, and liposomes. Despite their non-cytotoxic nature, the large size of these NPs limits the in vivo distribution and clearance and inflames complex pharmacokinetics, which hinder the regulatory approval for clinical applications. Herein, we report a unique method that combines magnetic resonance imaging (MRI) and fluorescence imaging modalities together in nanoscale entities by the simple, direct and stable conjugation of novel biotinylated coordination complexes of gadolinium(iii) to CdSe/ZnS quantum dots (QD) and terbium(iii) to super paramagnetic iron oxide NPs (SPION) but without any host material. Subsequently, we evaluate the potentials of such lanthanide-speckled fluorescent-magnetic NPs for bioimaging at single-molecule, cell and in vivo levels. The simple preparation and small size make such fluorescent-magnetic NPs promising contrast agents for biomedical imaging.

Informatics in radiology: An open-source and open-access cancer biomedical informatics grid annotation and image markup template builder.

PubMed

Mongkolwat, Pattanasak; Channin, David S; Kleper, Vladimir; Rubin, Daniel L

2012-01-01

In a routine clinical environment or clinical trial, a case report form or structured reporting template can be used to quickly generate uniform and consistent reports. Annotation and image markup (AIM), a project supported by the National Cancer Institute's cancer biomedical informatics grid, can be used to collect information for a case report form or structured reporting template. AIM is designed to store, in a single information source, (a) the description of pixel data with use of markups or graphical drawings placed on the image, (b) calculation results (which may or may not be directly related to the markups), and (c) supplemental information. To facilitate the creation of AIM annotations with data entry templates, an AIM template schema and an open-source template creation application were developed to assist clinicians, image researchers, and designers of clinical trials to quickly create a set of data collection items, thereby ultimately making image information more readily accessible.

Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder

PubMed Central

Channin, David S.; Rubin, Vladimir Kleper Daniel L.

2012-01-01

In a routine clinical environment or clinical trial, a case report form or structured reporting template can be used to quickly generate uniform and consistent reports. Annotation and Image Markup (AIM), a project supported by the National Cancer Institute’s cancer Biomedical Informatics Grid, can be used to collect information for a case report form or structured reporting template. AIM is designed to store, in a single information source, (a) the description of pixel data with use of markups or graphical drawings placed on the image, (b) calculation results (which may or may not be directly related to the markups), and (c) supplemental information. To facilitate the creation of AIM annotations with data entry templates, an AIM template schema and an open-source template creation application were developed to assist clinicians, image researchers, and designers of clinical trials to quickly create a set of data collection items, thereby ultimately making image information more readily accessible. © RSNA, 2012 PMID:22556315

Design and development of a simple UV fluorescence multi-spectral imaging system

NASA Astrophysics Data System (ADS)

Tovar, Carlos; Coker, Zachary; Yakovlev, Vladislav V.

2018-02-01

Healthcare access in low-resource settings is compromised by the availability of affordable and accurate diagnostic equipment. The four primary poverty-related diseases - AIDS, pneumonia, malaria, and tuberculosis - account for approximately 400 million annual deaths worldwide as of 2016 estimates. Current diagnostic procedures for these diseases are prolonged and can become unreliable under various conditions. We present the development of a simple low-cost UV fluorescence multi-spectral imaging system geared towards low resource settings for a variety of biological and in-vitro applications. Fluorescence microscopy serves as a useful diagnostic indicator and imaging tool. The addition of a multi-spectral imaging modality allows for the detection of fluorophores within specific wavelength bands, as well as the distinction between fluorophores possessing overlapping spectra. The developed instrument has the potential for a very diverse range of diagnostic applications in basic biomedical science and biomedical diagnostics and imaging. Performance assessment of the microscope will be validated with a variety of samples ranging from organic compounds to biological samples.

IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine.

PubMed

Domínguez, César; Heras, Jónathan; Pascual, Vico

2017-05-01

The effective processing of biomedical images usually requires the interoperability of diverse software tools that have different aims but are complementary. The goal of this work is to develop a bridge to connect two of those tools: ImageJ, a program for image analysis in life sciences, and OpenCV, a computer vision and machine learning library. Based on a thorough analysis of ImageJ and OpenCV, we detected the features of these systems that could be enhanced, and developed a library to combine both tools, taking advantage of the strengths of each system. The library was implemented on top of the SciJava converter framework. We also provide a methodology to use this library. We have developed the publicly available library IJ-OpenCV that can be employed to create applications combining features from both ImageJ and OpenCV. From the perspective of ImageJ developers, they can use IJ-OpenCV to easily create plugins that use any functionality provided by the OpenCV library and explore different alternatives. From the perspective of OpenCV developers, this library provides a link to the ImageJ graphical user interface and all its features to handle regions of interest. The IJ-OpenCV library bridges the gap between ImageJ and OpenCV, allowing the connection and the cooperation of these two systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

PubMed

Albarqouni, Shadi; Baur, Christoph; Achilles, Felix; Belagiannis, Vasileios; Demirci, Stefanie; Navab, Nassir

2016-05-01

The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.

Biomimetic synthesis of highly biocompatible gold nanoparticles with amino acid-dithiocarbamate as a precursor for SERS imaging

NASA Astrophysics Data System (ADS)

Li, Li; Liu, Jianbo; Yang, Xiaohai; Huang, Jin; He, Dinggeng; Guo, Xi; Wan, Lan; He, Xiaoxiao; Wang, Kemin

2016-03-01

Amino acid-dithiocarbamate (amino acid-DTC) was developed as both the reductant and ligand stabilizer for biomimetic synthesis of gold nanoparticles (AuNPs), which served as an excellent surface-enhanced Raman scattering (SERS) contrast nanoprobe for cell imaging. Glycine (Gly), glutamic acid (Glu), and histidine (His) with different isoelectric points were chosen as representative amino acid candidates to synthesize corresponding amino acid-DTC compounds through mixing with carbon disulfide (CS2), respectively. The pyrogenic decomposition of amino acid-DTC initiated the reduction synthesis of AuNPs, and the strong coordinating dithiocarbamate group of amino acid-DTC served as a stabilizer that grafted onto the surface of the AuNPs, which rendered the as-prepared nanoparticles a negative surface charge and high colloidal stability. MTT cell viability assay demonstrated that the biomimetic AuNPs possessed neglectful toxicity to the human hepatoma cell, which guaranteed them good biocompatibility for biomedical application. Meanwhile, the biomimetic AuNPs showed a strong SERS effect with an enhancement factor of 9.8 × 105 for the sensing of Rhodamine 6G, and two distinct Raman peaks located at 1363 and 1509 cm-1 could be clearly observed in the cell-imaging experiments. Therefore, biomimetic AuNPs can be explored as an excellent SERS contrast nanoprobe for biomedical imaging, and the amino acid-DTC mediated synthesis of the AuNPs has a great potential in bio-engineering and biomedical imaging applications.

Conformal image-guided microbeam radiation therapy at the ESRF biomedical beamline ID17

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donzelli, Mattia, E-mail: donzelli@esrf.fr; Bräuer-Krisch, Elke; Nemoz, Christian

Purpose: Upcoming veterinary trials in microbeam radiation therapy (MRT) demand for more advanced irradiation techniques than in preclinical research with small animals. The treatment of deep-seated tumors in cats and dogs with MRT requires sophisticated irradiation geometries from multiple ports, which impose further efforts to spare the normal tissue surrounding the target. Methods: This work presents the development and benchmarking of a precise patient alignment protocol for MRT at the biomedical beamline ID17 of the European Synchrotron Radiation Facility (ESRF). The positioning of the patient prior to irradiation is verified by taking x-ray projection images from different angles. Results: Usingmore » four external fiducial markers of 1.7  mm diameter and computed tomography-based treatment planning, a target alignment error of less than 2  mm can be achieved with an angular deviation of less than 2{sup ∘}. Minor improvements on the protocol and the use of smaller markers indicate that even a precision better than 1  mm is technically feasible. Detailed investigations concerning the imaging dose lead to the conclusion that doses for skull radiographs lie in the same range as dose reference levels for human head radiographs. A currently used online dose monitor for MRT has been proven to give reliable results for the imaging beam. Conclusions: The ESRF biomedical beamline ID17 is technically ready to apply conformal image-guided MRT from multiple ports to large animals during future veterinary trials.« less

A fast discrete S-transform for biomedical signal processing.

PubMed

Brown, Robert A; Frayne, Richard

2008-01-01

Determining the frequency content of a signal is a basic operation in signal and image processing. The S-transform provides both the true frequency and globally referenced phase measurements characteristic of the Fourier transform and also generates local spectra, as does the wavelet transform. Due to this combination, the S-transform has been successfully demonstrated in a variety of biomedical signal and image processing tasks. However, the computational demands of the S-transform have limited its application in medicine to this point in time. This abstract introduces the fast S-transform, a more efficient discrete implementation of the classic S-transform with dramatically reduced computational requirements.

A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis

NASA Astrophysics Data System (ADS)

Feng, Steve; Woo, Min-jae; Kim, Hannah; Kim, Eunso; Ki, Sojung; Shao, Lei; Ozcan, Aydogan

2016-03-01

We developed an easy-to-use and widely accessible crowd-sourcing tool for rapidly training humans to perform biomedical image diagnostic tasks and demonstrated this platform's ability on middle and high school students in South Korea to diagnose malaria infected red-blood-cells (RBCs) using Giemsa-stained thin blood smears imaged under light microscopes. We previously used the same platform (i.e., BioGames) to crowd-source diagnostics of individual RBC images, marking them as malaria positive (infected), negative (uninfected), or questionable (insufficient information for a reliable diagnosis). Using a custom-developed statistical framework, we combined the diagnoses from both expert diagnosticians and the minimally trained human crowd to generate a gold standard library of malaria-infection labels for RBCs. Using this library of labels, we developed a web-based training and educational toolset that provides a quantified score for diagnosticians/users to compare their performance against their peers and view misdiagnosed cells. We have since demonstrated the ability of this platform to quickly train humans without prior training to reach high diagnostic accuracy as compared to expert diagnosticians. Our initial trial group of 55 middle and high school students has collectively played more than 170 hours, each demonstrating significant improvements after only 3 hours of training games, with diagnostic scores that match expert diagnosticians'. Next, through a national-scale educational outreach program in South Korea we recruited >1660 students who demonstrated a similar performance level after 5 hours of training. We plan to further demonstrate this tool's effectiveness for other diagnostic tasks involving image labeling and aim to provide an easily-accessible and quickly adaptable framework for online training of new diagnosticians.

IMAGEP - A FORTRAN ALGORITHM FOR DIGITAL IMAGE PROCESSING

NASA Technical Reports Server (NTRS)

Roth, D. J.

1994-01-01

IMAGEP is a FORTRAN computer algorithm containing various image processing, analysis, and enhancement functions. It is a keyboard-driven program organized into nine subroutines. Within the subroutines are other routines, also, selected via keyboard. Some of the functions performed by IMAGEP include digitization, storage and retrieval of images; image enhancement by contrast expansion, addition and subtraction, magnification, inversion, and bit shifting; display and movement of cursor; display of grey level histogram of image; and display of the variation of grey level intensity as a function of image position. This algorithm has possible scientific, industrial, and biomedical applications in material flaw studies, steel and ore analysis, and pathology, respectively. IMAGEP is written in VAX FORTRAN for DEC VAX series computers running VMS. The program requires the use of a Grinnell 274 image processor which can be obtained from Mark McCloud Associates, Campbell, CA. An object library of the required GMR series software is included on the distribution media. IMAGEP requires 1Mb of RAM for execution. The standard distribution medium for this program is a 1600 BPI 9track magnetic tape in VAX FILES-11 format. It is also available on a TK50 tape cartridge in VAX FILES-11 format. This program was developed in 1991. DEC, VAX, VMS, and TK50 are trademarks of Digital Equipment Corporation.

Informatics in radiology: automated structured reporting of imaging findings using the AIM standard and XML.

PubMed

Zimmerman, Stefan L; Kim, Woojin; Boonn, William W

2011-01-01

Quantitative and descriptive imaging data are a vital component of the radiology report and are frequently of paramount importance to the ordering physician. Unfortunately, current methods of recording these data in the report are both inefficient and error prone. In addition, the free-text, unstructured format of a radiology report makes aggregate analysis of data from multiple reports difficult or even impossible without manual intervention. A structured reporting work flow has been developed that allows quantitative data created at an advanced imaging workstation to be seamlessly integrated into the radiology report with minimal radiologist intervention. As an intermediary step between the workstation and the reporting software, quantitative and descriptive data are converted into an extensible markup language (XML) file in a standardized format specified by the Annotation and Image Markup (AIM) project of the National Institutes of Health Cancer Biomedical Informatics Grid. The AIM standard was created to allow image annotation data to be stored in a uniform machine-readable format. These XML files containing imaging data can also be stored on a local database for data mining and analysis. This structured work flow solution has the potential to improve radiologist efficiency, reduce errors, and facilitate storage of quantitative and descriptive imaging data for research. Copyright © RSNA, 2011.

Experiences of engineering Grid-based medical software.

PubMed

Estrella, F; Hauer, T; McClatchey, R; Odeh, M; Rogulin, D; Solomonides, T

2007-08-01

Grid-based technologies are emerging as potential solutions for managing and collaborating distributed resources in the biomedical domain. Few examples exist, however, of successful implementations of Grid-enabled medical systems and even fewer have been deployed for evaluation in practice. The objective of this paper is to evaluate the use in clinical practice of a Grid-based imaging prototype and to establish directions for engineering future medical Grid developments and their subsequent deployment. The MammoGrid project has deployed a prototype system for clinicians using the Grid as its information infrastructure. To assist in the specification of the system requirements (and for the first time in healthgrid applications), use-case modelling has been carried out in close collaboration with clinicians and radiologists who had no prior experience of this modelling technique. A critical qualitative and, where possible, quantitative analysis of the MammoGrid prototype is presented leading to a set of recommendations from the delivery of the first deployed Grid-based medical imaging application. We report critically on the application of software engineering techniques in the specification and implementation of the MammoGrid project and show that use-case modelling is a suitable vehicle for representing medical requirements and for communicating effectively with the clinical community. This paper also discusses the practical advantages and limitations of applying the Grid to real-life clinical applications and presents the consequent lessons learned. The work presented in this paper demonstrates that given suitable commitment from collaborating radiologists it is practical to deploy in practice medical imaging analysis applications using the Grid but that standardization in and stability of the Grid software is a necessary pre-requisite for successful healthgrids. The MammoGrid prototype has therefore paved the way for further advanced Grid-based deployments in the medical and biomedical domains.

