Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures.

PubMed

Wong, Kelvin K L; Wang, Defeng; Ko, Jacky K L; Mazumdar, Jagannath; Le, Thu-Thao; Ghista, Dhanjoo

2017-03-21

Cardiac dysfunction constitutes common cardiovascular health issues in the society, and has been an investigation topic of strong focus by researchers in the medical imaging community. Diagnostic modalities based on echocardiography, magnetic resonance imaging, chest radiography and computed tomography are common techniques that provide cardiovascular structural information to diagnose heart defects. However, functional information of cardiovascular flow, which can in fact be used to support the diagnosis of many cardiovascular diseases with a myriad of hemodynamics performance indicators, remains unexplored to its full potential. Some of these indicators constitute important cardiac functional parameters affecting the cardiovascular abnormalities. With the advancement of computer technology that facilitates high speed computational fluid dynamics, the realization of a support diagnostic platform of hemodynamics quantification and analysis can be achieved. This article reviews the state-of-the-art medical imaging and high fidelity multi-physics computational analyses that together enable reconstruction of cardiovascular structures and hemodynamic flow patterns within them, such as of the left ventricle (LV) and carotid bifurcations. The combined medical imaging and hemodynamic analysis enables us to study the mechanisms of cardiovascular disease-causing dysfunctions, such as how (1) cardiomyopathy causes left ventricular remodeling and loss of contractility leading to heart failure, and (2) modeling of LV construction and simulation of intra-LV hemodynamics can enable us to determine the optimum procedure of surgical ventriculation to restore its contractility and health This combined medical imaging and hemodynamics framework can potentially extend medical knowledge of cardiovascular defects and associated hemodynamic behavior and their surgical restoration, by means of an integrated medical image diagnostics and hemodynamic performance analysis framework.

DICOMGrid: a middleware to integrate PACS and EELA-2 grid infrastructure

NASA Astrophysics Data System (ADS)

Moreno, Ramon A.; de Sá Rebelo, Marina; Gutierrez, Marco A.

2010-03-01

Medical images provide lots of information for physicians, but the huge amount of data produced by medical image equipments in a modern Health Institution is not completely explored in its full potential yet. Nowadays medical images are used in hospitals mostly as part of routine activities while its intrinsic value for research is underestimated. Medical images can be used for the development of new visualization techniques, new algorithms for patient care and new image processing techniques. These research areas usually require the use of huge volumes of data to obtain significant results, along with enormous computing capabilities. Such qualities are characteristics of grid computing systems such as EELA-2 infrastructure. The grid technologies allow the sharing of data in large scale in a safe and integrated environment and offer high computing capabilities. In this paper we describe the DicomGrid to store and retrieve medical images, properly anonymized, that can be used by researchers to test new processing techniques, using the computational power offered by grid technology. A prototype of the DicomGrid is under evaluation and permits the submission of jobs into the EELA-2 grid infrastructure while offering a simple interface that requires minimal understanding of the grid operation.

Towards Portable Large-Scale Image Processing with High-Performance Computing.

PubMed

Huo, Yuankai; Blaber, Justin; Damon, Stephen M; Boyd, Brian D; Bao, Shunxing; Parvathaneni, Prasanna; Noguera, Camilo Bermudez; Chaganti, Shikha; Nath, Vishwesh; Greer, Jasmine M; Lyu, Ilwoo; French, William R; Newton, Allen T; Rogers, Baxter P; Landman, Bennett A

2018-05-03

High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called "spiders." The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software development and expansion, and (3) scalable spider deployment compatible with HPC clusters and local workstations.

Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.

PubMed

Widmer, Antoine; Schaer, Roger; Markonis, Dimitrios; Muller, Henning

2014-01-01

Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover many useful applications, also in the medical domain. For example, Google Glass can easily provide video conference between medical doctors to discuss a live case. Using these glasses can also facilitate medical information search by allowing the access of a large amount of annotated medical cases during a consultation in a non-disruptive fashion for medical staff. In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.

Medical image analysis with artificial neural networks.

PubMed

Jiang, J; Trundle, P; Ren, J

2010-12-01

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

Computer Human Interaction for Image Information Systems.

ERIC Educational Resources Information Center

Beard, David Volk

1991-01-01

Presents an approach to developing viable image computer-human interactions (CHI) involving user metaphors for comprehending image data and methods for locating, accessing, and displaying computer images. A medical-image radiology workstation application is used as an example, and feedback and evaluation methods are discussed. (41 references) (LRW)

A Framework for Integration of Heterogeneous Medical Imaging Networks

PubMed Central

Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

2014-01-01

Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS. PMID:25279021

A framework for integration of heterogeneous medical imaging networks.

PubMed

Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

2014-01-01

Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS.

Advantages and disadvantages of computer imaging in cosmetic surgery.

PubMed

Koch, R J; Chavez, A; Dagum, P; Newman, J P

1998-02-01

Despite the growing popularity of computer imaging systems, it is not clear whether the medical and legal advantages of using such a system outweigh the disadvantages. The purpose of this report is to evaluate these aspects, and provide some protective guidelines in the use of computer imaging in cosmetic surgery. The positive and negative aspects of computer imaging from a medical and legal perspective are reviewed. Also, specific issues are examined by a legal panel. The greatest advantages are potential problem patient exclusion, and enhanced physician-patient communication. Disadvantages include cost, user learning curve, and potential liability. Careful use of computer imaging should actually reduce one's liability when all aspects are considered. Recommendations for such use and specific legal issues are discussed.

GPU Accelerated Ultrasonic Tomography Using Propagation and Back Propagation Method

DTIC Science & Technology

2015-09-28

the medical imaging field using GPUs has been done for many years. In [1], Copeland et al. used 2D images , obtained by X - ray projections, to...Index Terms— Medical Imaging , Ultrasonic Tomography, GPU, CUDA, Parallel Computing I. INTRODUCTION GRAPHIC Processing Units (GPUs) are computation... Imaging Algorithm The process of reconstructing images from ultrasonic infor- mation starts with the following acoustical wave equation: ∂2 ∂t2 u ( x

Partitioning medical image databases for content-based queries on a Grid.

PubMed

Montagnat, J; Breton, V; E Magnin, I

2005-01-01

In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Grids are promising for content-based image retrieval in medical databases.

Medical physics: some recollections in diagnostic X-ray imaging and therapeutic radiology.

PubMed

Gray, J E; Orton, C G

2000-12-01

Medical physics has changed dramatically since 1895. There was a period of slow evolutionary change during the first 70 years after Roentgen's discovery of x rays. With the advent of the computer, however, both diagnostic and therapeutic radiology have undergone rapid growth and changes. Technologic advances such as computed tomography and magnetic resonance imaging in diagnostic imaging and three-dimensional treatment planning systems, stereotactic radiosurgery, and intensity modulated radiation therapy in radiation oncology have resulted in substantial changes in medical physics. These advances have improved diagnostic imaging and radiation therapy while expanding the need for better educated and experienced medical physics staff.

Post-processing methods of rendering and visualizing 3-D reconstructed tomographic images

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

Wong, S.T.C.

The purpose of this presentation is to discuss the computer processing techniques of tomographic images, after they have been generated by imaging scanners, for volume visualization. Volume visualization is concerned with the representation, manipulation, and rendering of volumetric data. Since the first digital images were produced from computed tomography (CT) scanners in the mid 1970s, applications of visualization in medicine have expanded dramatically. Today, three-dimensional (3D) medical visualization has expanded from using CT data, the first inherently digital source of 3D medical data, to using data from various medical imaging modalities, including magnetic resonance scanners, positron emission scanners, digital ultrasound,more » electronic and confocal microscopy, and other medical imaging modalities. We have advanced from rendering anatomy to aid diagnosis and visualize complex anatomic structures to planning and assisting surgery and radiation treatment. New, more accurate and cost-effective procedures for clinical services and biomedical research have become possible by integrating computer graphics technology with medical images. This trend is particularly noticeable in current market-driven health care environment. For example, interventional imaging, image-guided surgery, and stereotactic and visualization techniques are now stemming into surgical practice. In this presentation, we discuss only computer-display-based approaches of volumetric medical visualization. That is, we assume that the display device available is two-dimensional (2D) in nature and all analysis of multidimensional image data is to be carried out via the 2D screen of the device. There are technologies such as holography and virtual reality that do provide a {open_quotes}true 3D screen{close_quotes}. To confine the scope, this presentation will not discuss such approaches.« less

Medical image processing on the GPU - past, present and future.

PubMed

Eklund, Anders; Dufort, Paul; Forsberg, Daniel; LaConte, Stephen M

2013-12-01

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges. Copyright © 2013 Elsevier B.V. All rights reserved.

Deep Learning in Medical Image Analysis.