Rising Expectations: Access to Biomedical Information

PubMed Central

Lindberg, D. A. B.; Humphreys, B. L.

2008-01-01

Summary Objective To provide an overview of the expansion in public access to electronic biomedical information over the past two decades, with an emphasis on developments to which the U.S. National Library of Medicine contributed. Methods Review of the increasingly broad spectrum of web-accessible genomic data, biomedical literature, consumer health information, clinical trials data, and images. Results The amount of publicly available electronic biomedical information has increased dramatically over the past twenty years. Rising expectations regarding access to biomedical information were stimulated by the spread of the Internet, the World Wide Web, advanced searching and linking techniques. These informatics advances simplified and improved access to electronic information and reduced costs, which enabled inter-organizational collaborations to build and maintain large international information resources and also aided outreach and education efforts The demonstrated benefits of free access to electronic biomedical information encouraged the development of public policies that further increase the amount of information available. Conclusions Continuing rapid growth of publicly accessible electronic biomedical information presents tremendous opportunities and challenges, including the need to ensure uninterrupted access during disasters or emergencies and to manage digital resources so they remain available for future generations. PMID:18587496

Biomedical Nanomagnetics: A Spin Through Possibilities in Imaging, Diagnostics, and Therapy

PubMed Central

Krishnan, Kannan M.

2010-01-01

Biomedical nanomagnetics is a multidisciplinary area of research in science, engineering and medicine with broad applications in imaging, diagnostics and therapy. Recent developments offer exciting possibilities in personalized medicine provided a truly integrated approach, combining chemistry, materials science, physics, engineering, biology and medicine, is implemented. Emphasizing this perspective, here we address important issues for the rapid development of the field, i.e., magnetic behavior at the nanoscale with emphasis on the relaxation dynamics, synthesis and surface functionalization of nanoparticles and core-shell structures, biocompatibility and toxicity studies, biological constraints and opportunities, and in vivo and in vitro applications. Specifically, we discuss targeted drug delivery and triggered release, novel contrast agents for magnetic resonance imaging, cancer therapy using magnetic fluid hyperthermia, in vitro diagnostics and the emerging magnetic particle imaging technique, that is quantitative and sensitive enough to compete with established imaging methods. In addition, the physics of self-assembly, which is fundamental to both biology and the future development of nanoscience, is illustrated with magnetic nanoparticles. It is shown that various competing energies associated with self-assembly converge on the nanometer length scale and different assemblies can be tailored by varying particle size and size distribution. Throughout this paper, while we discuss our recent research in the broad context of the multidisciplinary literature, we hope to bridge the gap between related work in physics/chemistry/engineering and biology/medicine and, at the same time, present the essential concepts in the individual disciplines. This approach is essential as biomedical nanomagnetics moves into the next phase of innovative translational research with emphasis on development of quantitative in vivo imaging, targeted and triggered drug release, and image guided therapy including validation of delivery and therapy response. PMID:20930943

Development of a combined microSPECT/CT system for small animal imaging

NASA Astrophysics Data System (ADS)

Sun, Mingshan

Modern advances in the biomedical sciences have placed increased attention on small animals such as mice and rats as models of human biology and disease in biological research and pharmaceutical development. Their small size and fast breeding rate, their physiologic similarity to human, and, more importantly, the availability of sophisticated genetic manipulations, all have made mice and rats the laboratory mammals of choice in these experimental studies. However, the increased use of small animals in biomedical research also calls for new instruments that can measure the anatomic and metabolic information noninvasively with adequate spatial resolution and measurement sensitivity to facilitate these studies. This dissertation describes the engineering development of a combined single photon emission computed tomography (SPECT) and X-ray computed tomography (CT) system dedicated for small animals imaging. The system aims to obtain both the anatomic and metabolic images with submillimeter spatial resolution in a way that the data can be correlated to provide improved image quality and to offer more complete biological evaluation for biomedical studies involving small animals. The project requires development of complete microSPECT and microCT subsystems. Both subsystems are configured with a shared gantry and animal bed with integrated instrumentation for data acquisition and system control. The microCT employs a microfocus X-ray tube and a CCD-based detector for low noise, high resolution imaging. The microSPECT utilizes three semiconductor detectors coupled with pinhole collimators. A significant contribution of this dissertation project is the development of iterative algorithms with geometrical compensation that allows radionuclide images to be reconstructed at submillimeter spatial resolution, but with significantly higher detection efficiency than conventional methods. Both subsystems are capable of helical scans, offering lengthened field of view and improved axial resolution. System performance of both modalities is characterized with phantoms and animals. The microSPECT shows 0.6 mm resolution and 60 cps/MBq detection efficiency for imaging mice with 0.5 mm pinholes. The microCT achieves 120 mum spatial resolution on detector but with a relatively low detective quantum efficiency of 0.2 at the zero frequency. The combined system demonstrates a flexible platform for instrumentation development and a valuable tool for biomedical research. In summary, this dissertation describes the development of a combined SPECT/CT system for imaging the physiological function and anatomical structure in small animals.

Low Cost, High-Throughput 3D Pulmonary Imager Using Hyperpolarized Contrast Agents and Low Field MRI

DTIC Science & Technology

2016-10-01

HAS THE PROJECT PROVIDED? ..... 7 HOW WERE THE RESULTS DISSEMINATED TO COMMUNITIES OF INTEREST? ................................................. 8...the results disseminated to communities of interest? Nothing to Report 4. IMPACT What was the impact on the development of the principal...the broader biomedical community , expanding the utility of HP methods as a new tool for probing fundamental biomedical questions. Acknowledgments The

Introducing anisotropic Minkowski functionals and quantitative anisotropy measures for local structure analysis in biomedical imaging

NASA Astrophysics Data System (ADS)

Wismüller, Axel; De, Titas; Lochmüller, Eva; Eckstein, Felix; Nagarajan, Mahesh B.

2013-03-01

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis in Biomedical Imaging

PubMed Central

Wismüller, Axel; De, Titas; Lochmüller, Eva; Eckstein, Felix; Nagarajan, Mahesh B.

2017-01-01

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10−4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications. PMID:29170580

Translational research of optical molecular imaging for personalized medicine.

PubMed

Qin, C; Ma, X; Tian, J

2013-12-01

In the medical imaging field, molecular imaging is a rapidly developing discipline and forms many imaging modalities, providing us effective tools to visualize, characterize, and measure molecular and cellular mechanisms in complex biological processes of living organisms, which can deepen our understanding of biology and accelerate preclinical research including cancer study and medicine discovery. Among many molecular imaging modalities, although the penetration depth of optical imaging and the approved optical probes used for clinics are limited, it has evolved considerably and has seen spectacular advances in basic biomedical research and new drug development. With the completion of human genome sequencing and the emergence of personalized medicine, the specific drug should be matched to not only the right disease but also to the right person, and optical molecular imaging should serve as a strong adjunct to develop personalized medicine by finding the optimal drug based on an individual's proteome and genome. In this process, the computational methodology and imaging system as well as the biomedical application regarding optical molecular imaging will play a crucial role. This review will focus on recent typical translational studies of optical molecular imaging for personalized medicine followed by a concise introduction. Finally, the current challenges and the future development of optical molecular imaging are given according to the understanding of the authors, and the review is then concluded.

Segmentation of vessels cluttered with cells using a physics based model.

PubMed

Schmugge, Stephen J; Keller, Steve; Nguyen, Nhat; Souvenir, Richard; Huynh, Toan; Clemens, Mark; Shin, Min C

2008-01-01

Segmentation of vessels in biomedical images is important as it can provide insight into analysis of vascular morphology, topology and is required for kinetic analysis of flow velocity and vessel permeability. Intravital microscopy is a powerful tool as it enables in vivo imaging of both vasculature and circulating cells. However, the analysis of vasculature in those images is difficult due to the presence of cells and their image gradient. In this paper, we provide a novel method of segmenting vessels with a high level of cell related clutter. A set of virtual point pairs ("vessel probes") are moved reacting to forces including Vessel Vector Flow (VVF) and Vessel Boundary Vector Flow (VBVF) forces. Incorporating the cell detection, the VVF force attracts the probes toward the vessel, while the VBVF force attracts the virtual points of the probes to localize the vessel boundary without being distracted by the image features of the cells. The vessel probes are moved according to Newtonian Physics reacting to the net of forces applied on them. We demonstrate the results on a set of five real in vivo images of liver vasculature cluttered by white blood cells. When compared against the ground truth prepared by the technician, the Root Mean Squared Error (RMSE) of segmentation with VVF and VBVF was 55% lower than the method without VVF and VBVF.

New Trends of Emerging Technologies in Digital Pathology.

PubMed

Bueno, Gloria; Fernández-Carrobles, M Milagro; Deniz, Oscar; García-Rojo, Marcial

2016-01-01

The future paradigm of pathology will be digital. Instead of conventional microscopy, a pathologist will perform a diagnosis through interacting with images on computer screens and performing quantitative analysis. The fourth generation of virtual slide telepathology systems, so-called virtual microscopy and whole-slide imaging (WSI), has allowed for the storage and fast dissemination of image data in pathology and other biomedical areas. These novel digital imaging modalities encompass high-resolution scanning of tissue slides and derived technologies, including automatic digitization and computational processing of whole microscopic slides. Moreover, automated image analysis with WSI can extract specific diagnostic features of diseases and quantify individual components of these features to support diagnoses and provide informative clinical measures of disease. Therefore, the challenge is to apply information technology and image analysis methods to exploit the new and emerging digital pathology technologies effectively in order to process and model all the data and information contained in WSI. The final objective is to support the complex workflow from specimen receipt to anatomic pathology report transmission, that is, to improve diagnosis both in terms of pathologists' efficiency and with new information. This article reviews the main concerns about and novel methods of digital pathology discussed at the latest workshop in the field carried out within the European project AIDPATH (Academia and Industry Collaboration for Digital Pathology). © 2016 S. Karger AG, Basel.

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

PubMed Central

Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

2016-01-01

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering. PMID:27872840

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation.

PubMed

Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

2016-01-01

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

Where are Romanian biomedical journals now and what does the future hold for them? A scientometric analysis.

PubMed

Dumitrascu, Dan L

2018-01-01

There is a competition between scientific journals in order to achieve leadership in their scientific field. There are several Romanian biomedical journals which are published in English and a smaller number in Romanian. We need a periodical analysis of their visibility and ranking according to scientometric measures. We searched all biomedical journals indexed on international data bases: Web of Science, PubMed, Scopus, Embase, Google Scholar. We analyzed their evaluation factors. Several journals from Romania in the biomedical field are indexed in international databases. Their scientometric indexes are not high. The best journal was acquired by an international publisher and is no longer listed for Romania. There are several Romanian biomedical journals indexed in international databases that deserve periodical analysis. There is a need to improve their ranking.

Biomedical nuclear and X-ray imager using high-energy grazing incidence mirrors

DOEpatents

Ziock, Klaus-Peter; Craig, William W.; Hasegawa, Bruce; Pivovaroff, Michael J.

2005-09-27

Imaging of radiation sources located in a subject is explored for medical applications. The approach involves using grazing-incidence optics to form images of the location of radiopharmaceuticals administered to a subject. The optics are "true focusing" optics, meaning that they project a real and inverted image of the radiation source onto a detector possessing spatial and energy resolution.

Biomedical engineering for health research and development.

PubMed

Zhang, X-Y

2015-01-01

Biomedical engineering is a new area of research in medicine and biology, providing new concepts and designs for the diagnosis, treatment and prevention of various diseases. There are several types of biomedical engineering, such as tissue, genetic, neural and stem cells, as well as chemical and clinical engineering for health care. Many electronic and magnetic methods and equipments are used for the biomedical engineering such as Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, Electroencephalography (EEG), Ultrasound and regenerative medicine and stem cell cultures, preparations of artificial cells and organs, such as pancreas, urinary bladders, liver cells, and fibroblasts cells of foreskin and others. The principle of tissue engineering is described with various types of cells used for tissue engineering purposes. The use of several medical devices and bionics are mentioned with scaffold, cells and tissue cultures and various materials are used for biomedical engineering. The use of biomedical engineering methods is very important for the human health, and research and development of diseases. The bioreactors and preparations of artificial cells or tissues and organs are described here.

Acoustic imaging with time reversal methods: From medicine to NDT

NASA Astrophysics Data System (ADS)

Fink, Mathias

2015-03-01

This talk will present an overview of the research conducted on ultrasonic time-reversal methods applied to biomedical imaging and to non-destructive testing. We will first describe iterative time-reversal techniques that allow both focusing ultrasonic waves on reflectors in tissues (kidney stones, micro-calcifications, contrast agents) or on flaws in solid materials. We will also show that time-reversal focusing does not need the presence of bright reflectors but it can be achieved only from the speckle noise generated by random distributions of non-resolved scatterers. We will describe the applications of this concept to correct distortions and aberrations in ultrasonic imaging and in NDT. In the second part of the talk we will describe the concept of time-reversal processors to get ultrafast ultrasonic images with typical frame rates of order of 10.000 F/s. It is the field of ultrafast ultrasonic imaging that has plenty medical applications and can be of great interest in NDT. We will describe some applications in the biomedical domain: Quantitative Elasticity imaging of tissues by following shear wave propagation to improve cancer detection and Ultrafast Doppler imaging that allows ultrasonic functional imaging.

Research-oriented image registry for multimodal image integration.

PubMed

Tanaka, M; Sadato, N; Ishimori, Y; Yonekura, Y; Yamashita, Y; Komuro, H; Hayahsi, N; Ishii, Y

1998-01-01

To provide multimodal biomedical images automatically, we constructed the research-oriented image registry, Data Delivery System (DDS). DDS was constructed on the campus local area network. Machines which generate images (imagers: DSA, ultrasound, PET, MRI, SPECT and CT) were connected to the campus LAN. Once a patient is registered, all his images are automatically picked up by DDS as they are generated, transferred through the gateway server to the intermediate server, and copied into the directory of the user who registered the patient. DDS informs the user through e-mail that new data have been generated and transferred. Data format is automatically converted into one which is chosen by the user. Data inactive for a certain period in the intermediate server are automatically achieved into the final and permanent data server based on compact disk. As a soft link is automatically generated through this step, a user has access to all (old or new) image data of the patient of his interest. As DDS runs with minimal maintenance, cost and time for data transfer are significantly saved. By making the complex process of data transfer and conversion invisible, DDS has made it easy for naive-to-computer researchers to concentrate on their biomedical interest.

Analysis of thirteen predatory publishers: a trap for eager-to-publish researchers.