PubMed

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

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

Arimura, Hidetaka, E-mail: arimurah@med.kyushu-u.ac.jp; Kamezawa, Hidemi; Jin, Ze

Good relationships between computational image analysis and radiological physics have been constructed for increasing the accuracy of medical diagnostic imaging and radiation therapy in radiological physics. Computational image analysis has been established based on applied mathematics, physics, and engineering. This review paper will introduce how computational image analysis is useful in radiation therapy with respect to radiological physics.

Microelectronics and Computers in Medicine.

ERIC Educational Resources Information Center

Meindl, James D.

1982-01-01

The use of microelectronics and computers in medicine is reviewed, focusing on medical research; medical data collection, storage, retrieval, and manipulation; medical decision making; computed tomography; ultrasonic imaging; role in clinical laboratories; and use as adjuncts for diagnostic tests, monitors of critically-ill patients, and with the…

Image-guided tissue engineering

PubMed Central

Ballyns, Jeffrey J; Bonassar, Lawrence J

2009-01-01

Replication of anatomic shape is a significant challenge in developing implants for regenerative medicine. This has lead to significant interest in using medical imaging techniques such as magnetic resonance imaging and computed tomography to design tissue engineered constructs. Implementation of medical imaging and computer aided design in combination with technologies for rapid prototyping of living implants enables the generation of highly reproducible constructs with spatial resolution up to 25 μm. In this paper, we review the medical imaging modalities available and a paradigm for choosing a particular imaging technique. We also present fabrication techniques and methodologies for producing cellular engineered constructs. Finally, we comment on future challenges involved with image guided tissue engineering and efforts to generate engineered constructs ready for implantation. PMID:19583811

Viewpoints on Medical Image Processing: From Science to Application

PubMed Central

Deserno (né Lehmann), Thomas M.; Handels, Heinz; Maier-Hein (né Fritzsche), Klaus H.; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-01-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment. PMID:24078804

Viewpoints on Medical Image Processing: From Science to Application.

PubMed

Deserno Né Lehmann, Thomas M; Handels, Heinz; Maier-Hein Né Fritzsche, Klaus H; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-05-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.

Computer-aided diagnosis workstation and teleradiology network system for chest diagnosis using the web medical image conference system with a new information security solution

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kaneko, Masahiro; Kakinuma, Ryutaro; Moriyama, Noriyuki

2010-03-01

Diagnostic MDCT imaging requires a considerable number of images to be read. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. Because of such a background, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis. We also have developed the teleradiology network system by using web medical image conference system. In the teleradiology network system, the security of information network is very important subjects. Our teleradiology network system can perform Web medical image conference in the medical institutions of a remote place using the web medical image conference system. We completed the basic proof experiment of the web medical image conference system with information security solution. We can share the screen of web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with the workstation that builds in some diagnostic assistance methods. Biometric face authentication used on site of teleradiology makes "Encryption of file" and "Success in login" effective. Our Privacy and information security technology of information security solution ensures compliance with Japanese regulations. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new teleradiology network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our teleradiology network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

Automated image quality assessment for chest CT scans.

PubMed

Reeves, Anthony P; Xie, Yiting; Liu, Shuang

2018-02-01

Medical image quality needs to be maintained at standards sufficient for effective clinical reading. Automated computer analytic methods may be applied to medical images for quality assessment. For chest CT scans in a lung cancer screening context, an automated quality assessment method is presented that characterizes image noise and image intensity calibration. This is achieved by image measurements in three automatically segmented homogeneous regions of the scan: external air, trachea lumen air, and descending aorta blood. Profiles of CT scanner behavior are also computed. The method has been evaluated on both phantom and real low-dose chest CT scans and results show that repeatable noise and calibration measures may be realized by automated computer algorithms. Noise and calibration profiles show relevant differences between different scanners and protocols. Automated image quality assessment may be useful for quality control for lung cancer screening and may enable performance improvements to automated computer analysis methods. © 2017 American Association of Physicists in Medicine.

A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images.

PubMed

Du, Xiaogang; Dang, Jianwu; Wang, Yangping; Wang, Song; Lei, Tao

2016-01-01

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).

Overview of deep learning in medical imaging.

PubMed

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Deep Learning in Medical Image Analysis

PubMed Central

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2016-01-01

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements. PMID:28301734

Monte Carlo simulations of medical imaging modalities

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

Estes, G.P.

Because continuous-energy Monte Carlo radiation transport calculations can be nearly exact simulations of physical reality (within data limitations, geometric approximations, transport algorithms, etc.), it follows that one should be able to closely approximate the results of many experiments from first-principles computations. This line of reasoning has led to various MCNP studies that involve simulations of medical imaging modalities and other visualization methods such as radiography, Anger camera, computerized tomography (CT) scans, and SABRINA particle track visualization. It is the intent of this paper to summarize some of these imaging simulations in the hope of stimulating further work, especially as computermore » power increases. Improved interpretation and prediction of medical images should ultimately lead to enhanced medical treatments. It is also reasonable to assume that such computations could be used to design new or more effective imaging instruments.« less

Cloud based emergency health care information service in India.

PubMed

Karthikeyan, N; Sukanesh, R

2012-12-01

A hospital is a health care organization providing patient treatment by expert physicians, surgeons and equipments. A report from a health care accreditation group says that miscommunication between patients and health care providers is the reason for the gap in providing emergency medical care to people in need. In developing countries, illiteracy is the major key root for deaths resulting from uncertain diseases constituting a serious public health problem. Mentally affected, differently abled and unconscious patients can't communicate about their medical history to the medical practitioners. Also, Medical practitioners can't edit or view DICOM images instantly. Our aim is to provide palm vein pattern recognition based medical record retrieval system, using cloud computing for the above mentioned people. Distributed computing technology is coming in the new forms as Grid computing and Cloud computing. These new forms are assured to bring Information Technology (IT) as a service. In this paper, we have described how these new forms of distributed computing will be helpful for modern health care industries. Cloud Computing is germinating its benefit to industrial sectors especially in medical scenarios. In Cloud Computing, IT-related capabilities and resources are provided as services, via the distributed computing on-demand. This paper is concerned with sprouting software as a service (SaaS) by means of Cloud computing with an aim to bring emergency health care sector in an umbrella with physical secured patient records. In framing the emergency healthcare treatment, the crucial thing considered necessary to decide about patients is their previous health conduct records. Thus a ubiquitous access to appropriate records is essential. Palm vein pattern recognition promises a secured patient record access. Likewise our paper reveals an efficient means to view, edit or transfer the DICOM images instantly which was a challenging task for medical practitioners in the past years. We have developed two services for health care. 1. Cloud based Palm vein recognition system 2. Distributed Medical image processing tools for medical practitioners.

Distributed Network, Wireless and Cloud Computing Enabled 3-D Ultrasound; a New Medical Technology Paradigm

PubMed Central

Meir, Arie; Rubinsky, Boris

2009-01-01

Medical technologies are indispensable to modern medicine. However, they have become exceedingly expensive and complex and are not available to the economically disadvantaged majority of the world population in underdeveloped as well as developed parts of the world. For example, according to the World Health Organization about two thirds of the world population does not have access to medical imaging. In this paper we introduce a new medical technology paradigm centered on wireless technology and cloud computing that was designed to overcome the problems of increasing health technology costs. We demonstrate the value of the concept with an example; the design of a wireless, distributed network and central (cloud) computing enabled three-dimensional (3-D) ultrasound system. Specifically, we demonstrate the feasibility of producing a 3-D high end ultrasound scan at a central computing facility using the raw data acquired at the remote patient site with an inexpensive low end ultrasound transducer designed for 2-D, through a mobile device and wireless connection link between them. Producing high-end 3D ultrasound images with simple low-end transducers reduces the cost of imaging by orders of magnitude. It also removes the requirement of having a highly trained imaging expert at the patient site, since the need for hand-eye coordination and the ability to reconstruct a 3-D mental image from 2-D scans, which is a necessity for high quality ultrasound imaging, is eliminated. This could enable relatively untrained medical workers in developing nations to administer imaging and a more accurate diagnosis, effectively saving the lives of people. PMID:19936236

Distributed network, wireless and cloud computing enabled 3-D ultrasound; a new medical technology paradigm.

PubMed

Meir, Arie; Rubinsky, Boris

2009-11-19

Medical technologies are indispensable to modern medicine. However, they have become exceedingly expensive and complex and are not available to the economically disadvantaged majority of the world population in underdeveloped as well as developed parts of the world. For example, according to the World Health Organization about two thirds of the world population does not have access to medical imaging. In this paper we introduce a new medical technology paradigm centered on wireless technology and cloud computing that was designed to overcome the problems of increasing health technology costs. We demonstrate the value of the concept with an example; the design of a wireless, distributed network and central (cloud) computing enabled three-dimensional (3-D) ultrasound system. Specifically, we demonstrate the feasibility of producing a 3-D high end ultrasound scan at a central computing facility using the raw data acquired at the remote patient site with an inexpensive low end ultrasound transducer designed for 2-D, through a mobile device and wireless connection link between them. Producing high-end 3D ultrasound images with simple low-end transducers reduces the cost of imaging by orders of magnitude. It also removes the requirement of having a highly trained imaging expert at the patient site, since the need for hand-eye coordination and the ability to reconstruct a 3-D mental image from 2-D scans, which is a necessity for high quality ultrasound imaging, is eliminated. This could enable relatively untrained medical workers in developing nations to administer imaging and a more accurate diagnosis, effectively saving the lives of people.

Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

2009-02-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

A RONI Based Visible Watermarking Approach for Medical Image Authentication.

PubMed

Thanki, Rohit; Borra, Surekha; Dwivedi, Vedvyas; Borisagar, Komal

2017-08-09

Nowadays medical data in terms of image files are often exchanged between different hospitals for use in telemedicine and diagnosis. Visible watermarking being extensively used for Intellectual Property identification of such medical images, leads to serious issues if failed to identify proper regions for watermark insertion. In this paper, the Region of Non-Interest (RONI) based visible watermarking for medical image authentication is proposed. In this technique, to RONI of the cover medical image is first identified using Human Visual System (HVS) model. Later, watermark logo is visibly inserted into RONI of the cover medical image to get watermarked medical image. Finally, the watermarked medical image is compared with the original medical image for measurement of imperceptibility and authenticity of proposed scheme. The experimental results showed that this proposed scheme reduces the computational complexity and improves the PSNR when compared to many existing schemes.

Neural networks: Application to medical imaging

NASA Technical Reports Server (NTRS)

Clarke, Laurence P.

1994-01-01

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images

PubMed Central

Wang, Yangping; Wang, Song

2016-01-01

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU). PMID:28053653

A review of GPU-based medical image reconstruction.

PubMed

Després, Philippe; Jia, Xun

2017-10-01

Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units (GPUs), which are exploited as massively parallel computational engines. This review paper focuses on recent developments made in GPU-based medical image reconstruction, from a CT, PET, SPECT, MRI and US perspective. Strategies and approaches to get the most out of GPUs in image reconstruction are presented as well as innovative applications arising from an increased computing capacity. The future of GPU-based image reconstruction is also envisioned, based on current trends in high-performance computing. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

WE-D-303-00: Computational Phantoms

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

Lewis, John; Brigham and Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA

2015-06-15

Modern medical physics deals with complex problems such as 4D radiation therapy and imaging quality optimization. Such problems involve a large number of radiological parameters, and anatomical and physiological breathing patterns. A major challenge is how to develop, test, evaluate and compare various new imaging and treatment techniques, which often involves testing over a large range of radiological parameters as well as varying patient anatomies and motions. It would be extremely challenging, if not impossible, both ethically and practically, to test every combination of parameters and every task on every type of patient under clinical conditions. Computer-based simulation using computationalmore » phantoms offers a practical technique with which to evaluate, optimize, and compare imaging technologies and methods. Within simulation, the computerized phantom provides a virtual model of the patient’s anatomy and physiology. Imaging data can be generated from it as if it was a live patient using accurate models of the physics of the imaging and treatment process. With sophisticated simulation algorithms, it is possible to perform virtual experiments entirely on the computer. By serving as virtual patients, computational phantoms hold great promise in solving some of the most complex problems in modern medical physics. In this proposed symposium, we will present the history and recent developments of computational phantom models, share experiences in their application to advanced imaging and radiation applications, and discuss their promises and limitations. Learning Objectives: Understand the need and requirements of computational phantoms in medical physics research Discuss the developments and applications of computational phantoms Know the promises and limitations of computational phantoms in solving complex problems.« less

RayPlus: a Web-Based Platform for Medical Image Processing.

PubMed

Yuan, Rong; Luo, Ming; Sun, Zhi; Shi, Shuyue; Xiao, Peng; Xie, Qingguo

2017-04-01

Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.

Personalized, relevance-based Multimodal Robotic Imaging and augmented reality for Computer Assisted Interventions.

PubMed

Navab, Nassir; Fellow, Miccai; Hennersperger, Christoph; Frisch, Benjamin; Fürst, Bernhard

2016-10-01

In the last decade, many researchers in medical image computing and computer assisted interventions across the world focused on the development of the Virtual Physiological Human (VPH), aiming at changing the practice of medicine from classification and treatment of diseases to that of modeling and treating patients. These projects resulted in major advancements in segmentation, registration, morphological, physiological and biomechanical modeling based on state of art medical imaging as well as other sensory data. However, a major issue which has not yet come into the focus is personalizing intra-operative imaging, allowing for optimal treatment. In this paper, we discuss the personalization of imaging and visualization process with particular focus on satisfying the challenging requirements of computer assisted interventions. We discuss such requirements and review a series of scientific contributions made by our research team to tackle some of these major challenges. Copyright © 2016. Published by Elsevier B.V.

TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging

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

Badal, A; Zbijewski, W; Bolch, W

Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods,more » are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10{sup 7} xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the virtual generation of medical images and accurate estimation of radiation dose and other imaging parameters. For this, detailed computational phantoms of the patient anatomy must be utilized and implemented within the radiation transport code. Computational phantoms presently come in one of three format types, and in one of four morphometric categories. Format types include stylized (mathematical equation-based), voxel (segmented CT/MR images), and hybrid (NURBS and polygon mesh surfaces). Morphometric categories include reference (small library of phantoms by age at 50th height/weight percentile), patient-dependent (larger library of phantoms at various combinations of height/weight percentiles), patient-sculpted (phantoms altered to match the patient's unique outer body contour), and finally, patient-specific (an exact representation of the patient with respect to both body contour and internal anatomy). The existence and availability of these phantoms represents a very important advance for the simulation of realistic medical imaging applications using Monte Carlo methods. New Monte Carlo simulation codes need to be thoroughly validated before they can be used to perform novel research. Ideally, the validation process would involve comparison of results with those of an experimental measurement, but accurate replication of experimental conditions can be very challenging. It is very common to validate new Monte Carlo simulations by replicating previously published simulation results of similar experiments. This process, however, is commonly problematic due to the lack of sufficient information in the published reports of previous work so as to be able to replicate the simulation in detail. To aid in this process, the AAPM Task Group 195 prepared a report in which six different imaging research experiments commonly performed using Monte Carlo simulations are described and their results provided. The simulation conditions of all six cases are provided in full detail, with all necessary data on material composition, source, geometry, scoring and other parameters provided. The results of these simulations when performed with the four most common publicly available Monte Carlo packages are also provided in tabular form. The Task Group 195 Report will be useful for researchers needing to validate their Monte Carlo work, and for trainees needing to learn Monte Carlo simulation methods. In this symposium we will review the recent advancements in highperformance computing hardware enabling the reduction in computational resources needed for Monte Carlo simulations in medical imaging. We will review variance reduction techniques commonly applied in Monte Carlo simulations of medical imaging systems and present implementation strategies for efficient combination of these techniques with GPU acceleration. Trade-offs involved in Monte Carlo acceleration by means of denoising and “sparse sampling” will be discussed. A method for rapid scatter correction in cone-beam CT (<5 min/scan) will be presented as an illustration of the simulation speeds achievable with optimized Monte Carlo simulations. We will also discuss the development, availability, and capability of the various combinations of computational phantoms for Monte Carlo simulation of medical imaging systems. Finally, we will review some examples of experimental validation of Monte Carlo simulations and will present the AAPM Task Group 195 Report. Learning Objectives: Describe the advances in hardware available for performing Monte Carlo simulations in high performance computing environments. Explain variance reduction, denoising and sparse sampling techniques available for reduction of computational time needed for Monte Carlo simulations of medical imaging. List and compare the computational anthropomorphic phantoms currently available for more accurate assessment of medical imaging parameters in Monte Carlo simulations. Describe experimental methods used for validation of Monte Carlo simulations in medical imaging. Describe the AAPM Task Group 195 Report and its use for validation and teaching of Monte Carlo simulations in medical imaging.« less

Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.

PubMed

Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling

2016-05-01

Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches. Copyright © 2016 Elsevier Ltd. All rights reserved.

Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience.

PubMed

Kapur, Tina; Pieper, Steve; Fedorov, Andriy; Fillion-Robin, J-C; Halle, Michael; O'Donnell, Lauren; Lasso, Andras; Ungi, Tamas; Pinter, Csaba; Finet, Julien; Pujol, Sonia; Jagadeesan, Jayender; Tokuda, Junichi; Norton, Isaiah; Estepar, Raul San Jose; Gering, David; Aerts, Hugo J W L; Jakab, Marianna; Hata, Nobuhiko; Ibanez, Luiz; Blezek, Daniel; Miller, Jim; Aylward, Stephen; Grimson, W Eric L; Fichtinger, Gabor; Wells, William M; Lorensen, William E; Schroeder, Will; Kikinis, Ron

2016-10-01

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision. Copyright © 2016 Elsevier B.V. All rights reserved.