PubMed

Bolshete, Pravin

2018-01-01

To demonstrate a strategy employed by predatory publishers to trap eager-to-publish authors or researchers into submitting their work. This was a case study of 13 potential, possible, or probable predatory scholarly open-access publishers with similar characteristics. Eleven publishers were included from Beall's list and two additional publishers were identified from a Google web search. Each publisher's site was visited and its content analyzed. Publishers publishing biomedical journals were further explored and additional data was collected regarding their volumes, details of publications and editorial-board members. Overall, the look and feel of all 13 publishers was similar including names of publishers, website addresses, homepage content, homepage images, list of journals and subject areas, as if they were copied and pasted. There were discrepancies in article-processing charges within the publishers. None of the publishers identified names in their contact details and primarily included only email addresses. Author instructions were similar across all 13 publishers. Most publishers listed journals of varied subject areas including biomedical journals (12 publishers) covering different geographic locations. Most biomedical journals published none or very few articles. The highest number of articles published by any single biomedical journal was 28. Several editorial-board members were listed across more than one journals, with one member listed 81 times in different 69 journals (i.e. twice in 12 journals). There was a strong reason to believe that predatory publishers may have several publication houses with different names under a single roof to trap authors from different geographic locations.

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

PubMed

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A; Kapur, Tina; Wells, William M; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-02-11

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

PubMed Central

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-01-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections. PMID:28615794

Time-dependent photon migration imaging

NASA Astrophysics Data System (ADS)

Sevick, Eva M.; Wang, NaiGuang; Chance, Britton

1992-02-01

Recently, the application of both time- and frequency-resolved fluorescence techniques for the determination of photon migration characteristics in strongly scattering media has been used to characterize the optical properties in strongly scattering media. Specifically, Chance and coworkers have utilized measurement of photon migration characteristics to determine tissue hemoglobin absorbance and ultimately oxygenation status in homogeneous tissues. In this study, we present simulation results and experimental measurements for both techniques to show the capacity of time-dependent photon migration characteristics to image optically obscure absorbers located in strongly scattering media. The applications of time-dependent photon imaging in the biomedical community include imaging of light absorbing hematomas, tumors, hypoxic tissue volumes, and other tissue abnormalities. Herein, we show that the time-resolved parameter of mean photon path length, , and the frequency- resolved parameter of phase-shift, (theta) , can be used similarly to obtain three dimensional information of absorber position from two-dimensional measurements. Finally, we show that unlike imaging techniques that monitor the intensity of light without regard to the migration characteristics, the resolution of time-dependent photon migration measurements is enhanced by tissue scattering, further potentiating their use for biomedical imaging.

Comparative study on fluorescence spectra of Chinese medicine north and south isatis root granules

NASA Astrophysics Data System (ADS)

Liang, Lan; He, Qing; Chen, Zhenqiang; Zhu, Siqi

2016-03-01

Since the spectral imaging technology emerged, it has gained a lot of application achievements in the military field, precision agriculture and biomedical science. When the fluorescence spectrum imaging first applied to the detection of the feature resource of Chinese herbal medicine, the characteristics of holistic and ambiguity made it a new approach to the traditional Chinese medicine testing. In this paper, we applied this method to study the Chinese medicine north and south isatis root granules by comparing their fluorescence spectra. Using cluster analysis, the results showed that the north and south Banlangen can not be divided by ascription. And these indicate that there is a large difference in the quality of Banlangen granules on the market, and fluorescence spectrum imaging method can be used in monitoring the quality of radix isatidis granules.

Mitigating fringing in discrete frequency infrared imaging using time-delayed integration

PubMed Central

Ran, Shihao; Berisha, Sebastian; Mankar, Rupali; Shih, Wei-Chuan; Mayerich, David

2018-01-01

Infrared (IR) spectroscopic microscopes provide the potential for label-free quantitative molecular imaging of biological samples, which can be used to aid in histology, forensics, and pharmaceutical analysis. Most IR imaging systems use broadband illumination combined with a spectrometer to separate the signal into spectral components. This technique is currently too slow for many biomedical applications such as clinical diagnosis, primarily due to the availability of bright mid-infrared sources and sensitive MCT detectors. There has been a recent push to increase throughput using coherent light sources, such as synchrotron radiation and quantum cascade lasers. While these sources provide a significant increase in intensity, the coherence introduces fringing artifacts in the final image. We demonstrate that applying time-delayed integration in one dimension can dramatically reduce fringing artifacts with minimal alterations to the standard infrared imaging pipeline. The proposed technique also offers the potential for less expensive focal plane array detectors, since linear arrays can be more readily incorporated into the proposed framework. PMID:29552416

Quantitative phase microscopy via optimized inversion of the phase optical transfer function.

PubMed

Jenkins, Micah H; Gaylord, Thomas K

2015-10-01

Although the field of quantitative phase imaging (QPI) has wide-ranging biomedical applicability, many QPI methods are not well-suited for such applications due to their reliance on coherent illumination and specialized hardware. By contrast, methods utilizing partially coherent illumination have the potential to promote the widespread adoption of QPI due to their compatibility with microscopy, which is ubiquitous in the biomedical community. Described herein is a new defocus-based reconstruction method that utilizes a small number of efficiently sampled micrographs to optimally invert the partially coherent phase optical transfer function under assumptions of weak absorption and slowly varying phase. Simulation results are provided that compare the performance of this method with similar algorithms and demonstrate compatibility with large phase objects. The accuracy of the method is validated experimentally using a microlens array as a test phase object. Lastly, time-lapse images of live adherent cells are obtained with an off-the-shelf microscope, thus demonstrating the new method's potential for extending QPI capability widely in the biomedical community.

CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition

PubMed Central

Gou, Shuiping; Wang, Yueyue; Wang, Zhilong; Peng, Yong; Zhang, Xiaopeng; Jiao, Licheng; Wu, Jianshe

2013-01-01

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images. PMID:24023764

A framework for biomedical figure segmentation towards image-based document retrieval

PubMed Central

2013-01-01

The figures included in many of the biomedical publications play an important role in understanding the biological experiments and facts described within. Recent studies have shown that it is possible to integrate the information that is extracted from figures in classical document classification and retrieval tasks in order to improve their accuracy. One important observation about the figures included in biomedical publications is that they are often composed of multiple subfigures or panels, each describing different methodologies or results. The use of these multimodal figures is a common practice in bioscience, as experimental results are graphically validated via multiple methodologies or procedures. Thus, for a better use of multimodal figures in document classification or retrieval tasks, as well as for providing the evidence source for derived assertions, it is important to automatically segment multimodal figures into subfigures and panels. This is a challenging task, however, as different panels can contain similar objects (i.e., barcharts and linecharts) with multiple layouts. Also, certain types of biomedical figures are text-heavy (e.g., DNA sequences and protein sequences images) and they differ from traditional images. As a result, classical image segmentation techniques based on low-level image features, such as edges or color, are not directly applicable to robustly partition multimodal figures into single modal panels. In this paper, we describe a robust solution for automatically identifying and segmenting unimodal panels from a multimodal figure. Our framework starts by robustly harvesting figure-caption pairs from biomedical articles. We base our approach on the observation that the document layout can be used to identify encoded figures and figure boundaries within PDF files. Taking into consideration the document layout allows us to correctly extract figures from the PDF document and associate their corresponding caption. We combine pixel-level representations of the extracted images with information gathered from their corresponding captions to estimate the number of panels in the figure. Thus, our approach simultaneously identifies the number of panels and the layout of figures. In order to evaluate the approach described here, we applied our system on documents containing protein-protein interactions (PPIs) and compared the results against a gold standard that was annotated by biologists. Experimental results showed that our automatic figure segmentation approach surpasses pure caption-based and image-based approaches, achieving a 96.64% accuracy. To allow for efficient retrieval of information, as well as to provide the basis for integration into document classification and retrieval systems among other, we further developed a web-based interface that lets users easily retrieve panels containing the terms specified in the user queries. PMID:24565394

Integrating cell phone imaging with magnetic levitation (i-LEV) for label-free blood analysis at the point-of-living

PubMed Central

Durmus, Naside Gozde; Davis, Ronald W.; Steinmetz, Lars M.; Demirci, Utkan

2016-01-01

There is an emerging need for portable, robust, inexpensive and easy-to-use disease diagnosis and prognosis monitoring platforms to share health information at the point-of-living, including clinical and home settings. Recent advances in digital health technologies have improved early diagnosis, drug treatment, and personalized medicine. Smartphones with high-resolution cameras and high data processing power enable intriguing biomedical applications when integrated with diagnostic devices. Further, these devices have immense potential to contribute to public health in resource-limited settings where there is a particular need for portable, rapid, label-free, easy-to-use and affordable biomedical devices to diagnose and continuously monitor patients for precision medicine, especially those suffering from rare diseases, such as sickle cell anemia, thalassemia and chronic fatigue syndrome. Here, we present a magnetic levitation-based diagnosis system in which different cell types (i.e., white and red blood cells) are levitated in a magnetic gradient and separated due to their unique densities. Moreover, we introduce an easy-to-use, smartphone incorporated levitation system for cell analysis. Using our portable imaging magnetic levitation (i-LEV) system, we show that white and red blood cells can be identified and cell numbers can be quantified without using any labels. In addition, cells levitated in i-LEV can be distinguished at single cell resolution, potentially enabling diagnosis and monitoring, as well as clinical and research applications. PMID:26523938

Integrating Cell Phone Imaging with Magnetic Levitation (i-LEV) for Label-Free Blood Analysis at the Point-of-Living.

PubMed

Baday, Murat; Calamak, Semih; Durmus, Naside Gozde; Davis, Ronald W; Steinmetz, Lars M; Demirci, Utkan

2016-03-02

There is an emerging need for portable, robust, inexpensive, and easy-to-use disease diagnosis and prognosis monitoring platforms to share health information at the point-of-living, including clinical and home settings. Recent advances in digital health technologies have improved early diagnosis, drug treatment, and personalized medicine. Smartphones with high-resolution cameras and high data processing power enable intriguing biomedical applications when integrated with diagnostic devices. Further, these devices have immense potential to contribute to public health in resource-limited settings where there is a particular need for portable, rapid, label-free, easy-to-use, and affordable biomedical devices to diagnose and continuously monitor patients for precision medicine, especially those suffering from rare diseases, such as sickle cell anemia, thalassemia, and chronic fatigue syndrome. Here, a magnetic levitation-based diagnosis system is presented in which different cell types (i.e., white and red blood cells) are levitated in a magnetic gradient and separated due to their unique densities. Moreover, an easy-to-use, smartphone incorporated levitation system for cell analysis is introduced. Using our portable imaging magnetic levitation (i-LEV) system, it is shown that white and red blood cells can be identified and cell numbers can be quantified without using any labels. In addition, cells levitated in i-LEV can be distinguished at single-cell resolution, potentially enabling diagnosis and monitoring, as well as clinical and research applications. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A benchmark for comparison of dental radiography analysis algorithms.

PubMed

Wang, Ching-Wei; Huang, Cheng-Ta; Lee, Jia-Hong; Li, Chung-Hsing; Chang, Sheng-Wei; Siao, Ming-Jhih; Lai, Tat-Ming; Ibragimov, Bulat; Vrtovec, Tomaž; Ronneberger, Olaf; Fischer, Philipp; Cootes, Tim F; Lindner, Claudia

2016-07-01

Dental radiography plays an important role in clinical diagnosis, treatment and surgery. In recent years, efforts have been made on developing computerized dental X-ray image analysis systems for clinical usages. A novel framework for objective evaluation of automatic dental radiography analysis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2015 Bitewing Radiography Caries Detection Challenge and Cephalometric X-ray Image Analysis Challenge. In this article, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the dental anatomy data repository of bitewing radiographs, the creation of the anatomical abnormality classification data repository of cephalometric radiographs, and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, seven automatic methods for analysing cephalometric X-ray image and two automatic methods for detecting bitewing radiography caries have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic dental radiography analysis is still a challenging and unsolved problem. The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/). Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies

NASA Astrophysics Data System (ADS)

Vega, Francisco; Pérez, Wilson; Tello, Andrés.; Saquicela, Victor; Espinoza, Mauricio; Solano-Quinde, Lizandro; Vidal, Maria-Esther; La Cruz, Alexandra

2015-12-01

Advances in medical imaging have fostered medical diagnosis based on digital images. Consequently, the number of studies by medical images diagnosis increases, thus, collaborative work and tele-radiology systems are required to effectively scale up to this diagnosis trend. We tackle the problem of the collaborative access of medical images, and present WebMedSA, a framework to manage large datasets of medical images. WebMedSA relies on a PACS and supports the ontological annotation, as well as segmentation and visualization of the images based on their semantic description. Ontological annotations can be performed directly on the volumetric image or at different image planes (e.g., axial, coronal, or sagittal); furthermore, annotations can be complemented after applying a segmentation technique. WebMedSA is based on three main steps: (1) RDF-ization process for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML; (2) Integration of different biomedical ontologies (using L-MOM library), making this approach ontology independent; and (3) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Initial user evaluations suggest that WebMedSA facilitates the exchange of knowledge between radiologists, and provides the basis for collaborative work among them.

Near-infrared (NIR) emitting conjugated polymers for biomedical applications (Presentation Recording)

NASA Astrophysics Data System (ADS)

Repenko, Tatjana; Kuehne, Alexander J. C.

2015-10-01

Fluorescent biomedical markers of today such as dye-infiltrated colloids, microgels and quantum dots suffer from fast bleaching, lack surface functionality (for targets or pharmaceutical agents) and potentially leach heavy metals in case of quantum dots (e.g. Cd). By contrast, conjugated polymer particles are non-cytotoxic, exhibit reduced bleaching, as the entire particle consists of fluorophore, they are hydrophobic and show high quantum yields. Consequently, conjugated polymer particles represent ideal materials for biological applications and imaging. However currently, conjugated polymer particles for biomedical imaging usually lack near-infrared (NIR) emission and are polydisperse. Fluorescent agents with emission in the NIR spectrum are interesting for biomedical applications due to their low photo-damage towards biological species and the ability of NIR radiation to penetrate deep into biological tissue.. I will present the development and synthesis of new conjugated polymers particles with fluorescence in the NIR spectral region for bio-imaging and clinical diagnosis. The particle synthesis proceeds in a one-step Pd or Ni-catalyzed dispersion polymerization of functional NIR emitters. The resulting monodisperse conjugated polymer particles are obtained as a dispersion in a non-hazardous solvent. Different sizes in the sub-micrometer range with a narrow size distribution can be produced. Furthermore biological recognition motifs can be easily attached to the conjugated polymers via thiol-yne click-chemistry providing specific tumor targeting without quenching of the fluorescence. References [1] Kuehne AJC, Gather MC, Sprakel J., Nature Commun. 2012, 3, 1088. [2] Repenko T, Fokong S, De Laporte L, Go D, Kiessling F, Lammers T, Kuehne AJC.,Chem Commun 2015, accepted.