Machine Learning for Medical Imaging

PubMed Central

Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L.

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017 PMID:28212054

Machine Learning for Medical Imaging.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

MO-C-BRCD-03: The Role of Informatics in Medical Physics and Vice Versa.

PubMed

Andriole, K

2012-06-01

Like Medical Physics, Imaging Informatics encompasses concepts touching every aspect of the imaging chain from image creation, acquisition, management and archival, to image processing, analysis, display and interpretation. The two disciplines are in fact quite complementary, with similar goals to improve the quality of care provided to patients using an evidence-based approach, to assure safety in the clinical and research environments, to facilitate efficiency in the workplace, and to accelerate knowledge discovery. Use-cases describing several areas of informatics activity will be given to illustrate current limitations that would benefit from medical physicist participation, and conversely areas in which informaticists may contribute to the solution. Topics to be discussed include radiation dose monitoring, process management and quality control, display technologies, business analytics techniques, and quantitative imaging. Quantitative imaging is increasingly becoming an essential part of biomedicalresearch as well as being incorporated into clinical diagnostic activities. Referring clinicians are asking for more objective information to be gleaned from the imaging tests that they order so that they may make the best clinical management decisions for their patients. Medical Physicists may be called upon to identify existing issues as well as develop, validate and implement new approaches and technologies to help move the field further toward quantitative imaging methods for the future. Biomedical imaging informatics tools and techniques such as standards, integration, data mining, cloud computing and new systems architectures, ontologies and lexicons, data visualization and navigation tools, and business analytics applications can be used to overcome some of the existing limitations. 1. Describe what is meant by Medical Imaging Informatics and understand why the medical physicist should care. 2. Identify existing limitations in information technologies with respect to Medical Physics, and conversely see how Informatics may assist the medical physicist in filling some of the current gaps in their activities. 3. Understand general informatics concepts and areas of investigation including imaging and workflow standards, systems integration, computing architectures, ontologies, data mining and business analytics, data visualization and human-computer interface tools, and the importance of quantitative imaging for the future of Medical Physics and Imaging Informatics. 4. Become familiar with on-going efforts to address current challenges facing future research into and clinical implementation of quantitative imaging applications. © 2012 American Association of Physicists in Medicine.

Secure public cloud platform for medical images sharing.

PubMed

Pan, Wei; Coatrieux, Gouenou; Bouslimi, Dalel; Prigent, Nicolas

2015-01-01

Cloud computing promises medical imaging services offering large storage and computing capabilities for limited costs. In this data outsourcing framework, one of the greatest issues to deal with is data security. To do so, we propose to secure a public cloud platform devoted to medical image sharing by defining and deploying a security policy so as to control various security mechanisms. This policy stands on a risk assessment we conducted so as to identify security objectives with a special interest for digital content protection. These objectives are addressed by means of different security mechanisms like access and usage control policy, partial-encryption and watermarking.

Cloud computing in medical imaging.

PubMed

Kagadis, George C; Kloukinas, Christos; Moore, Kevin; Philbin, Jim; Papadimitroulas, Panagiotis; Alexakos, Christos; Nagy, Paul G; Visvikis, Dimitris; Hendee, William R

2013-07-01

Over the past century technology has played a decisive role in defining, driving, and reinventing procedures, devices, and pharmaceuticals in healthcare. Cloud computing has been introduced only recently but is already one of the major topics of discussion in research and clinical settings. The provision of extensive, easily accessible, and reconfigurable resources such as virtual systems, platforms, and applications with low service cost has caught the attention of many researchers and clinicians. Healthcare researchers are moving their efforts to the cloud, because they need adequate resources to process, store, exchange, and use large quantities of medical data. This Vision 20/20 paper addresses major questions related to the applicability of advanced cloud computing in medical imaging. The paper also considers security and ethical issues that accompany cloud computing.

High-performance floating-point image computing workstation for medical applications

NASA Astrophysics Data System (ADS)

Mills, Karl S.; Wong, Gilman K.; Kim, Yongmin

1990-07-01

The medical imaging field relies increasingly on imaging and graphics techniques in diverse applications with needs similar to (or more stringent than) those of the military, industrial and scientific communities. However, most image processing and graphics systems available for use in medical imaging today are either expensive, specialized, or in most cases both. High performance imaging and graphics workstations which can provide real-time results for a number of applications, while maintaining affordability and flexibility, can facilitate the application of digital image computing techniques in many different areas. This paper describes the hardware and software architecture of a medium-cost floating-point image processing and display subsystem for the NeXT computer, and its applications as a medical imaging workstation. Medical imaging applications of the workstation include use in a Picture Archiving and Communications System (PACS), in multimodal image processing and 3-D graphics workstation for a broad range of imaging modalities, and as an electronic alternator utilizing its multiple monitor display capability and large and fast frame buffer. The subsystem provides a 2048 x 2048 x 32-bit frame buffer (16 Mbytes of image storage) and supports both 8-bit gray scale and 32-bit true color images. When used to display 8-bit gray scale images, up to four different 256-color palettes may be used for each of four 2K x 2K x 8-bit image frames. Three of these image frames can be used simultaneously to provide pixel selectable region of interest display. A 1280 x 1024 pixel screen with 1: 1 aspect ratio can be windowed into the frame buffer for display of any portion of the processed image or images. In addition, the system provides hardware support for integer zoom and an 82-color cursor. This subsystem is implemented on an add-in board occupying a single slot in the NeXT computer. Up to three boards may be added to the NeXT for multiple display capability (e.g., three 1280 x 1024 monitors, each with a 16-Mbyte frame buffer). Each add-in board provides an expansion connector to which an optional image computing coprocessor board may be added. Each coprocessor board supports up to four processors for a peak performance of 160 MFLOPS. The coprocessors can execute programs from external high-speed microcode memory as well as built-in internal microcode routines. The internal microcode routines provide support for 2-D and 3-D graphics operations, matrix and vector arithmetic, and image processing in integer, IEEE single-precision floating point, or IEEE double-precision floating point. In addition to providing a library of C functions which links the NeXT computer to the add-in board and supports its various operational modes, algorithms and medical imaging application programs are being developed and implemented for image display and enhancement. As an extension to the built-in algorithms of the coprocessors, 2-D Fast Fourier Transform (FF1), 2-D Inverse FFF, convolution, warping and other algorithms (e.g., Discrete Cosine Transform) which exploit the parallel architecture of the coprocessor board are being implemented.

X-ray Computed Tomography.

ERIC Educational Resources Information Center

Michael, Greg

2001-01-01

Describes computed tomography (CT), a medical imaging technique that produces images of transaxial planes through the human body. A CT image is reconstructed mathematically from a large number of one-dimensional projections of a plane. The technique is used in radiological examinations and radiotherapy treatment planning. (Author/MM)

Machine Learning in Medical Imaging.

PubMed

Giger, Maryellen L

2018-03-01

Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine. Copyright © 2018. Published by Elsevier Inc.

Design and Configuration of a Medical Imaging Systems Computer Laboratory Syllabus

ERIC Educational Resources Information Center

Selver, M. Alper

2016-01-01

Medical imaging systems (MIS) constitute an important emergent subdiscipline of engineering studies. In the context of electrical and electronics engineering (EEE) education, MIS courses cover physics, instrumentation, data acquisition, image formation, modeling, and quality assessment of various modalities. Many well-structured MIS courses are…

A service protocol for post-processing of medical images on the mobile device

NASA Astrophysics Data System (ADS)

He, Longjun; Ming, Xing; Xu, Lang; Liu, Qian

2014-03-01

With computing capability and display size growing, the mobile device has been used as a tool to help clinicians view patient information and medical images anywhere and anytime. It is uneasy and time-consuming for transferring medical images with large data size from picture archiving and communication system to mobile client, since the wireless network is unstable and limited by bandwidth. Besides, limited by computing capability, memory and power endurance, it is hard to provide a satisfactory quality of experience for radiologists to handle some complex post-processing of medical images on the mobile device, such as real-time direct interactive three-dimensional visualization. In this work, remote rendering technology is employed to implement the post-processing of medical images instead of local rendering, and a service protocol is developed to standardize the communication between the render server and mobile client. In order to make mobile devices with different platforms be able to access post-processing of medical images, the Extensible Markup Language is taken to describe this protocol, which contains four main parts: user authentication, medical image query/ retrieval, 2D post-processing (e.g. window leveling, pixel values obtained) and 3D post-processing (e.g. maximum intensity projection, multi-planar reconstruction, curved planar reformation and direct volume rendering). And then an instance is implemented to verify the protocol. This instance can support the mobile device access post-processing of medical image services on the render server via a client application or on the web page.