Black Phosphorus and its Biomedical Applications

PubMed Central

Choi, Jane Ru; Yong, Kar Wey; Choi, Jean Yu; Nilghaz, Azadeh; Lin, Yang; Xu, Jie; Lu, Xiaonan

2018-01-01

Black phosphorus (BP), also known as phosphorene, has attracted recent scientific attention since its first successful exfoliation in 2014 owing to its unique structure and properties. In particular, its exceptional attributes, such as the excellent optical and mechanical properties, electrical conductivity and electron-transfer capacity, contribute to its increasing demand as an alternative to graphene-based materials in biomedical applications. Although the outlook of this material seems promising, its practical applications are still highly challenging. In this review article, we discuss the unique properties of BP, which make it a potential platform for biomedical applications compared to other 2D materials, including graphene, molybdenum disulphide (MoS2), tungsten diselenide (WSe2) and hexagonal boron nitride (h-BN). We then introduce various synthesis methods of BP and review its latest progress in biomedical applications, such as biosensing, drug delivery, photoacoustic imaging and cancer therapies (i.e., photothermal and photodynamic therapies). Lastly, the existing challenges and future perspective of BP in biomedical applications are briefly discussed. PMID:29463996

An overview of biomedical literature search on the World Wide Web in the third millennium.

PubMed

Kumar, Prince; Goel, Roshni; Jain, Chandni; Kumar, Ashish; Parashar, Abhishek; Gond, Ajay Ratan

2012-06-01

Complete access to the existing pool of biomedical literature and the ability to "hit" upon the exact information of the relevant specialty are becoming essential elements of academic and clinical expertise. With the rapid expansion of the literature database, it is almost impossible to keep up to date with every innovation. Using the Internet, however, most people can freely access this literature at any time, from almost anywhere. This paper highlights the use of the Internet in obtaining valuable biomedical research information, which is mostly available from journals, databases, textbooks and e-journals in the form of web pages, text materials, images, and so on. The authors present an overview of web-based resources for biomedical researchers, providing information about Internet search engines (e.g., Google), web-based bibliographic databases (e.g., PubMed, IndMed) and how to use them, and other online biomedical resources that can assist clinicians in reaching well-informed clinical decisions.

Stimuli-responsive magnetic particles for biomedical applications.

PubMed

Medeiros, S F; Santos, A M; Fessi, H; Elaissari, A

2011-01-17

In recent years, magnetic nanoparticles have been studied due to their potential applications as magnetic carriers in biomedical area. These materials have been increasingly exploited as efficient delivery vectors, leading to opportunities of use as magnetic resonance imaging (MRI) agents, mediators of hyperthermia cancer treatment and in targeted therapies. Much attention has been also focused on "smart" polymers, which are able to respond to environmental changes, such as changes in the temperature and pH. In this context, this article reviews the state-of-the art in stimuli-responsive magnetic systems for biomedical applications. The paper describes different types of stimuli-sensitive systems, mainly temperature- and pH sensitive polymers, the combination of this characteristic with magnetic properties and, finally, it gives an account of their preparation methods. The article also discusses the main in vivo biomedical applications of such materials. A survey of the recent literature on various stimuli-responsive magnetic gels in biomedical applications is also included. Copyright © 2010 Elsevier B.V. All rights reserved.

Figure mining for biomedical research.

PubMed

Rodriguez-Esteban, Raul; Iossifov, Ivan

2009-08-15

Figures from biomedical articles contain valuable information difficult to reach without specialized tools. Currently, there is no search engine that can retrieve specific figure types. This study describes a retrieval method that takes advantage of principles in image understanding, text mining and optical character recognition (OCR) to retrieve figure types defined conceptually. A search engine was developed to retrieve tables and figure types to aid computational and experimental research. http://iossifovlab.cshl.edu/figurome/.

Synthesis and optimization of chitosan nanoparticles: Potential applications in nanomedicine and biomedical engineering.

PubMed

Ghadi, Arezou; Mahjoub, Soleiman; Tabandeh, Fatemeh; Talebnia, Farid

2014-01-01

Chitosan nanoparticles have become of great interest for nanomedicine, biomedical engineering and development of new therapeutic drug release systems with improved bioavailability, increased specificity and sensitivity, and reduced pharmacological toxicity. The aim of the present study was to synthesis and optimize of the chitosan nanoparticles for industrial and biomedical applications. Fe3O4 was synthesized and optimized as magnetic core nanoparticles and then chitosan covered this magnetic core. The size and morphology of the nano-magnetic chitosan was analyzed by scanning electron microscope (SEM). Topography and size distribution of the nanoparticles were shown with two-dimensional and three-dimensional images of atomic force microscopy (AFM). The nanoparticles were analyzed using transmission electron microscopy (TEM). The chitosan nanoparticles prepared in the experiment exhibited white powder shape. The SEM micrographs of the nano-magnetic chitosan showed that they were approximately uniform spheres. The unmodified chitosan nanoparticles composed of clusters of nanoparticles with sizes ranging from 10 nm to 80 nm. AFM provides a three-dimensional surface profile. The TEM image showed physical aggregation of the chitosan nanoparticles. The results show that a novel chitosan nanoparticle was successfully synthesized and characterized. It seems that this nanoparticle like the other chitosan nano particles has potential applications for nanomedicine, biomedical engineering, industrial and pharmaceutical fields.

'Add women & stir'--the biomedical approach to cardiac research!

PubMed

O'Donnell, Sharon; Condell, Sarah; Begley, Cecily M

2004-07-01

In conditions shared by women and men, the biomedical model of disease assumes that illness-symptoms and outcomes are biologically and socially 'neutral'. Consequently, up until a decade ago, white middle-aged men were the model subjects in most funded cardiac trials, with the assumption that whatever the findings, the results would also hold true for women. This 'add women and stir' approach has resulted in imbalances in cardiac care and an image of coronary artery disease, which portrays a middle-aged male as its victim. Moreover, cardiac health care has been designed with the male anatomy and male experience of illness in mind, and health promotional measures have been targeted towards men. Women have received these health promotional messages to protect the hearts of men, and have been less likely to modify their own lifestyles in a cardio-protective manner. However, the biological and social differences that exist between women and men, must surely invalidate such biased biomedical assertions, and signify a need to delve beyond the realm of biomedical reductionism for greater insights and understanding. This review examines how scientific reductionism has failed to explore the impact of coronary artery disease on the lives of women and how the gendered image of this disease has privileged the normative frame.

Early Light Imaging for Biomedical Applications

DTIC Science & Technology

2000-07-01

such as prostate cancer, breast cancer, cervical cancer, glaucoma , macular degeneration, macular endema, and atherosclerosis plaques. Understanding of...harmonic- generation cross-correlation time gating", Opt. Lett., 16 1019-1021 (1991) 17. K. Yoo, Z. Zang, S. Ahmed , R. Alfano, "Imaging objects hidden

Closer Look at Cancer Imaging Tools

MedlinePlus

... download on the Apple Store and Google Play. SOURCE: National Institute of Biomedical Imaging and Bioengineering: Medical Scans Spring 2018 Issue: Volume 13 Number 1 Page 11 MedlinePlus Subscribe Magazine Information Contact Us Viewers & Players Friends of the National Library of Medicine (FNLM) top

A review of optimization and quantification techniques for chemical exchange saturation transfer (CEST) MRI toward sensitive in vivo imaging

PubMed Central

Guo, Yingkun; Zheng, Hairong; Sun, Phillip Zhe

2015-01-01

Chemical exchange saturation transfer (CEST) MRI is a versatile imaging method that probes the chemical exchange between bulk water and exchangeable protons. CEST imaging indirectly detects dilute labile protons via bulk water signal changes following selective saturation of exchangeable protons, which offers substantial sensitivity enhancement and has sparked numerous biomedical applications. Over the past decade, CEST imaging techniques have rapidly evolved due to contributions from multiple domains, including the development of CEST mathematical models, innovative contrast agent designs, sensitive data acquisition schemes, efficient field inhomogeneity correction algorithms, and quantitative CEST (qCEST) analysis. The CEST system that underlies the apparent CEST-weighted effect, however, is complex. The experimentally measurable CEST effect depends not only on parameters such as CEST agent concentration, pH and temperature, but also on relaxation rate, magnetic field strength and more importantly, experimental parameters including repetition time, RF irradiation amplitude and scheme, and image readout. Thorough understanding of the underlying CEST system using qCEST analysis may augment the diagnostic capability of conventional imaging. In this review, we provide a concise explanation of CEST acquisition methods and processing algorithms, including their advantages and limitations, for optimization and quantification of CEST MRI experiments. PMID:25641791

Risk management for outsourcing biomedical waste disposal – Using the failure mode and effects analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liao, Ching-Jong; Ho, Chao Chung, E-mail: ho919@pchome.com.tw

Highlights: • This study is based on a real case in hospital in Taiwan. • We use Failure Mode and Effects Analysis (FMEA) as the evaluation method. • We successfully identify the evaluation factors of bio-medical waste disposal risk. - Abstract: Using the failure mode and effects analysis, this study examined biomedical waste companies through risk assessment. Moreover, it evaluated the supervisors of biomedical waste units in hospitals, and factors relating to the outsourcing risk assessment of biomedical waste in hospitals by referring to waste disposal acts. An expert questionnaire survey was conducted on the personnel involved in waste disposalmore » units in hospitals, in order to identify important factors relating to the outsourcing risk of biomedical waste in hospitals. This study calculated the risk priority number (RPN) and selected items with an RPN value higher than 80 for improvement. These items included “availability of freezing devices”, “availability of containers for sharp items”, “disposal frequency”, “disposal volume”, “disposal method”, “vehicles meeting the regulations”, and “declaration of three lists”. This study also aimed to identify important selection factors of biomedical waste disposal companies by hospitals in terms of risk. These findings can serve as references for hospitals in the selection of outsourcing companies for biomedical waste disposal.« less

Asymmetric spatiotemporal chaos induced by a polypoid mass in the excised larynx

PubMed Central

Zhang, Yu; Jiang, Jack J.

2008-01-01

In this paper, asymmetric spatiotemporal chaos induced by a polypoid mass simulating the laryngeal pathology of a vocal polyp is experimentally observed using high-speed imaging in an excised larynx. Spatiotemporal analysis reveals that the normal vocal folds show spatiotemporal correlation and symmetry. Normal vocal fold vibrations are dominated mainly by the first vibratory eigenmode. However, pathological vocal folds with a polypoid mass show broken symmetry and spatiotemporal irregularity. The spatial correlation is decreased. The pathological vocal folds spread vibratory energy across a large number of eigenmodes and induce asymmetric spatiotemporal chaos. High-order eigenmodes show complicated dynamics. Spatiotemporal analysis provides a valuable biomedical application for investigating the spatiotemporal chaotic dynamics of pathological vocal fold systems with a polypoid mass and may represent a valuable clinical tool for the detection of laryngeal mass lesion using high-speed imaging. PMID:19123612

Microwave Sensors for Breast Cancer Detection

PubMed Central

2018-01-01

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript. PMID:29473867

Microwave Sensors for Breast Cancer Detection.

PubMed

Wang, Lulu

2018-02-23

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.

An exploration of the biomedical optics course construction of undergraduate biomedical engineering program in medical colleges

NASA Astrophysics Data System (ADS)

Guo, Shijun; Lyu, Jie; Zhang, Peiming

2017-08-01

In this paper, the teaching goals, teaching contents and teaching methods in biomedical optics course construction are discussed. From the dimension of teaching goals, students should master the principle of optical inspection on the human body, diagnosis and treatment of methodology and instruments, through the study of the theory and practice of this course, and can utilize biomedical optics methods to solve practical problems in the clinical medical engineering practice. From the dimension of teaching contents, based on the characteristics of biomedical engineering in medical colleges, the organic integration of engineering aspects, medical optical instruments, and biomedical aspects dispersed in human anatomy, human physiology, clinical medicine fundamental related to the biomedical optics is build. Noninvasive measurement of the human body composition and noninvasive optical imaging of the human body were taken as actual problems in biomedical optics fields. Typical medical applications such as eye optics and laser medicine were also integrated into the theory and practice teaching. From the dimension of teaching methods, referencing to organ-system based medical teaching mode, optical principle and instrument principle were taught by teachers from school of medical instruments, and the histological characteristics and clinical actual need in areas such as digestive diseases and urinary surgery were taught by teachers from school of basic medicine or clinical medicine of medical colleges. Furthermore, clinical application guidance would be provided by physician and surgeons in hospitals.

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping.

PubMed

Soh, Jung; Turinsky, Andrei L; Trinh, Quang M; Chang, Jasmine; Sabhaney, Ajay; Dong, Xiaoli; Gordon, Paul Mk; Janzen, Ryan Pw; Hau, David; Xia, Jianguo; Wishart, David S; Sensen, Christoph W

2009-01-01

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

PLGA nanoparticles from nano-emulsion templating as imaging agents: Versatile technology to obtain nanoparticles loaded with fluorescent dyes.

PubMed

Fornaguera, C; Feiner-Gracia, N; Calderó, G; García-Celma, M J; Solans, C

2016-11-01

The interest in polymeric nanoparticles as imaging systems for biomedical applications has increased notably in the last decades. In this work, PLGA nanoparticles, prepared from nano-emulsion templating, have been used to prepare novel fluorescent imaging agents. Two model fluorescent dyes were chosen and dissolved in the oil phase of the nano-emulsions together with PLGA. Nano-emulsions were prepared by the phase inversion composition (PIC) low-energy method. Fluorescent dye-loaded nanoparticles were obtained by solvent evaporation of nano-emulsion templates. PLGA nanoparticles loaded with the fluorescent dyes showed hydrodynamic radii lower than 40nm; markedly lower than those reported in previous studies. The small nanoparticle size was attributed to the nano-emulsification strategy used. PLGA nanoparticles showed negative surface charge and enough stability to be used for biomedical imaging purposes. Encapsulation efficiencies were higher than 99%, which was also attributed to the nano-emulsification approach as well as to the low solubility of the dyes in the aqueous component. Release kinetics of both fluorescent dyes from the nanoparticle dispersions was pH-independent and sustained. These results indicate that the dyes could remain encapsulated enough time to reach any organ and that the decrease of the pH produced during cell internalization by the endocytic route would not affect their release. Therefore, it can be assumed that these nanoparticles are appropriate as systemic imaging agents. In addition, in vitro toxicity tests showed that nanoparticles are non-cytotoxic. Consequently, it can be concluded that the preparation of PLGA nanoparticles from nano-emulsion templating represents a very versatile technology that enables obtaining biocompatible, biodegradable and safe imaging agents suitable for biomedical purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

Viruses, Artificial Viruses and Virus-Based Structures for Biomedical Applications.

PubMed

van Rijn, Patrick; Schirhagl, Romana

2016-06-01

Nanobiomaterials such as virus particles and artificial virus particles offer tremendous opportunities to develop new biomedical applications such as drug- or gene-delivery, imaging and sensing but also improve understanding of biological mechanisms. Recent advances within the field of virus-based systems give insights in how to mimic viral structures and virus assembly processes as well as understanding biodistribution, cell/tissue targeting, controlled and triggered disassembly or release and circulation times. All these factors are of high importance for virus-based functional systems. This review illustrates advances in mimicking and enhancing or controlling these aspects to a high degree toward delivery and imaging applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernstein, Dr. Ira

This grant was awarded in support of Phase 2 of the University of Vermont Center for Biomedical Imaging. Phase 2 outlined several specific aims including: The development of expertise in MRI and fMRI imaging and their applications The acquisition of peer reviewed extramural funding in support of the Center The development of a Core Imaging Advisory Board, fee structure and protocol review and approval process.

Back in Time

NASA Technical Reports Server (NTRS)

1998-01-01

Under a Jet Propulsion Laboratory SBIR (Small Business Innovative Research), Cambridge Research and Instrumentation Inc., developed a new class of filters for the construction of small, low-cost multispectral imagers. The VariSpec liquid crystal enables users to obtain multi-spectral, ultra-high resolution images using a monochrome CCD (charge coupled device) camera. Application areas include biomedical imaging, remote sensing, and machine vision.

Dynamic laser speckle angiography achieved by eigen-decomposition filtering.

PubMed

Li, Chenxi; Wang, Ruikang

2017-06-01

A new approach is proposed for statistically analysis of laser speckle signals emerged from a living biological tissue based on eigen-decomposition to separate the dynamic speckle signals due to moving blood cells from the static speckle signals due to static tissue components, upon which to achieve angiography of the interrogated tissue in vivo. The proposed approach is tested by imaging mouse ear pinna in vivo, demonstrating its capability of providing detailed microvascular networks with high contrast, and high temporal and spatial resolutions. It is expected to provide further opportunities for laser speckle imaging in the biomedical and clinical applications where microvascular response to certain stimulus or tissue injury is of interest. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Compact Microscope Imaging System With Intelligent Controls Improved

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

The Compact Microscope Imaging System (CMIS) with intelligent controls is a diagnostic microscope analysis tool with intelligent controls for use in space, industrial, medical, and security applications. This compact miniature microscope, which can perform tasks usually reserved for conventional microscopes, has unique advantages in the fields of microscopy, biomedical research, inline process inspection, and space science. Its unique approach integrates a machine vision technique with an instrumentation and control technique that provides intelligence via the use of adaptive neural networks. The CMIS system was developed at the NASA Glenn Research Center specifically for interface detection used for colloid hard spheres experiments; biological cell detection for patch clamping, cell movement, and tracking; and detection of anode and cathode defects for laboratory samples using microscope technology.

Multi-color electron microscopy by element-guided identification of cells, organelles and molecules.

PubMed

Scotuzzi, Marijke; Kuipers, Jeroen; Wensveen, Dasha I; de Boer, Pascal; Hagen, Kees C W; Hoogenboom, Jacob P; Giepmans, Ben N G

2017-04-07

Cellular complexity is unraveled at nanometer resolution using electron microscopy (EM), but interpretation of macromolecular functionality is hampered by the difficulty in interpreting grey-scale images and the unidentified molecular content. We perform large-scale EM on mammalian tissue complemented with energy-dispersive X-ray analysis (EDX) to allow EM-data analysis based on elemental composition. Endogenous elements, labels (gold and cadmium-based nanoparticles) as well as stains are analyzed at ultrastructural resolution. This provides a wide palette of colors to paint the traditional grey-scale EM images for composition-based interpretation. Our proof-of-principle application of EM-EDX reveals that endocrine and exocrine vesicles exist in single cells in Islets of Langerhans. This highlights how elemental mapping reveals unbiased biomedical relevant information. Broad application of EM-EDX will further allow experimental analysis on large-scale tissue using endogenous elements, multiple stains, and multiple markers and thus brings nanometer-scale 'color-EM' as a promising tool to unravel molecular (de)regulation in biomedicine.

Multi-color electron microscopy by element-guided identification of cells, organelles and molecules

PubMed Central

Scotuzzi, Marijke; Kuipers, Jeroen; Wensveen, Dasha I.; de Boer, Pascal; Hagen, Kees (C.) W.; Hoogenboom, Jacob P.; Giepmans, Ben N. G.

2017-01-01

Cellular complexity is unraveled at nanometer resolution using electron microscopy (EM), but interpretation of macromolecular functionality is hampered by the difficulty in interpreting grey-scale images and the unidentified molecular content. We perform large-scale EM on mammalian tissue complemented with energy-dispersive X-ray analysis (EDX) to allow EM-data analysis based on elemental composition. Endogenous elements, labels (gold and cadmium-based nanoparticles) as well as stains are analyzed at ultrastructural resolution. This provides a wide palette of colors to paint the traditional grey-scale EM images for composition-based interpretation. Our proof-of-principle application of EM-EDX reveals that endocrine and exocrine vesicles exist in single cells in Islets of Langerhans. This highlights how elemental mapping reveals unbiased biomedical relevant information. Broad application of EM-EDX will further allow experimental analysis on large-scale tissue using endogenous elements, multiple stains, and multiple markers and thus brings nanometer-scale ‘color-EM’ as a promising tool to unravel molecular (de)regulation in biomedicine. PMID:28387351

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

NASA Astrophysics Data System (ADS)

Bianconi, Francesco; Bello-Cerezo, Raquel; Napoletano, Paolo

2018-01-01

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images.

Molecular imaging probe development: a chemistry perspective

PubMed Central

Nolting, Donald D; Nickels, Michael L; Guo, Ning; Pham, Wellington

2012-01-01

Molecular imaging is an attractive modality that has been widely employed in many aspects of biomedical research; especially those aimed at the early detection of diseases such as cancer, inflammation and neurodegenerative disorders. The field emerged in response to a new research paradigm in healthcare that seeks to integrate detection capabilities for the prediction and prevention of diseases. This approach made a distinct impact in biomedical research as it enabled researchers to leverage the capabilities of molecular imaging probes to visualize a targeted molecular event non-invasively, repeatedly and continuously in a living system. In addition, since such probes are inherently compact, robust, and amenable to high-throughput production, these probes could potentially facilitate screening of preclinical drug discovery, therapeutic assessment and validation of disease biomarkers. They could also be useful in drug discovery and safety evaluations. In this review, major trends in the chemical synthesis and development of positron emission tomography (PET), optical and magnetic resonance imaging (MRI) probes are discussed. PMID:22943038

Biologically Relevant Heterogeneity: Metrics and Practical Insights

PubMed Central

Gough, A; Stern, AM; Maier, J; Lezon, T; Shun, T-Y; Chennubhotla, C; Schurdak, ME; Haney, SA; Taylor, DL

2017-01-01

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications including basic biomedical research, drug discovery, diagnostics and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell level data, as well as the need for standard metrics of the spatial, temporal and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics and the design of optimal therapeutic strategies for individual patients. PMID:28231035

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

PubMed

Afgan, Enis; Baker, Dannon; Batut, Bérénice; van den Beek, Marius; Bouvier, Dave; Cech, Martin; Chilton, John; Clements, Dave; Coraor, Nate; Grüning, Björn A; Guerler, Aysam; Hillman-Jackson, Jennifer; Hiltemann, Saskia; Jalili, Vahid; Rasche, Helena; Soranzo, Nicola; Goecks, Jeremy; Taylor, James; Nekrutenko, Anton; Blankenberg, Daniel

2018-05-22

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

Fracture risk assessment: improved evaluation of vertebral integrity among metastatic cancer patients to aid in surgical decision-making

NASA Astrophysics Data System (ADS)

Augustine, Kurt E.; Camp, Jon J.; Holmes, David R.; Huddleston, Paul M.; Lu, Lichun; Yaszemski, Michael J.; Robb, Richard A.

2012-03-01

Failure of the spine's structural integrity from metastatic disease can lead to both pain and neurologic deficit. Fractures that require treatment occur in over 30% of bony metastases. Our objective is to use computed tomography (CT) in conjunction with analytic techniques that have been previously developed to predict fracture risk in cancer patients with metastatic disease to the spine. Current clinical practice for cancer patients with spine metastasis often requires an empirical decision regarding spinal reconstructive surgery. Early image-based software systems used for CT analysis are time consuming and poorly suited for clinical application. The Biomedical Image Resource (BIR) at Mayo Clinic, Rochester has developed an image analysis computer program that calculates from CT scans, the residual load-bearing capacity in a vertebra with metastatic cancer. The Spine Cancer Assessment (SCA) program is built on a platform designed for clinical practice, with a workflow format that allows for rapid selection of patient CT exams, followed by guided image analysis tasks, resulting in a fracture risk report. The analysis features allow the surgeon to quickly isolate a single vertebra and obtain an immediate pre-surgical multiple parallel section composite beam fracture risk analysis based on algorithms developed at Mayo Clinic. The analysis software is undergoing clinical validation studies. We expect this approach will facilitate patient management and utilization of reliable guidelines for selecting among various treatment option based on fracture risk.

Geometry-constraint-scan imaging for in-line phase contrast micro-CT.

PubMed

Fu, Jian; Yu, Guangyuan; Fan, Dekai

2014-01-01

X-ray phase contrast computed tomography (CT) uses the phase shift that x-rays undergo when passing through matter, rather than their attenuation, as the imaging signal and may provide better image quality in soft-tissue and biomedical materials with low atomic number. Here a geometry-constraint-scan imaging technique for in-line phase contrast micro-CT is reported. It consists of two circular-trajectory scans with x-ray detector at different positions, the phase projection extraction method with the Fresnel free-propagation theory and the filter back-projection reconstruction algorithm. This method removes the contact-detector scan and the pure phase object assumption in classical in-line phase contrast Micro-CT. Consequently it relaxes the experimental conditions and improves the image contrast. This work comprises a numerical study of this technique and its experimental verification using a biomedical composite dataset measured at an x-ray tube source Micro-CT setup. The numerical and experimental results demonstrate the validity of the presented method. It will be of interest for a wide range of in-line phase contrast Micro-CT applications in biology and medicine.

Potential use of MCR-ALS for the identification of coeliac-related biochemical changes in hyperspectral Raman maps from pediatric intestinal biopsies.

PubMed

Fornasaro, Stefano; Vicario, Annalisa; De Leo, Luigina; Bonifacio, Alois; Not, Tarcisio; Sergo, Valter

2018-05-14

Raman hyperspectral imaging is an emerging practice in biological and biomedical research for label free analysis of tissues and cells. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. The current study reports the first Raman microspectroscopic characterisation of colon tissues from patients with Coeliac Disease (CD). The aim was to assess if Raman imaging coupled with hyperspectral multivariate image analysis is capable of detecting the alterations in the biochemical composition of intestinal tissues associated with CD. The analytical approach was based on a multi-step methodology: duodenal biopsies from healthy and coeliac patients were measured and processed with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). Based on the distribution maps and the pure spectra of the image constituents obtained from MCR-ALS, interesting biochemical differences between healthy and coeliac patients has been derived. Noticeably, a reduced distribution of complex lipids in the pericryptic space, and a different distribution and abundance of proteins rich in beta-sheet structures was found in CD patients. The output of the MCR-ALS analysis was then used as a starting point for two clustering algorithms (k-means clustering and hierarchical clustering methods). Both methods converged with similar results providing precise segmentation over multiple Raman images of studied tissues.

Three-way parallel independent component analysis for imaging genetics using multi-objective optimization.

PubMed

Ulloa, Alvaro; Jingyu Liu; Vergara, Victor; Jiayu Chen; Calhoun, Vince; Pattichis, Marios

2014-01-01

In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.

Immobilization of biomolecules on the surface of inorganic nanoparticles for biomedical applications

PubMed Central

Xing, Zhi-Cai; Chang, Yongmin; Kang, Inn-Kyu

2010-01-01

Various inorganic nanoparticles have been used for drug delivery, magnetic resonance and fluorescence imaging, and cell targeting owing to their unique properties, such as large surface area and efficient contrasting effect. In this review, we focus on the surface functionalization of inorganic nanoparticles via immobilization of biomolecules and the corresponding surface interactions with biocomponents. Applications of surface-modified inorganic nanoparticles in biomedical fields are also outlined. PMID:27877316

Characterization of platelet adhesion under flow using microscopic image sequence analysis.

PubMed

Machin, M; Santomaso, A; Cozzi, M R; Battiston, M; Mazzuccato, M; De Marco, L; Canu, P

2005-07-01

A method for quantitative analysis of platelet deposition under flow is discussed here. The model system is based upon perfusion of blood platelets over an adhesive substrate immobilized on a glass coverslip acting as the lower surface of a rectangular flow chamber. The perfusion apparatus is mounted onto an inverted microscope equipped with epifluorescent illumination and intensified CCD video camera. Characterization is based on information obtained from a specific image analysis method applied to continuous sequences of microscopical images. Platelet recognition across the sequence of images is based on a time-dependent, bidimensional, gaussian-like pdf. Once a platelet is located,the variation of its position and shape as a function of time (i.e., the platelet history) can be determined. Analyzing the history we can establish if the platelet is moving on the surface, the frequency of this movement and the distance traveled before its resumes the velocity of a non-interacting cell. Therefore, we can determine how long the adhesion would last which is correlated to the resistance of the platelet-substrate bond. This algorithm enables the dynamic quantification of trajectories, as well as residence times, arrest and release frequencies for a high numbers of platelets at the same time. Statistically significant conclusions on platelet-surface interactions can then be obtained. An image analysis tool of this kind can dramatically help the investigation and characterization of the thrombogenic properties of artificial surfaces such as those used in artificial organs and biomedical devices.

Scanning transmission electron microscopy methods for the analysis of nanoparticles.