OpenID Connect as a security service in cloud-based medical imaging systems.

PubMed

Ma, Weina; Sartipi, Kamran; Sharghigoorabi, Hassan; Koff, David; Bak, Peter

2016-04-01

The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as "Kerberos of cloud." We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model.

Evaluation of image compression for computer-aided diagnosis of breast tumors in 3D sonography

NASA Astrophysics Data System (ADS)

Chen, We-Min; Huang, Yu-Len; Tao, Chi-Chuan; Chen, Dar-Ren; Moon, Woo-Kyung

2006-03-01

Medical imaging examinations form the basis for physicians diagnosing diseases, as evidenced by the increasing use of digital medical images for picture archiving and communications systems (PACS). However, with enlarged medical image databases and rapid growth of patients' case reports, PACS requires image compression to accelerate the image transmission rate and conserve disk space for diminishing implementation costs. For this purpose, JPEG and JPEG2000 have been accepted as legal formats for the digital imaging and communications in medicine (DICOM). The high compression ratio is felt to be useful for medical imagery. Therefore, this study evaluates the compression ratios of JPEG and JPEG2000 standards for computer-aided diagnosis (CAD) of breast tumors in 3-D medical ultrasound (US) images. The 3-D US data sets with various compression ratios are compressed using the two efficacious image compression standards. The reconstructed data sets are then diagnosed by a previous proposed CAD system. The diagnostic accuracy is measured based on receiver operating characteristic (ROC) analysis. Namely, the ROC curves are used to compare the diagnostic performance of two or more reconstructed images. Analysis results ensure a comparison of the compression ratios by using JPEG and JPEG2000 for 3-D US images. Results of this study provide the possible bit rates using JPEG and JPEG2000 for 3-D breast US images.

The virtual mirror: a new interaction paradigm for augmented reality environments.

PubMed

Bichlmeier, Christoph; Heining, Sandro Michael; Feuerstein, Marco; Navab, Nassir

2009-09-01

Medical augmented reality (AR) has been widely discussed within the medical imaging as well as computer aided surgery communities. Different systems for exemplary medical applications have been proposed. Some of them produced promising results. One major issue still hindering AR technology to be regularly used in medical applications is the interaction between physician and the superimposed 3-D virtual data. Classical interaction paradigms, for instance with keyboard and mouse, to interact with visualized medical 3-D imaging data are not adequate for an AR environment. This paper introduces the concept of a tangible/controllable Virtual Mirror for medical AR applications. This concept intuitively augments the direct view of the surgeon with all desired views on volumetric medical imaging data registered with the operation site without moving around the operating table or displacing the patient. We selected two medical procedures to demonstrate and evaluate the potentials of the Virtual Mirror for the surgical workflow. Results confirm the intuitiveness of this new paradigm and its perceptive advantages for AR-based computer aided interventions.

A framework for interactive visualization of digital medical images.

PubMed

Koehring, Andrew; Foo, Jung Leng; Miyano, Go; Lobe, Thom; Winer, Eliot

2008-10-01

The visualization of medical images obtained from scanning techniques such as computed tomography and magnetic resonance imaging is a well-researched field. However, advanced tools and methods to manipulate these data for surgical planning and other tasks have not seen widespread use among medical professionals. Radiologists have begun using more advanced visualization packages on desktop computer systems, but most physicians continue to work with basic two-dimensional grayscale images or not work directly with the data at all. In addition, new display technologies that are in use in other fields have yet to be fully applied in medicine. It is our estimation that usability is the key aspect in keeping this new technology from being more widely used by the medical community at large. Therefore, we have a software and hardware framework that not only make use of advanced visualization techniques, but also feature powerful, yet simple-to-use, interfaces. A virtual reality system was created to display volume-rendered medical models in three dimensions. It was designed to run in many configurations, from a large cluster of machines powering a multiwalled display down to a single desktop computer. An augmented reality system was also created for, literally, hands-on interaction when viewing models of medical data. Last, a desktop application was designed to provide a simple visualization tool, which can be run on nearly any computer at a user's disposal. This research is directed toward improving the capabilities of medical professionals in the tasks of preoperative planning, surgical training, diagnostic assistance, and patient education.

Development of an assisting detection system for early infarct diagnosis

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

Sim, K. S.; Nia, M. E.; Ee, C. S.

2015-04-24

In this paper, a detection assisting system for early infarct detection is developed. This new developed method is used to assist the medical practitioners to diagnose infarct from computed tomography images of brain. Using this assisting system, the infarct could be diagnosed at earlier stages. The non-contrast computed tomography (NCCT) brain images are the data set used for this system. Detection module extracts the pixel data from NCCT brain images, and produces the colourized version of images. The proposed method showed great potential in detecting infarct, and helps medical practitioners to make earlier and better diagnoses.

Image Engine: an object-oriented multimedia database for storing, retrieving and sharing medical images and text.

PubMed Central

Lowe, H. J.

1993-01-01

This paper describes Image Engine, an object-oriented, microcomputer-based, multimedia database designed to facilitate the storage and retrieval of digitized biomedical still images, video, and text using inexpensive desktop computers. The current prototype runs on Apple Macintosh computers and allows network database access via peer to peer file sharing protocols. Image Engine supports both free text and controlled vocabulary indexing of multimedia objects. The latter is implemented using the TView thesaurus model developed by the author. The current prototype of Image Engine uses the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary (with UMLS Meta-1 extensions) as its indexing thesaurus. PMID:8130596

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

PubMed

Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

2017-11-03

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

Stereoscopic medical imaging collaboration system

NASA Astrophysics Data System (ADS)

Okuyama, Fumio; Hirano, Takenori; Nakabayasi, Yuusuke; Minoura, Hirohito; Tsuruoka, Shinji

2007-02-01

The computerization of the clinical record and the realization of the multimedia have brought improvement of the medical service in medical facilities. It is very important for the patients to obtain comprehensible informed consent. Therefore, the doctor should plainly explain the purpose and the content of the diagnoses and treatments for the patient. We propose and design a Telemedicine Imaging Collaboration System which presents a three dimensional medical image as X-ray CT, MRI with stereoscopic image by using virtual common information space and operating the image from a remote location. This system is composed of two personal computers, two 15 inches stereoscopic parallax barrier type LCD display (LL-151D, Sharp), one 1Gbps router and 1000base LAN cables. The software is composed of a DICOM format data transfer program, an operation program of the images, the communication program between two personal computers and a real time rendering program. Two identical images of 512×768 pixcels are displayed on two stereoscopic LCD display, and both images show an expansion, reduction by mouse operation. This system can offer a comprehensible three-dimensional image of the diseased part. Therefore, the doctor and the patient can easily understand it, depending on their needs.

Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

Identifying regions of interest in medical images using self-organizing maps.

PubMed

Teng, Wei-Guang; Chang, Ping-Lin

2012-10-01

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

High performance computing for deformable image registration: towards a new paradigm in adaptive radiotherapy.

PubMed

Samant, Sanjiv S; Xia, Junyi; Muyan-Ozcelik, Pinar; Owens, John D

2008-08-01

The advent of readily available temporal imaging or time series volumetric (4D) imaging has become an indispensable component of treatment planning and adaptive radiotherapy (ART) at many radiotherapy centers. Deformable image registration (DIR) is also used in other areas of medical imaging, including motion corrected image reconstruction. Due to long computation time, clinical applications of DIR in radiation therapy and elsewhere have been limited and consequently relegated to offline analysis. With the recent advances in hardware and software, graphics processing unit (GPU) based computing is an emerging technology for general purpose computation, including DIR, and is suitable for highly parallelized computing. However, traditional general purpose computation on the GPU is limited because the constraints of the available programming platforms. As well, compared to CPU programming, the GPU currently has reduced dedicated processor memory, which can limit the useful working data set for parallelized processing. We present an implementation of the demons algorithm using the NVIDIA 8800 GTX GPU and the new CUDA programming language. The GPU performance will be compared with single threading and multithreading CPU implementations on an Intel dual core 2.4 GHz CPU using the C programming language. CUDA provides a C-like language programming interface, and allows for direct access to the highly parallel compute units in the GPU. Comparisons for volumetric clinical lung images acquired using 4DCT were carried out. Computation time for 100 iterations in the range of 1.8-13.5 s was observed for the GPU with image size ranging from 2.0 x 10(6) to 14.2 x 10(6) pixels. The GPU registration was 55-61 times faster than the CPU for the single threading implementation, and 34-39 times faster for the multithreading implementation. For CPU based computing, the computational time generally has a linear dependence on image size for medical imaging data. Computational efficiency is characterized in terms of time per megapixels per iteration (TPMI) with units of seconds per megapixels per iteration (or spmi). For the demons algorithm, our CPU implementation yielded largely invariant values of TPMI. The mean TPMIs were 0.527 spmi and 0.335 spmi for the single threading and multithreading cases, respectively, with <2% variation over the considered image data range. For GPU computing, we achieved TPMI =0.00916 spmi with 3.7% variation, indicating optimized memory handling under CUDA. The paradigm of GPU based real-time DIR opens up a host of clinical applications for medical imaging.