PubMed

Ponce, Arturo; Mejía-Rosales, Sergio; José-Yacamán, Miguel

2012-01-01

Here we review the scanning transmission electron microscopy (STEM) characterization technique and STEM imaging methods. We describe applications of STEM for studying inorganic nanoparticles, and other uses of STEM in biological and health sciences and discuss how to interpret STEM results. The STEM imaging mode has certain benefits compared with the broad-beam illumination mode; the main advantage is the collection of the information about the specimen using a high angular annular dark field (HAADF) detector, in which the images registered have different levels of contrast related to the chemical composition of the sample. Another advantage of its use in the analysis of biological samples is its contrast for thick stained sections, since HAADF images of samples with thickness of 100-120 nm have notoriously better contrast than those obtained by other techniques. Combining the HAADF-STEM imaging with the new aberration correction era, the STEM technique reaches a direct way to imaging the atomistic structure and composition of nanostructures at a sub-angstrom resolution. Thus, alloying in metallic nanoparticles is directly resolved at atomic scale by the HAADF-STEM imaging, and the comparison of the STEM images with results from simulations gives a very powerful way of analysis of structure and composition. The use of X-ray energy dispersive spectroscopy attached to the electron microscope for STEM mode is also described. In issues where characterization at the atomic scale of the interaction between metallic nanoparticles and biological systems is needed, all the associated techniques to STEM become powerful tools for the best understanding on how to use these particles in biomedical applications.

The importance of Zebrafish in biomedical research.

PubMed

Tavares, Bárbara; Santos Lopes, Susana

2013-01-01

Zebrafish (Danio rerio) is an ideal model organism for the study of vertebrate development. This is due to the large clutches that each couple produces, with up to 200 embryos every 7 days, and to the fact that the embryos and larvae are small, transparent and undergo rapid external development. Using scientific literature research tools available online and the keywords Zebrafish, biomedical research, human disease, and drug screening, we reviewed original studies and reviews indexed in PubMed. In this review we summarized work conducted with this model for the advancement of our knowledge related to several human diseases. We also focused on the biomedical research being performed in Portugal with the zebrafish model. Powerful live imaging and genetic tools are currently available for zebrafish making it a valuable model in biomedical research. The combination of these properties with the optimization of automated systems for drug screening has transformed the zebrafish into "a top model" in biomedical research, drug discovery and toxicity testing. Furthermore, with the optimization of xenografts technology it will be possible to use zebrafish to aide in the choice of the best therapy for each patient. Zebrafish is an excellent model organism in biomedical research, drug development and in clinical therapy.

Tutorial on photoacoustic tomography

NASA Astrophysics Data System (ADS)

Zhou, Yong; Yao, Junjie; Wang, Lihong V.

2016-06-01

Photoacoustic tomography (PAT) has become one of the fastest growing fields in biomedical optics. Unlike pure optical imaging, such as confocal microscopy and two-photon microscopy, PAT employs acoustic detection to image optical absorption contrast with high-resolution deep into scattering tissue. So far, PAT has been widely used for multiscale anatomical, functional, and molecular imaging of biological tissues. We focus on PAT's basic principles, major implementations, imaging contrasts, and recent applications.

Functional imaging and assessment of the glucose diffusion rate in epithelial tissues in optical coherence tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larin, K V; Tuchin, V V

2008-06-30

Functional imaging, monitoring and quantitative description of glucose diffusion in epithelial and underlying stromal tissues in vivo and controlling of the optical properties of tissues are extremely important for many biomedical applications including the development of noninvasive or minimally invasive glucose sensors as well as for therapy and diagnostics of various diseases, such as cancer, diabetic retinopathy, and glaucoma. Recent progress in the development of a noninvasive molecular diffusion biosensor based on optical coherence tomography (OCT) is described. The diffusion of glucose was studied in several epithelial tissues both in vitro and in vivo. Because OCT provides depth-resolved imaging ofmore » tissues with high in-depth resolution, the glucose diffusion is described not only as a function of time but also as a function of depth. (special issue devoted to application of laser technologies in biophotonics and biomedical studies)« less

Liquid crystal thermography and true-colour digital image processing

NASA Astrophysics Data System (ADS)

Stasiek, J.; Stasiek, A.; Jewartowski, M.; Collins, M. W.

2006-06-01

In the last decade thermochromic liquid crystals (TLC) and true-colour digital image processing have been successfully used in non-intrusive technical, industrial and biomedical studies and applications. Thin coatings of TLCs at surfaces are utilized to obtain detailed temperature distributions and heat transfer rates for steady or transient processes. Liquid crystals also can be used to make visible the temperature and velocity fields in liquids by the simple expedient of directly mixing the liquid crystal material into the liquid (water, glycerol, glycol, and silicone oils) in very small quantities to use as thermal and hydrodynamic tracers. In biomedical situations e.g., skin diseases, breast cancer, blood circulation and other medical application, TLC and image processing are successfully used as an additional non-invasive diagnostic method especially useful for screening large groups of potential patients. The history of this technique is reviewed, principal methods and tools are described and some examples are also presented.

Imaging of Rabbit VX-2 Hepatic Cancer by Cold and Thermal Neutron Radiography

NASA Astrophysics Data System (ADS)

Tsuchiya, Yoshinori; Matsubayashi, Masahito; Takeda, Tohoru; Lwin, Thet Thet; Wu, Jin; Yoneyama, Akio; Matsumura, Akira; Hori, Tomiei; Itai, Yuji

2003-11-01

Neutron radiography is based on differences in neutron mass attenuation coefficients among the elements and is a non-destructive imaging method. To investigate biomedical applications of neutron radiography, imaging of rabbit VX-2 liver cancer was performed using thermal and cold neutron radiography with a neutron imaging plate. Hepatic vessels and VX-2 tumor were clearly observed by neutron radiography, especially by cold neutron imaging. The image contrast of this modality was better than that of absorption-contrast X-ray radiography.

Introducing Theranostics Journal - From the Editor-in-Chief

PubMed Central

Chen, Xiaoyuan (Shawn)

2011-01-01

Theranostics is a multidisciplinary journal that publishes innovative and original research papers reflecting the field of molecular imaging, molecular therapeutics, multifunctional nanoparticle platforms, image-guided therapy, and translational nanomedicine. A broad spectrum of biomedical research that can be applied to future theranostic applications is encouraged. PMID:21547150

Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

PubMed

Allen, William J; Gabr, Refaat E; Tefera, Getaneh B; Pednekar, Amol S; Vaughn, Matthew W; Narayana, Ponnada A

2018-03-01

Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.

Deep learning in mammography and breast histology, an overview and future trends.

PubMed

Hamidinekoo, Azam; Denton, Erika; Rampun, Andrik; Honnor, Kate; Zwiggelaar, Reyer

2018-07-01

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Analysis of monochromatic and quasi-monochromatic X-ray sources in imaging and therapy

NASA Astrophysics Data System (ADS)

Westphal, Maximillian; Lim, Sara; Nahar, Sultana; Orban, Christopher; Pradhan, Anil

2017-04-01

We studied biomedical imaging and therapeutic applications of recently developed quasi-monochromatic and monochromatic X-ray sources. Using the Monte Carlo code GEANT4, we found that the quasi-monochromatic 65 keV Gaussian X-ray spectrum created by inverse Compton scattering with relatavistic electron beams were capable of producing better image contrast with less radiation compared to conventional 120 kV broadband CT scans. We also explored possible experimental detection of theoretically predicted K α resonance fluorescence in high-Z elements using the European Synchrotron Research Facility with a tungsten (Z = 74) target. In addition, we studied a newly developed quasi-monochromatic source generated by converting broadband X-rays to monochromatic K α and β X-rays with a zirconium target (Z = 40). We will further study how these K α and K β dominated spectra can be implemented in conjunction with nanoparticles for targeted therapy. Acknowledgement: Ohio Supercomputer Center, Columbus, OH.

Risk management for outsourcing biomedical waste disposal - using the failure mode and effects analysis.

PubMed

Liao, Ching-Jong; Ho, Chao Chung

2014-07-01

Using the failure mode and effects analysis, this study examined biomedical waste companies through risk assessment. Moreover, it evaluated the supervisors of biomedical waste units in hospitals, and factors relating to the outsourcing risk assessment of biomedical waste in hospitals by referring to waste disposal acts. An expert questionnaire survey was conducted on the personnel involved in waste disposal units in hospitals, in order to identify important factors relating to the outsourcing risk of biomedical waste in hospitals. This study calculated the risk priority number (RPN) and selected items with an RPN value higher than 80 for improvement. These items included "availability of freezing devices", "availability of containers for sharp items", "disposal frequency", "disposal volume", "disposal method", "vehicles meeting the regulations", and "declaration of three lists". This study also aimed to identify important selection factors of biomedical waste disposal companies by hospitals in terms of risk. These findings can serve as references for hospitals in the selection of outsourcing companies for biomedical waste disposal. Copyright © 2014 Elsevier Ltd. All rights reserved.

Carbon dots: emerging theranostic nanoarchitectures.

PubMed

Mishra, Vijay; Patil, Akshay; Thakur, Sourav; Kesharwani, Prashant

2018-06-01

Nanotechnology has gained significant interest from biomedical and analytical researchers in recent years. Carbon dots (C-dots), a new member of the carbon nanomaterial family, are spherical, nontoxic, biocompatible, and discrete particles less than 10nm in diameter. Research interest has focused on C-dots because of their ultra-compact nanosize, favorable biocompatibility, outstanding photoluminescence, superior electron transfer ability, and versatile surface engineering properties. C-dots show significant potential for use in cellular imaging, biosensing, targeted drug delivery, and other biomedical applications. Here we discuss C-dots, in terms of their physicochemical properties, fabrication techniques, toxicity issues, surface engineering and biomedical potential in drug delivery, targeting as well as bioimaging. Copyright © 2018 Elsevier Ltd. All rights reserved.

Super-resolution microscopy reveals structural diversity in molecular exchange among peptide amphiphile nanofibres

DOE PAGES

da Silva, Ricardo M. P.; van der Zwaag, Daan; Albertazzi, Lorenzo; ...

2016-05-19

The dynamic behaviour of supramolecular systems is an important dimension of their potential functions. Here, we report on the use of stochastic optical reconstruction microscopy to study the molecular exchange of peptide amphiphile nanofibres, supramolecular systems known to have important biomedical functions. Solutions of nanofibres labelled with different dyes (Cy3 and Cy5) were mixed, and the distribution of dyes inserting into initially single-colour nanofibres was quantified using correlative image analysis. Our observations are consistent with an exchange mechanism involving monomers or small clusters of molecules inserting randomly into a fibre. Different exchange rates are observed within the same fibre, suggestingmore » that local cohesive structures exist on the basis of beta-sheet discontinuous domains. The results reported here show that peptide amphiphile supramolecular systems can be dynamic and that their intermolecular interactions affect exchange patterns. Lastly, this information can be used to generate useful aggregate morphologies for improved biomedical function.« less

Dynamic-focusing microscope objective for optical coherence tomography

NASA Astrophysics Data System (ADS)

Murali, Supraja; Rolland, Jannick

2007-01-01

Optical Coherence Tomography (OCT) is a novel optical imaging technique that has assumed significant importance in bio-medical imaging in the last two decades because it is non-invasive and provides accurate, high resolution images of three dimensional cross-sections of body tissue, exceeding the capabilities of the current predominant imaging technique - ultrasound. In this paper, the application of high resolution OCT, known as optical coherence microscopy (OCM) is investigated for in vivo detection of abnormal skin pathology for the early diagnosis of cancer. A main challenge in OCM is maintaining invariant resolution throughout the sample. The technology presented is based on a dynamic focusing microscope imaging probe conceived for skin imaging and the detection of abnormalities in the epithelium. A novel method for dynamic focusing in the biological sample is presented using variable-focus lens technology to obtain three dimensional images with invariant resolution throughout the cross-section and depth of the sample is presented and discussed. A low coherence broadband source centered at near IR wavelengths is used to illuminate the sample. The design, analysis and predicted performance of the dynamic focusing microscope objective designed for dynamic three dimensional imaging at 5μm resolution for the chosen broadband spectrum is presented.

Sculpting narrowband Fano resonances inherent in the large-area mid-infrared photonic crystal microresonators for spectroscopic imaging

PubMed Central

Liu, Jui-Nung; Schulmerich, Matthew V.; Bhargava, Rohit; Cunningham, Brian T.

2014-01-01

Fourier transform infrared (FT-IR) imaging spectrometers are almost universally used to record microspectroscopic imaging data in the mid-infrared (mid-IR) spectral region. While the commercial standard, interferometry necessitates collection of large spectral regions, requires a large data handling overhead for microscopic imaging and is slow. Here we demonstrate an approach for mid-IR spectroscopic imaging at selected discrete wavelengths using narrowband resonant filtering of a broadband thermal source, enabled by high-performance guided-mode Fano resonances in one-layer, large-area mid-IR photonic crystals on a glass substrate. The microresonant devices enable discrete frequency IR (DF-IR), in which a limited number of wavelengths that are of interest are recorded using a mechanically robust instrument. This considerably simplifies instrumentation as well as overhead of data acquisition, storage and analysis for large format imaging with array detectors. To demonstrate the approach, we perform DF-IR spectral imaging of a polymer USAF resolution target and human tissue in the C−H stretching region (2600−3300 cm−1). DF-IR spectroscopy and imaging can be generalized to other IR spectral regions and can serve as an analytical tool for environmental and biomedical applications. PMID:25089433

I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chard, Kyle; D'Arcy, Mike; Heavner, Benjamin D.

Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshaling and permitting errors of omission and commission because dataset members are not explicitly specified. We address these issues by proposing simple methods and toolsmore » for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (BDBags), data descriptions (Research Objects), and simple persistent identifiers (Minids) to create a powerful ecosystem of tools and services for big data analysis and sharing. We present these tools and use biomedical case studies to illustrate their use for the rapid assembly, sharing, and analysis of large datasets.« less

[Application of Fourier transform profilometry in 3D-surface reconstruction].

PubMed

Shi, Bi'er; Lu, Kuan; Wang, Yingting; Li, Zhen'an; Bai, Jing

2011-08-01

With the improvement of system frame and reconstruction methods in fluorescent molecules tomography (FMT), the FMT technology has been widely used as an important experimental tool in biomedical research. It is necessary to get the 3D-surface profile of the experimental object as the boundary constraints of FMT reconstruction algorithms. We proposed a new 3D-surface reconstruction method based on Fourier transform profilometry (FTP) method under the blue-purple light condition. The slice images were reconstructed using proper image processing methods, frequency spectrum analysis and filtering. The results of experiment showed that the method properly reconstructed the 3D-surface of objects and has the mm-level accuracy. Compared to other methods, this one is simple and fast. Besides its well-reconstructed, the proposed method could help monitor the behavior of the object during the experiment to ensure the correspondence of the imaging process. Furthermore, the method chooses blue-purple light section as its light source to avoid the interference towards fluorescence imaging.

Big Data Application in Biomedical Research and Health Care: A Literature Review.

PubMed

Luo, Jake; Wu, Min; Gopukumar, Deepika; Zhao, Yiqing

2016-01-01

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

Big Data Application in Biomedical Research and Health Care: A Literature Review

PubMed Central

Luo, Jake; Wu, Min; Gopukumar, Deepika; Zhao, Yiqing

2016-01-01

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care. PMID:26843812

Ridge extraction from the time-frequency representation (TFR) of signals based on an image processing approach: application to the analysis of uterine electromyogram AR TFR.

PubMed

Terrien, Jérémy; Marque, Catherine; Germain, Guy

2008-05-01

Time-frequency representations (TFRs) of signals are increasingly being used in biomedical research. Analysis of such representations is sometimes difficult, however, and is often reduced to the extraction of ridges, or local energy maxima. In this paper, we describe a new ridge extraction method based on the image processing technique of active contours or snakes. We have tested our method on several synthetic signals and for the analysis of uterine electromyogram or electrohysterogram (EHG) recorded during gestation in monkeys. We have also evaluated a postprocessing algorithm that is especially suited for EHG analysis. Parameters are evaluated on real EHG signals in different gestational periods. The presented method gives good results when applied to synthetic as well as EHG signals. We have been able to obtain smaller ridge extraction errors when compared to two other methods specially developed for EHG. The gradient vector flow (GVF) snake method, or GVF-snake method, appears to be a good ridge extraction tool, which could be used on TFR of mono or multicomponent signals with good results.

Development of High-Speed Fluorescent X-Ray Micro-Computed Tomography

NASA Astrophysics Data System (ADS)

Takeda, T.; Tsuchiya, Y.; Kuroe, T.; Zeniya, T.; Wu, J.; Lwin, Thet-Thet; Yashiro, T.; Yuasa, T.; Hyodo, K.; Matsumura, K.; Dilmanian, F. A.; Itai, Y.; Akatsuka, T.

2004-05-01

A high-speed fluorescent x-ray CT (FXCT) system using monochromatic synchrotron x rays was developed to detect very low concentration of medium-Z elements for biomedical use. The system is equipped two types of high purity germanium detectors, and fast electronics and software. Preliminary images of a 10mm diameter plastic phantom containing channels field with iodine solutions of different concentrations showed a minimum detection level of 0.002 mg I/ml at an in-plane spatial resolution of 100μm. Furthermore, the acquisition time was reduced about 1/2 comparing to previous system. The results indicate that FXCT is a highly sensitive imaging modality capable of detecting very low concentration of iodine, and that the method has potential in biomedical applications.

78 FR 24223 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-24

... Imaging and Bioengineering Special Emphasis Panel; Center for Multiscale Simulations in the Human..., Bethesda, MD 20892, (Virtual Meeting). Contact Person: John K. Hayes, Ph.D., Scientific Review Officer... Boulevard, Room 959, Bethesda, MD 20892, 301-451-3398, [email protected] . Dated: April 18, 2013. David...

Metabolic Mapping of Breast Cancer with Multiphoton Spectral and Lifetime Imaging

DTIC Science & Technology

2008-03-01

Biomedical Optics May/June 2008 Vol. 133031220-1 the most prevalent cancer among women . 1 Therefore, tech- nologies to detect, classify, study, and...and molecular function using optical imaging: applications to breast cancer,” Breast Cancer Res. Treat. 31, 41–46 2001. 3. M. Sidani , J. Wyckoff

Smart molecules for imaging, sensing and health (SMITH).

PubMed

Smith, Bradley D

2015-01-01

This autobiographical review provides a personal account of the author's academic journey in supramolecular chemistry, including brief summaries of research efforts in membrane transport, molecular imaging, ion-pair receptors, rotaxane synthesis, squaraine rotaxanes, and synthtavidin technology. The article concludes with a short perspective of likely future directions in biomedical supramolecular chemistry.

Color Microfiche: Applications to Biomedical Optometric Education.

ERIC Educational Resources Information Center

Wing, Joan Tanabe; Chronister, Connie; Whittaker, Stephen G.; Crozier, Gilda C.

1999-01-01

A color microfiche containing ocular tissue section images was developed and introduced into an ocular history and embryology course to enhance student access to such images. Students found that the materials enhanced their ability to learn. Faculty found that the materials allowed students to prepare in advance for the laboratory, freeing class…

78 FR 46995 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-02

...) and 552b(c)(6), Title 5 U.S.C., as amended. The grant applications and the discussions could disclose... concerning individuals associated with the grant applications, the disclosure of which would constitute a... Imaging and Bioengineering Special Emphasis Panel Computer Integrated Systems for Microscopy...

Nanodiamonds of Laser Synthesis for Biomedical Applications.

PubMed

Perevedentseva, E; Peer, D; Uvarov, V; Zousman, B; Levinson, O

2015-02-01

In recent decade detonation nanodiamonds (DND), discovered 50 years ago and used in diverse technological processes, have been actively applied in biomedical research as a drug and gene delivery carrier, a contrast agent for bio-imaging and diagnostics and an adsorbent for protein separation and purification. In this work we report about nanodiamonds of high purity produced by laser assisted technique, compare them with DND and consider the prospect and advantages of their use in the said applications.

Data Analysis and Data Mining: Current Issues in Biomedical Informatics

PubMed Central

Bellazzi, Riccardo; Diomidous, Marianna; Sarkar, Indra Neil; Takabayashi, Katsuhiko; Ziegler, Andreas; McCray, Alexa T.

2011-01-01

Summary Background Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. Objectives To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, that reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. Results The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. Conclusions Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers. PMID:22146916

Additive Manufacturing of Biomedical Constructs with Biomimetic Structural Organizations.

PubMed

Li, Xiao; He, Jiankang; Zhang, Weijie; Jiang, Nan; Li, Dichen

2016-11-09

Additive manufacturing (AM), sometimes called three-dimensional (3D) printing, has attracted a lot of research interest and is presenting unprecedented opportunities in biomedical fields, because this technology enables the fabrication of biomedical constructs with great freedom and in high precision. An important strategy in AM of biomedical constructs is to mimic the structural organizations of natural biological organisms. This can be done by directly depositing cells and biomaterials, depositing biomaterial structures before seeding cells, or fabricating molds before casting biomaterials and cells. This review organizes the research advances of AM-based biomimetic biomedical constructs into three major directions: 3D constructs that mimic tubular and branched networks of vasculatures; 3D constructs that contains gradient interfaces between different tissues; and 3D constructs that have different cells positioned to create multicellular systems. Other recent advances are also highlighted, regarding the applications of AM for organs-on-chips, AM-based micro/nanostructures, and functional nanomaterials. Under this theme, multiple aspects of AM including imaging/characterization, material selection, design, and printing techniques are discussed. The outlook at the end of this review points out several possible research directions for the future.

Tutorial on photoacoustic tomography

PubMed Central

Zhou, Yong; Yao, Junjie; Wang, Lihong V.

2016-01-01

Abstract. Photoacoustic tomography (PAT) has become one of the fastest growing fields in biomedical optics. Unlike pure optical imaging, such as confocal microscopy and two-photon microscopy, PAT employs acoustic detection to image optical absorption contrast with high-resolution deep into scattering tissue. So far, PAT has been widely used for multiscale anatomical, functional, and molecular imaging of biological tissues. We focus on PAT’s basic principles, major implementations, imaging contrasts, and recent applications. PMID:27086868

Contrast-enhanced photoacoustic imaging with an optical wavelength of 1064 nm

NASA Astrophysics Data System (ADS)

Kim, Jeesu; Park, Sara; Park, Gyeong Bae; Choi, Wonseok; Jeong, Unyong; Kim, Chulhong

2018-02-01

Photoacoustic (PA) imaging is a biomedical imaging method that can provide both structural and functional information of living tissues beyond the optical diffusion limit by combining the concepts of conventional optical and ultrasound imaging methods. Although endogenous chromophores can be utilized to acquire PA images of biological tissues, exogenous contrast agents that absorb near-infrared (NIR) lights have been extensively explored to improve the contrast and penetration depth of PA images. Here, we demonstrate Bi2Se3 nanoplates, that strongly absorbs NIR lights, as a contrast agent for PA imaging. In particularly, the Bi2Se3 nanoplates produce relatively strong PA signals with an optical wavelength of 1064 nm, which has several advantages for deep tissue imaging including: (1) relatively low absorption by other intrinsic chromophores, (2) cost-effective light source using Nd:YAG laser, and (3) higher available energy than other NIR lights according to American National Standards Institute (ANSI) safety limit. We have investigated deep tissue imaging capability of the Bi2Se3 nanoplates by acquiring in vitro PA images of microtubes under chicken breast tissues. We have also acquired in vivo PA images of bladders, gastrointestinal tracts, and sentinel lymph nodes in mice after injection of the Bi2Se3 nanoplates to verify their applicability to a variety of biomedical research. The results show the promising potential of the Bi2Se3 nanoplates as a PA contrast agent for deep tissue imaging with an optical wavelength of 1064 nm.

Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers.

PubMed

Buckler, Andrew J; Liu, Tiffany Ting; Savig, Erica; Suzek, Baris E; Ouellette, M; Danagoulian, J; Wernsing, G; Rubin, Daniel L; Paik, David

2013-08-01

A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

Spatial and symbolic queries for 3D image data

NASA Astrophysics Data System (ADS)

Benson, Daniel C.; Zick, Gregory L.

1992-04-01

We present a query system for an object-oriented biomedical imaging database containing 3-D anatomical structures and their corresponding 2-D images. The graphical interface facilitates the formation of spatial queries, nonspatial or symbolic queries, and combined spatial/symbolic queries. A query editor is used for the creation and manipulation of 3-D query objects as volumes, surfaces, lines, and points. Symbolic predicates are formulated through a combination of text fields and multiple choice selections. Query results, which may include images, image contents, composite objects, graphics, and alphanumeric data, are displayed in multiple views. Objects returned by the query may be selected directly within the views for further inspection or modification, or for use as query objects in subsequent queries. Our image database query system provides visual feedback and manipulation of spatial query objects, multiple views of volume data, and the ability to combine spatial and symbolic queries. The system allows for incremental enhancement of existing objects and the addition of new objects and spatial relationships. The query system is designed for databases containing symbolic and spatial data. This paper discuses its application to data acquired in biomedical 3- D image reconstruction, but it is applicable to other areas such as CAD/CAM, geographical information systems, and computer vision.

Terahertz in-line digital holography of human hepatocellular carcinoma tissue.

PubMed

Rong, Lu; Latychevskaia, Tatiana; Chen, Chunhai; Wang, Dayong; Yu, Zhengping; Zhou, Xun; Li, Zeyu; Huang, Haochong; Wang, Yunxin; Zhou, Zhou

2015-02-13

Terahertz waves provide a better contrast in imaging soft biomedical tissues than X-rays, and unlike X-rays, they cause no ionisation damage, making them a good option for biomedical imaging. Terahertz absorption imaging has conventionally been used for cancer diagnosis. However, the absorption properties of a cancerous sample are influenced by two opposing factors: an increase in absorption due to a higher degree of hydration and a decrease in absorption due to structural changes. It is therefore difficult to diagnose cancer from an absorption image. Phase imaging can thus be critical for diagnostics. We demonstrate imaging of the absorption and phase-shift distributions of 3.2 mm × 2.3 mm × 30-μm-thick human hepatocellular carcinoma tissue by continuous-wave terahertz digital in-line holography. The acquisition time of a few seconds for a single in-line hologram is much shorter than that of other terahertz diagnostic techniques, and future detectors will allow acquisition of meaningful holograms without sample dehydration. The resolution of the reconstructions was enhanced by sub-pixel shifting and extrapolation. Another advantage of this technique is its relaxed minimal sample size limitation. The fibrosis indicated in the phase distribution demonstrates the potential of terahertz holographic imaging to obtain a more objective, early diagnosis of cancer.

Terahertz in-line digital holography of human hepatocellular carcinoma tissue

PubMed Central

Rong, Lu; Latychevskaia, Tatiana; Chen, Chunhai; Wang, Dayong; Yu, Zhengping; Zhou, Xun; Li, Zeyu; Huang, Haochong; Wang, Yunxin; Zhou, Zhou

2015-01-01

Terahertz waves provide a better contrast in imaging soft biomedical tissues than X-rays, and unlike X-rays, they cause no ionisation damage, making them a good option for biomedical imaging. Terahertz absorption imaging has conventionally been used for cancer diagnosis. However, the absorption properties of a cancerous sample are influenced by two opposing factors: an increase in absorption due to a higher degree of hydration and a decrease in absorption due to structural changes. It is therefore difficult to diagnose cancer from an absorption image. Phase imaging can thus be critical for diagnostics. We demonstrate imaging of the absorption and phase-shift distributions of 3.2 mm × 2.3 mm × 30-μm-thick human hepatocellular carcinoma tissue by continuous-wave terahertz digital in-line holography. The acquisition time of a few seconds for a single in-line hologram is much shorter than that of other terahertz diagnostic techniques, and future detectors will allow acquisition of meaningful holograms without sample dehydration. The resolution of the reconstructions was enhanced by sub-pixel shifting and extrapolation. Another advantage of this technique is its relaxed minimal sample size limitation. The fibrosis indicated in the phase distribution demonstrates the potential of terahertz holographic imaging to obtain a more objective, early diagnosis of cancer. PMID:25676705

Terahertz in-line digital holography of human hepatocellular carcinoma tissue

NASA Astrophysics Data System (ADS)

Rong, Lu; Latychevskaia, Tatiana; Chen, Chunhai; Wang, Dayong; Yu, Zhengping; Zhou, Xun; Li, Zeyu; Huang, Haochong; Wang, Yunxin; Zhou, Zhou

2015-02-01

Terahertz waves provide a better contrast in imaging soft biomedical tissues than X-rays, and unlike X-rays, they cause no ionisation damage, making them a good option for biomedical imaging. Terahertz absorption imaging has conventionally been used for cancer diagnosis. However, the absorption properties of a cancerous sample are influenced by two opposing factors: an increase in absorption due to a higher degree of hydration and a decrease in absorption due to structural changes. It is therefore difficult to diagnose cancer from an absorption image. Phase imaging can thus be critical for diagnostics. We demonstrate imaging of the absorption and phase-shift distributions of 3.2 mm × 2.3 mm × 30-μm-thick human hepatocellular carcinoma tissue by continuous-wave terahertz digital in-line holography. The acquisition time of a few seconds for a single in-line hologram is much shorter than that of other terahertz diagnostic techniques, and future detectors will allow acquisition of meaningful holograms without sample dehydration. The resolution of the reconstructions was enhanced by sub-pixel shifting and extrapolation. Another advantage of this technique is its relaxed minimal sample size limitation. The fibrosis indicated in the phase distribution demonstrates the potential of terahertz holographic imaging to obtain a more objective, early diagnosis of cancer.