[The use of computer technology and digital photography in the work of a forensic medical expertise bureau].

PubMed

Novoselov, V P; Fedorov, S A

1999-01-01

UNISCAN scanner with PC was used at department of medical criminology and at the histological department of the Novosibirsk Regional Bureau of Forensic Medical Expert Evaluations. The quality of images obtained by computers and digital photography is not inferior to that of traditional photographs.

[Computational medical imaging (radiomics) and potential for immuno-oncology].

PubMed

Sun, R; Limkin, E J; Dercle, L; Reuzé, S; Zacharaki, E I; Chargari, C; Schernberg, A; Dirand, A S; Alexis, A; Paragios, N; Deutsch, É; Ferté, C; Robert, C

2017-10-01

The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes. This approach has the advantages of being non-invasive, capable of evaluating the tumour and its microenvironment in their entirety, thus characterising spatial heterogeneity, and being easily repeatable over time. The end goal of radiomics is to determine imaging biomarkers as decision support tools for clinical practice and to facilitate better understanding of cancer biology, allowing the assessment of the changes throughout the evolution of the disease and the therapeutic sequence. This review will develop the process of computational imaging analysis and present its potential in immuno-oncology. Copyright © 2017 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

An atlas of the (near) future: cognitive computing applications for medical imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

LeGrand, Anne

2017-02-01

The role of medical imaging in global health systems is literally fundamental. Like labs, medical images are used at one point or another in almost every high cost, high value episode of care. CT scans, mammograms, and x-rays, for example, "atlas" the body and help chart a course forward for a patient's care team. Imaging precision has improved as a result of technological advancements and breakthroughs in related medical research. Those advancements also bring with them exponential growth in medical imaging data. As IBM trains Watson to "see" medical images, Ms. Le Grand will discuss recent advances made by Watson Health and explore the potential value of "augmented intelligence" to assist healthcare providers like radiologists and cardiologists, as well as the patients they serve.

An intelligent framework for medical image retrieval using MDCT and multi SVM.

PubMed

Balan, J A Alex Rajju; Rajan, S Edward

2014-01-01

Volumes of medical images are rapidly generated in medical field and to manage them effectively has become a great challenge. This paper studies the development of innovative medical image retrieval based on texture features and accuracy. The objective of the paper is to analyze the image retrieval based on diagnosis of healthcare management systems. This paper traces the development of innovative medical image retrieval to estimate both the image texture features and accuracy. The texture features of medical images are extracted using MDCT and multi SVM. Both the theoretical approach and the simulation results revealed interesting observations and they were corroborated using MDCT coefficients and SVM methodology. All attempts to extract the data about the image in response to the query has been computed successfully and perfect image retrieval performance has been obtained. Experimental results on a database of 100 trademark medical images show that an integrated texture feature representation results in 98% of the images being retrieved using MDCT and multi SVM. Thus we have studied a multiclassification technique based on SVM which is prior suitable for medical images. The results show the retrieval accuracy of 98%, 99% for different sets of medical images with respect to the class of image.

Image-based teleconsultation using smartphones or tablets: qualitative assessment of medical experts.

PubMed

Boissin, Constance; Blom, Lisa; Wallis, Lee; Laflamme, Lucie

2017-02-01

Mobile health has promising potential in improving healthcare delivery by facilitating access to expert advice. Enabling experts to review images on their smartphone or tablet may save valuable time. This study aims at assessing whether images viewed by medical specialists on handheld devices such as smartphones and tablets are perceived to be of comparable quality as when viewed on a computer screen. This was a prospective study comparing the perceived quality of 18 images on three different display devices (smartphone, tablet and computer) by 27 participants (4 burn surgeons and 23 emergency medicine specialists). The images, presented in random order, covered clinical (dermatological conditions, burns, ECGs and X-rays) and non-clinical subjects and their perceived quality was assessed using a 7-point Likert scale. Differences in devices' quality ratings were analysed using linear regression models for clustered data adjusting for image type and participants' characteristics (age, gender and medical specialty). Overall, the images were rated good or very good in most instances and more so for the smartphone (83.1%, mean score 5.7) and tablet (78.2%, mean 5.5) than for a standard computer (70.6%, mean 5.2). Both handheld devices had significantly higher ratings than the computer screen, even after controlling for image type and participants' characteristics. Nearly all experts expressed that they would be comfortable using smartphones (n=25) or tablets (n=26) for image-based teleconsultation. This study suggests that handheld devices could be a substitute for computer screens for teleconsultation by physicians working in emergency settings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Initial experience with a handheld device digital imaging and communications in medicine viewer: OsiriX mobile on the iPhone.

PubMed

Choudhri, Asim F; Radvany, Martin G

2011-04-01

Medical imaging is commonly used to diagnose many emergent conditions, as well as plan treatment. Digital images can be reviewed on almost any computing platform. Modern mobile phones and handheld devices are portable computing platforms with robust software programming interfaces, powerful processors, and high-resolution displays. OsiriX mobile, a new Digital Imaging and Communications in Medicine viewing program, is available for the iPhone/iPod touch platform. This raises the possibility of mobile review of diagnostic medical images to expedite diagnosis and treatment planning using a commercial off the shelf solution, facilitating communication among radiologists and referring clinicians.

The contribution of Medical Physics to Nuclear Medicine: looking back - a physicist's perspective.

PubMed

Hutton, Brian F

2014-12-01

This paper is the first in a series of invited perspectives by four pioneers of Nuclear Medicine imaging and physics. A medical physicist and a Nuclear Medicine clinical specialist each take a backward look and a forward look at the contributions of Medical Physics to Nuclear Medicine. Contributions of Medical Physics are presented from the early discovery of radioactivity, development of first imaging devices, computers and emission tomography to recent development of hybrid imaging. There is evidence of significant contribution of Medical Physics throughout the development of Nuclear Medicine.

WE-D-303-01: Development and Application of Digital Human Phantoms

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

Segars, P.

2015-06-15

Modern medical physics deals with complex problems such as 4D radiation therapy and imaging quality optimization. Such problems involve a large number of radiological parameters, and anatomical and physiological breathing patterns. A major challenge is how to develop, test, evaluate and compare various new imaging and treatment techniques, which often involves testing over a large range of radiological parameters as well as varying patient anatomies and motions. It would be extremely challenging, if not impossible, both ethically and practically, to test every combination of parameters and every task on every type of patient under clinical conditions. Computer-based simulation using computationalmore » phantoms offers a practical technique with which to evaluate, optimize, and compare imaging technologies and methods. Within simulation, the computerized phantom provides a virtual model of the patient’s anatomy and physiology. Imaging data can be generated from it as if it was a live patient using accurate models of the physics of the imaging and treatment process. With sophisticated simulation algorithms, it is possible to perform virtual experiments entirely on the computer. By serving as virtual patients, computational phantoms hold great promise in solving some of the most complex problems in modern medical physics. In this proposed symposium, we will present the history and recent developments of computational phantom models, share experiences in their application to advanced imaging and radiation applications, and discuss their promises and limitations. Learning Objectives: Understand the need and requirements of computational phantoms in medical physics research Discuss the developments and applications of computational phantoms Know the promises and limitations of computational phantoms in solving complex problems.« less

Review methods for image segmentation from computed tomography images

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

Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik

Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affectmore » the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.« less

Adaptive tight frame based medical image reconstruction: a proof-of-concept study for computed tomography

NASA Astrophysics Data System (ADS)

Zhou, Weifeng; Cai, Jian-Feng; Gao, Hao

2013-12-01

A popular approach for medical image reconstruction has been through the sparsity regularization, assuming the targeted image can be well approximated by sparse coefficients under some properly designed system. The wavelet tight frame is such a widely used system due to its capability for sparsely approximating piecewise-smooth functions, such as medical images. However, using a fixed system may not always be optimal for reconstructing a variety of diversified images. Recently, the method based on the adaptive over-complete dictionary that is specific to structures of the targeted images has demonstrated its superiority for image processing. This work is to develop the adaptive wavelet tight frame method image reconstruction. The proposed scheme first constructs the adaptive wavelet tight frame that is task specific, and then reconstructs the image of interest by solving an l1-regularized minimization problem using the constructed adaptive tight frame system. The proof-of-concept study is performed for computed tomography (CT), and the simulation results suggest that the adaptive tight frame method improves the reconstructed CT image quality from the traditional tight frame method.

Bridging the Gap between Basic and Clinical Sciences: A Description of a Radiological Anatomy Course

ERIC Educational Resources Information Center

Torres, Anna; Staskiewicz, Grzegorz J.; Lisiecka, Justyna; Pietrzyk, Lukasz; Czekajlo, Michael; Arancibia, Carlos U.; Maciejewski, Ryszard; Torres, Kamil

2016-01-01

A wide variety of medical imaging techniques pervade modern medicine, and the changing portability and performance of tools like ultrasound imaging have brought these medical imaging techniques into the everyday practice of many specialties outside of radiology. However, proper interpretation of ultrasonographic and computed tomographic images…

Image analysis in modern ophthalmology: from acquisition to computer assisted diagnosis and telemedicine

NASA Astrophysics Data System (ADS)

Marrugo, Andrés G.; Millán, María S.; Cristóbal, Gabriel; Gabarda, Salvador; Sorel, Michal; Sroubek, Filip

2012-06-01

Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract information about many diseases. Modern ophthalmology thrives and develops on the advances in digital imaging and computing power. In this work we present an overview of recent image processing techniques proposed by the authors in the area of digital eye fundus photography. Our applications range from retinal image quality assessment to image restoration via blind deconvolution and visualization of structural changes in time between patient visits. All proposed within a framework for improving and assisting the medical practice and the forthcoming scenario of the information chain in telemedicine.

Deep Learning in Medical Imaging: General Overview

PubMed Central

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae

2017-01-01

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. PMID:28670152

Deep Learning in Medical Imaging: General Overview.

PubMed

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

2017-01-01

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

CUDA-based acceleration of collateral filtering in brain MR images

NASA Astrophysics Data System (ADS)

Li, Cheng-Yuan; Chang, Herng-Hua

2017-02-01

Image denoising is one of the fundamental and essential tasks within image processing. In medical imaging, finding an effective algorithm that can remove random noise in MR images is important. This paper proposes an effective noise reduction method for brain magnetic resonance (MR) images. Our approach is based on the collateral filter which is a more powerful method than the bilateral filter in many cases. However, the computation of the collateral filter algorithm is quite time-consuming. To solve this problem, we improved the collateral filter algorithm with parallel computing using GPU. We adopted CUDA, an application programming interface for GPU by NVIDIA, to accelerate the computation. Our experimental evaluation on an Intel Xeon CPU E5-2620 v3 2.40GHz with a NVIDIA Tesla K40c GPU indicated that the proposed implementation runs dramatically faster than the traditional collateral filter. We believe that the proposed framework has established a general blueprint for achieving fast and robust filtering in a wide variety of medical image denoising applications.

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

2006-03-01

Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures.

PubMed

Colen, Rivka; Foster, Ian; Gatenby, Robert; Giger, Mary Ellen; Gillies, Robert; Gutman, David; Heller, Matthew; Jain, Rajan; Madabhushi, Anant; Madhavan, Subha; Napel, Sandy; Rao, Arvind; Saltz, Joel; Tatum, James; Verhaak, Roeland; Whitman, Gary

2014-10-01

The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

Sharp-Focus Composite Microscope Imaging by Computer

NASA Technical Reports Server (NTRS)

Wall, R. J.

1983-01-01

Enhanced depth of focus aids medical analysis. Computer image-processing system synthesizes sharply-focused composite picture from series of photomicrographs of same object taken at different depths. Computer rejects blured parts of each photomicrograph. Remaining in focus portions form focused composite. System used to study alveolar lung tissue and has applications in medicine and physical sciences.

A Method for Identifying Contours in Processing Digital Images from Computer Tomograph

NASA Astrophysics Data System (ADS)

Roşu, Şerban; Pater, Flavius; Costea, Dan; Munteanu, Mihnea; Roşu, Doina; Fratila, Mihaela

2011-09-01

The first step in digital processing of two-dimensional computed tomography images is to identify the contour of component elements. This paper deals with the collective work of specialists in medicine and applied mathematics in computer science on elaborating new algorithms and methods in medical 2D and 3D imagery.

OpenID Connect as a security service in cloud-based medical imaging systems

PubMed Central

Ma, Weina; Sartipi, Kamran; Sharghigoorabi, Hassan; Koff, David; Bak, Peter

2016-01-01

Abstract. The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as “Kerberos of cloud.” We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model. PMID:27340682

[Design of visualized medical images network and web platform based on MeVisLab].

PubMed

Xiang, Jun; Ye, Qing; Yuan, Xun

2017-04-01

With the trend of the development of "Internet +", some further requirements for the mobility of medical images have been required in the medical field. In view of this demand, this paper presents a web-based visual medical imaging platform. First, the feasibility of medical imaging is analyzed and technical points. CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) images are reconstructed three-dimensionally by MeVisLab and packaged as X3D (Extensible 3D Graphics) files shown in the present paper. Then, the B/S (Browser/Server) system specially designed for 3D image is designed by using the HTML 5 and WebGL rendering engine library, and the X3D image file is parsed and rendered by the system. The results of this study showed that the platform was suitable for multiple operating systems to realize the platform-crossing and mobilization of medical image data. The development of medical imaging platform is also pointed out in this paper. It notes that web application technology will not only promote the sharing of medical image data, but also facilitate image-based medical remote consultations and distance learning.

Incidental findings in imaging research: evaluating incidence, benefit, and burden.

PubMed

Orme, Nicholas M; Fletcher, Joel G; Siddiki, Hassan A; Harmsen, W Scott; O'Byrne, Megan M; Port, John D; Tremaine, William J; Pitot, Henry C; McFarland, Elizabeth G; Robinson, Marguerite E; Koenig, Barbara A; King, Bernard F; Wolf, Susan M

2010-09-27

Little information exists concerning the frequency and medical significance of incidental findings (IFs) in imaging research. Medical records of research participants undergoing a research imaging examination interpreted by a radiologist during January through March 2004 were reviewed, with 3-year clinical follow-up. An expert panel reviewed all IFs generating clinical action to determine medical benefit/burden on the basis of predefined criteria. The frequency of IFs that generated further clinical action was estimated by modality, body part, age, and sex, along with net medical benefit or burden. Of 1426 research imaging examinations, 567 (39.8%) had at least 1 IF (1055 total). Risk of an IF increased significantly by age (odds ratio [OR], 1.5; 95% confidence interval, 1.4-1.7 per decade increase). Abdominopelvic computed tomography generated more IFs than other examinations (OR, 18.9 vs ultrasonography; 9.2% with subsequent clinical action), with computed tomography of the thorax and magnetic resonance imaging of the head next (OR, 11.9 and 5.9; 2.8% and 2.2% with action, respectively). Of the 567 examinations with an IF, 35 (6.2%) generated clinical action, resulting in clear medical benefit in 1.1% (6 of 567) and clear medical burden in 0.5% (3 of 567). Medical benefit/burden was usually unclear (26 of 567 [4.6%]). Frequency of IFs in imaging research examinations varies significantly by imaging modality, body region, and age. Research imaging studies at high risk for generating IFs can be identified. Routine evaluation of research images by radiologists may result in identification of IFs in a high number of cases and subsequent clinical action to address them in a small but significant minority. Such clinical action can result in medical benefit to a small number of patients.

Shaping the future through innovations: From medical imaging to precision medicine.

PubMed

Comaniciu, Dorin; Engel, Klaus; Georgescu, Bogdan; Mansi, Tommaso

2016-10-01

Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up. In addition, due to its patient-specific nature, imaging information represents a critical component required for advancing precision medicine into clinical practice. This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power. Throughout the manuscript we will analyze the capabilities of such technologies and extrapolate on their potential impact to advance the quality of medical care, while reducing its cost. Copyright © 2016 Elsevier B.V. All rights reserved.

Large-scale retrieval for medical image analytics: A comprehensive review.

PubMed

Li, Zhongyu; Zhang, Xiaofan; Müller, Henning; Zhang, Shaoting

2018-01-01

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration

PubMed Central

Kim, Hyunjoo; Parashar, Manish; Foran, David J.; Yang, Lin

2010-01-01

This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application. PMID:20640235

Processing And Display Of Medical Three Dimensional Arrays Of Numerical Data Using Octree Encoding

NASA Astrophysics Data System (ADS)

Amans, Jean-Louis; Darier, Pierre

1986-05-01

imaging modalities such as X-Ray computerized Tomography (CT), Nuclear Medecine and Nuclear Magnetic Resonance can produce three-dimensional (3-D) arrays of numerical data of medical object internal structures. The analysis of 3-D data by synthetic generation of realistic images is an important area of computer graphics and imaging.

Radiomic analysis in prediction of Human Papilloma Virus status.

PubMed

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

Picture archiving and communication system--Part one: Filmless radiology and distance radiology.