Simultaneous in vivo positron emission tomography and magnetic resonance imaging.

PubMed

Catana, Ciprian; Procissi, Daniel; Wu, Yibao; Judenhofer, Martin S; Qi, Jinyi; Pichler, Bernd J; Jacobs, Russell E; Cherry, Simon R

2008-03-11

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are widely used in vivo imaging technologies with both clinical and biomedical research applications. The strengths of MRI include high-resolution, high-contrast morphologic imaging of soft tissues; the ability to image physiologic parameters such as diffusion and changes in oxygenation level resulting from neuronal stimulation; and the measurement of metabolites using chemical shift imaging. PET images the distribution of biologically targeted radiotracers with high sensitivity, but images generally lack anatomic context and are of lower spatial resolution. Integration of these technologies permits the acquisition of temporally correlated data showing the distribution of PET radiotracers and MRI contrast agents or MR-detectable metabolites, with registration to the underlying anatomy. An MRI-compatible PET scanner has been built for biomedical research applications that allows data from both modalities to be acquired simultaneously. Experiments demonstrate no effect of the MRI system on the spatial resolution of the PET system and <10% reduction in the fraction of radioactive decay events detected by the PET scanner inside the MRI. The signal-to-noise ratio and uniformity of the MR images, with the exception of one particular pulse sequence, were little affected by the presence of the PET scanner. In vivo simultaneous PET and MRI studies were performed in mice. Proof-of-principle in vivo MR spectroscopy and functional MRI experiments were also demonstrated with the combined scanner.

Comprehensive, powerful, efficient, intuitive: a new software framework for clinical imaging applications

NASA Astrophysics Data System (ADS)

Augustine, Kurt E.; Holmes, David R., III; Hanson, Dennis P.; Robb, Richard A.

2006-03-01

One of the greatest challenges for a software engineer is to create a complex application that is comprehensive enough to be useful to a diverse set of users, yet focused enough for individual tasks to be carried out efficiently with minimal training. This "powerful yet simple" paradox is particularly prevalent in advanced medical imaging applications. Recent research in the Biomedical Imaging Resource (BIR) at Mayo Clinic has been directed toward development of an imaging application framework that provides powerful image visualization/analysis tools in an intuitive, easy-to-use interface. It is based on two concepts very familiar to physicians - Cases and Workflows. Each case is associated with a unique patient and a specific set of routine clinical tasks, or a workflow. Each workflow is comprised of an ordered set of general-purpose modules which can be re-used for each unique workflow. Clinicians help describe and design the workflows, and then are provided with an intuitive interface to both patient data and analysis tools. Since most of the individual steps are common to many different workflows, the use of general-purpose modules reduces development time and results in applications that are consistent, stable, and robust. While the development of individual modules may reflect years of research by imaging scientists, new customized workflows based on the new modules can be developed extremely fast. If a powerful, comprehensive application is difficult to learn and complicated to use, it will be unacceptable to most clinicians. Clinical image analysis tools must be intuitive and effective or they simply will not be used.

Design of Biomedical Robots for Phenotype Prediction Problems

PubMed Central

deAndrés-Galiana, Enrique J.; Sonis, Stephen T.

2016-01-01

Abstract Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem. PMID:27347715

Design of Biomedical Robots for Phenotype Prediction Problems.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan Luis; Sonis, Stephen T

2016-08-01

Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.

Evaluating the operational risks of biomedical waste using failure mode and effects analysis.

PubMed

Chen, Ying-Chu; Tsai, Pei-Yi

2017-06-01

The potential problems and risks of biomedical waste generation have become increasingly apparent in recent years. This study applied a failure mode and effects analysis to evaluate the operational problems and risks of biomedical waste. The microbiological contamination of biomedical waste seldom receives the attention of researchers. In this study, the biomedical waste lifecycle was divided into seven processes: Production, classification, packaging, sterilisation, weighing, storage, and transportation. Twenty main failure modes were identified in these phases and risks were assessed based on their risk priority numbers. The failure modes in the production phase accounted for the highest proportion of the risk priority number score (27.7%). In the packaging phase, the failure mode 'sharp articles not placed in solid containers' had the highest risk priority number score, mainly owing to its high severity rating. The sterilisation process is the main difference in the treatment of infectious and non-infectious biomedical waste. The failure modes in the sterilisation phase were mainly owing to human factors (mostly related to operators). This study increases the understanding of the potential problems and risks associated with biomedical waste, thereby increasing awareness of how to improve the management of biomedical waste to better protect workers, the public, and the environment.

[International regulation of ethics committees on biomedical research as protection mechanisms for people: analysis of the Additional Protocol to the Convention on Human Rights and Biomedicine, concerning Biomedical Research of the Council of Europe].

PubMed

de Lecuona, Itziar

2013-01-01

The article explores and analyses the content of the Council of Europe's Additional Protocol to the Convention on Human Rights and Biomedicine concerning Biomedical Research regarding the standard legal instrument in biomedical research, issued by an international organization with leadership in bioethics. This implies ethics committees are mechanisms of protection of humans in biomedical research and not mere bureaucratic agencies and that a sound inescapable international regulatory framework exists for States to regulate biomedical research. The methodology used focuses on the analysis of the background, the context in which it is made and the nature and scope of the Protocol. It also identifies and analyses the characteristics and functions of ethics committees in biomedical research and, in particular, the information that should be provided to this bodies to develop their functions previously, during and at the end of research projects. This analysis will provide guidelines, suggestions and conclusions for the awareness and training of members of these committees in order to influence the daily practice. This paper may also be of interest to legal practitioners who work in different areas of biomedical research. From this practical perspective, the article examines the legal treatment of the Protocol to meet new challenges and classic issues in research: the treatment of human biological samples, the use of placebos, avoiding double standards, human vulnerability, undue influence and conflicts of interest, among others. Also, from a critical view, this work links the legal responses to develop work procedures that are required for an effective performance of the functions assigned of ethics committees in biomedical research. An existing international legal response that lacks doctrinal standards and provides little support should, however, serve as a guide and standard to develop actions that allow ethics committees -as key bodies for States- to advance in the protection of humans in biomedical research.

Accessing and integrating data and knowledge for biomedical research.

PubMed

Burgun, A; Bodenreider, O

2008-01-01

To review the issues that have arisen with the advent of translational research in terms of integration of data and knowledge, and survey current efforts to address these issues. Using examples form the biomedical literature, we identified new trends in biomedical research and their impact on bioinformatics. We analyzed the requirements for effective knowledge repositories and studied issues in the integration of biomedical knowledge. New diagnostic and therapeutic approaches based on gene expression patterns have brought about new issues in the statistical analysis of data, and new workflows are needed are needed to support translational research. Interoperable data repositories based on standard annotations, infrastructures and services are needed to support the pooling and meta-analysis of data, as well as their comparison to earlier experiments. High-quality, integrated ontologies and knowledge bases serve as a source of prior knowledge used in combination with traditional data mining techniques and contribute to the development of more effective data analysis strategies. As biomedical research evolves from traditional clinical and biological investigations towards omics sciences and translational research, specific needs have emerged, including integrating data collected in research studies with patient clinical data, linking omics knowledge with medical knowledge, modeling the molecular basis of diseases, and developing tools that support in-depth analysis of research data. As such, translational research illustrates the need to bridge the gap between bioinformatics and medical informatics, and opens new avenues for biomedical informatics research.

A practical approach to spectral calibration of short wavelength infrared hyper-spectral imaging systems

NASA Astrophysics Data System (ADS)

Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2010-02-01

Near-infrared spectroscopy is a promising, rapidly developing, reliable and noninvasive technique, used extensively in the biomedicine and in pharmaceutical industry. With the introduction of acousto-optic tunable filters (AOTF) and highly sensitive InGaAs focal plane sensor arrays, real-time high resolution hyper-spectral imaging has become feasible for a number of new biomedical in vivo applications. However, due to the specificity of the AOTF technology and lack of spectral calibration standardization, maintaining long-term stability and compatibility of the acquired hyper-spectral images across different systems is still a challenging problem. Efficiently solving both is essential as the majority of methods for analysis of hyper-spectral images relay on a priori knowledge extracted from large spectral databases, serving as the basis for reliable qualitative or quantitative analysis of various biological samples. In this study, we propose and evaluate fast and reliable spectral calibration of hyper-spectral imaging systems in the short wavelength infrared spectral region. The proposed spectral calibration method is based on light sources or materials, exhibiting distinct spectral features, which enable robust non-rigid registration of the acquired spectra. The calibration accounts for all of the components of a typical hyper-spectral imaging system such as AOTF, light source, lens and optical fibers. The obtained results indicated that practical, fast and reliable spectral calibration of hyper-spectral imaging systems is possible, thereby assuring long-term stability and inter-system compatibility of the acquired hyper-spectral images.

Parallel, confocal, and complete spectrum imager for fluorescent detection of high-density microarray

NASA Astrophysics Data System (ADS)

Bogdanov, Valery L.; Boyce-Jacino, Michael

1999-05-01

Confined arrays of biochemical probes deposited on a solid support surface (analytical microarray or 'chip') provide an opportunity to analysis multiple reactions simultaneously. Microarrays are increasingly used in genetics, medicine and environment scanning as research and analytical instruments. A power of microarray technology comes from its parallelism which grows with array miniaturization, minimization of reagent volume per reaction site and reaction multiplexing. An optical detector of microarray signals should combine high sensitivity, spatial and spectral resolution. Additionally, low-cost and a high processing rate are needed to transfer microarray technology into biomedical practice. We designed an imager that provides confocal and complete spectrum detection of entire fluorescently-labeled microarray in parallel. Imager uses microlens array, non-slit spectral decomposer, and high- sensitive detector (cooled CCD). Two imaging channels provide a simultaneous detection of localization, integrated and spectral intensities for each reaction site in microarray. A dimensional matching between microarray and imager's optics eliminates all in moving parts in instrumentation, enabling highly informative, fast and low-cost microarray detection. We report theory of confocal hyperspectral imaging with microlenses array and experimental data for implementation of developed imager to detect fluorescently labeled microarray with a density approximately 103 sites per cm2.

Hybrid-modality high-resolution imaging: for diagnostic biomedical imaging and sensing for disease diagnosis

NASA Astrophysics Data System (ADS)

Murukeshan, Vadakke M.; Hoong Ta, Lim

2014-11-01

Medical diagnostics in the recent past has seen the challenging trend to come up with dual and multi-modality imaging for implementing better diagnostic procedures. The changes in tissues in the early disease stages are often subtle and can occur beneath the tissue surface. In most of these cases, conventional types of medical imaging using optics may not be able to detect these changes easily due to its penetration depth of the orders of 1 mm. Each imaging modality has its own advantages and limitations, and the use of a single modality is not suitable for every diagnostic applications. Therefore the need for multi or hybrid-modality imaging arises. Combining more than one imaging modalities overcomes the limitation of individual imaging method and integrates the respective advantages into a single setting. In this context, this paper will be focusing on the research and development of two multi-modality imaging platforms. The first platform combines ultrasound and photoacoustic imaging for diagnostic applications in the eye. The second platform consists of optical hyperspectral and photoacoustic imaging for diagnostic applications in the colon. Photoacoustic imaging is used as one of the modalities in both platforms as it can offer deeper penetration depth compared to optical imaging. The optical engineering and research challenges in developing the dual/multi-modality platforms will be discussed, followed by initial results validating the proposed scheme. The proposed schemes offer high spatial and spectral resolution imaging and sensing, and is expected to offer potential biomedical imaging solutions in the near future.

Wavelets in medical imaging

NASA Astrophysics Data System (ADS)

Zahra, Noor e.; Sevindir, Hulya Kodal; Aslan, Zafer; Siddiqi, A. H.

2012-07-01

The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

Quantitative assessment of the impact of biomedical image acquisition on the results obtained from image analysis and processing.

PubMed

Koprowski, Robert

2014-07-04

Dedicated, automatic algorithms for image analysis and processing are becoming more and more common in medical diagnosis. When creating dedicated algorithms, many factors must be taken into consideration. They are associated with selecting the appropriate algorithm parameters and taking into account the impact of data acquisition on the results obtained. An important feature of algorithms is the possibility of their use in other medical units by other operators. This problem, namely operator's (acquisition) impact on the results obtained from image analysis and processing, has been shown on a few examples. The analysed images were obtained from a variety of medical devices such as thermal imaging, tomography devices and those working in visible light. The objects of imaging were cellular elements, the anterior segment and fundus of the eye, postural defects and others. In total, almost 200'000 images coming from 8 different medical units were analysed. All image analysis algorithms were implemented in C and Matlab. For various algorithms and methods of medical imaging, the impact of image acquisition on the results obtained is different. There are different levels of algorithm sensitivity to changes in the parameters, for example: (1) for microscope settings and the brightness assessment of cellular elements there is a difference of 8%; (2) for the thyroid ultrasound images there is a difference in marking the thyroid lobe area which results in a brightness assessment difference of 2%. The method of image acquisition in image analysis and processing also affects: (3) the accuracy of determining the temperature in the characteristic areas on the patient's back for the thermal method - error of 31%; (4) the accuracy of finding characteristic points in photogrammetric images when evaluating postural defects - error of 11%; (5) the accuracy of performing ablative and non-ablative treatments in cosmetology - error of 18% for the nose, 10% for the cheeks, and 7% for the forehead. Similarly, when: (7) measuring the anterior eye chamber - there is an error of 20%; (8) measuring the tooth enamel thickness - error of 15%; (9) evaluating the mechanical properties of the cornea during pressure measurement - error of 47%. The paper presents vital, selected issues occurring when assessing the accuracy of designed automatic algorithms for image analysis and processing in bioengineering. The impact of acquisition of images on the problems arising in their analysis has been shown on selected examples. It has also been indicated to which elements of image analysis and processing special attention should be paid in their design.