PubMed

De Backer, A I; Mortelé, K J; De Keulenaer, B L

2004-01-01

Picture archiving and communication system (PACS) is a collection of technologies used to carry out digital medical imaging. PACS is used to digitally acquire medical images from the various modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and digital projection radiography. The image data and pertinent information are transmitted to other and possibly remote locations over networks, where they may be displayed on computer workstations for soft copy viewing in multiple locations, thus permitting simultaneous consultations and almost instant reporting from radiologists at a distance. Data are secured and archived on digital media such as optical disks or tape, and may be automatically retrieved as necessary. Close integration with the hospital information system (HIS)--radiology information system (RIS) is critical for system functionality. Medical image management systems are maturing, providing access outside of the radiology department to images throughout the hospital via the Ethernet, at different hospitals, or from a home workstation if teleradiology has been implemented.

Safety Precautions and Operating Procedures in an (A)BSL-4 Laboratory: 4. Medical Imaging Procedures.

PubMed

Byrum, Russell; Keith, Lauren; Bartos, Christopher; St Claire, Marisa; Lackemeyer, Matthew G; Holbrook, Michael R; Janosko, Krisztina; Barr, Jason; Pusl, Daniela; Bollinger, Laura; Wada, Jiro; Coe, Linda; Hensley, Lisa E; Jahrling, Peter B; Kuhn, Jens H; Lentz, Margaret R

2016-10-03

Medical imaging using animal models for human diseases has been utilized for decades; however, until recently, medical imaging of diseases induced by high-consequence pathogens has not been possible. In 2014, the National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick opened an Animal Biosafety Level 4 (ABSL-4) facility to assess the clinical course and pathology of infectious diseases in experimentally infected animals. Multiple imaging modalities including computed tomography (CT), magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are available to researchers for these evaluations. The focus of this article is to describe the workflow for safely obtaining a CT image of a live guinea pig in an ABSL-4 facility. These procedures include animal handling, anesthesia, and preparing and monitoring the animal until recovery from sedation. We will also discuss preparing the imaging equipment, performing quality checks, communication methods from "hot side" (containing pathogens) to "cold side," and moving the animal from the holding room to the imaging suite.

Medical image registration: basic science and clinical implications.

PubMed

Imran, Muhammad Babar; Meo, Sultan Ayoub; Yousuf, Mohammad; Othman, Saleh; Shahid, Abubakar

2010-01-01

Image Registration is a process of aligning two or more images so that corresponding feature can be related objectively. Integration of corresponding and complementary information from various images has become an important area of computation in medical imaging. Merging different images of the same patient taken by different modalities or acquired at different times is quite useful in interpreting lower resolution functional images, such as those provided by nuclear medicine, in determining spatial relationships of structures seen in different modalities. This will help in planning surgery and longitudinal follow up. The aim of this article was to introduce image registration to all those who are working in field of medical sciences in general and medical doctors in particular; and indicate how and where this specialty is moving to provide better health care services.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

PubMed

Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

2016-05-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

Medical imaging: examples of clinical applications

NASA Astrophysics Data System (ADS)

Meinzer, H. P.; Thorn, M.; Vetter, M.; Hassenpflug, P.; Hastenteufel, M.; Wolf, I.

Clinical routine is currently producing a multitude of diagnostic digital images but only a few are used in therapy planning and treatment. Medical imaging is involved in both diagnosis and therapy. Using a computer, existing 2D images can be transformed into interactive 3D volumes and results from different modalities can be merged. Furthermore, it is possible to calculate functional areas that were not visible in the primary images. This paper presents examples of clinical applications that are integrated into clinical routine and are based on medical imaging fundamentals. In liver surgery, the importance of virtual planning is increasing because surgery is still the only possible curative procedure. Visualisation and analysis of heart defects are also gaining in significance due to improved surgery techniques. Finally, an outlook is provided on future developments in medical imaging using navigation to support the surgeon's work. The paper intends to give an impression of the wide range of medical imaging that goes beyond the mere calculation of medical images.

[Research on fast implementation method of image Gaussian RBF interpolation based on CUDA].

PubMed

Chen, Hao; Yu, Haizhong

2014-04-01

Image interpolation is often required during medical image processing and analysis. Although interpolation method based on Gaussian radial basis function (GRBF) has high precision, the long calculation time still limits its application in field of image interpolation. To overcome this problem, a method of two-dimensional and three-dimensional medical image GRBF interpolation based on computing unified device architecture (CUDA) is proposed in this paper. According to single instruction multiple threads (SIMT) executive model of CUDA, various optimizing measures such as coalesced access and shared memory are adopted in this study. To eliminate the edge distortion of image interpolation, natural suture algorithm is utilized in overlapping regions while adopting data space strategy of separating 2D images into blocks or dividing 3D images into sub-volumes. Keeping a high interpolation precision, the 2D and 3D medical image GRBF interpolation achieved great acceleration in each basic computing step. The experiments showed that the operative efficiency of image GRBF interpolation based on CUDA platform was obviously improved compared with CPU calculation. The present method is of a considerable reference value in the application field of image interpolation.

Comprehensive Digital Imaging Network Project At Georgetown University Hospital

NASA Astrophysics Data System (ADS)

Mun, Seong K.; Stauffer, Douglas; Zeman, Robert; Benson, Harold; Wang, Paul; Allman, Robert

1987-10-01

The radiology practice is going through rapid changes due to the introduction of state-of-the-art computed based technologies. For the last twenty years we have witnessed the introduction of many new medical diagnostic imaging systems such as x-ray computed tomo-graphy, digital subtraction angiography (DSA), computerized nuclear medicine, single pho-ton emission computed tomography (SPECT), positron emission tomography (PET) and more re-cently, computerized digital radiography and nuclear magnetic resonance imaging (MRI). Other than the imaging systems, there has been a steady introduction of computed based information systems for radiology departments and hospitals.

Computer assisted analysis of medical x-ray images

NASA Astrophysics Data System (ADS)

Bengtsson, Ewert

1996-01-01

X-rays were originally used to expose film. The early computers did not have enough capacity to handle images with useful resolution. The rapid development of computer technology over the last few decades has, however, led to the introduction of computers into radiology. In this overview paper, the various possible roles of computers in radiology are examined. The state of the art is briefly presented, and some predictions about the future are made.

Space Technology - Game Changing Development NASA Facts: Autonomous Medical Operations

NASA Technical Reports Server (NTRS)

Thompson, David E.

2018-01-01

The AMO (Autonomous Medical Operations) Project is working extensively to train medical models on the reliability and confidence of computer-aided interpretation of ultrasound images in various clinical settings, and of various anatomical structures. AI (Artificial Intelligence) algorithms recognize and classify features in the ultrasound images, and these are compared to those features that clinicians use to diagnose diseases. The acquisition of clinically validated image assessment and the use of the AI algorithms constitutes fundamental baseline for a Medical Decision Support System that will advise crew on long-duration, remote missions.

DICOM image integration into an electronic medical record using thin viewing clients

NASA Astrophysics Data System (ADS)

Stewart, Brent K.; Langer, Steven G.; Taira, Ricky K.

1998-07-01

Purpose -- To integrate radiological DICOM images into our currently existing web-browsable Electronic Medical Record (MINDscape). Over the last five years the University of Washington has created a clinical data repository combining in a distributed relational database information from multiple departmental databases (MIND). A text-based view of this data called the Mini Medical Record (MMR) has been available for three years. MINDscape, unlike the text based MMR, provides a platform independent, web browser view of the MIND dataset that can easily be linked to other information resources on the network. We have now added the integration of radiological images into MINDscape through a DICOM webserver. Methods/New Work -- we have integrated a commercial webserver that acts as a DICOM Storage Class Provider to our, computed radiography (CR), computed tomography (CT), digital fluoroscopy (DF), magnetic resonance (MR) and ultrasound (US) scanning devices. These images can be accessed through CGI queries or by linking the image server database using ODBC or SQL gateways. This allows the use of dynamic HTML links to the images on the DICOM webserver from MINDscape, so that the radiology reports already resident in the MIND repository can be married with the associated images through the unique examination accession number generated by our Radiology Information System (RIS). The web browser plug-in used provides a wavelet decompression engine (up to 16-bits per pixel) and performs the following image manipulation functions: window/level, flip, invert, sort, rotate, zoom, cine-loop and save as JPEG. Results -- Radiological DICOM image sets (CR, CT, MR and US) are displayed with associated exam reports for referring physician and clinicians anywhere within the widespread academic medical center on PCs, Macs, X-terminals and Unix computers. This system is also being used for home teleradiology application. Conclusion -- Radiological DICOM images can be made available medical center wide to physicians quickly using low-cost and ubiquitous, thin client browsing technology and wavelet compression.

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine.

PubMed

Bao, Shunxing; Weitendorf, Frederick D; Plassard, Andrew J; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A

2017-02-11

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., "short" processing times and/or "large" datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply "large scale" processing transitions into "big data" and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and non-relevant for medical imaging.

Theoretical and empirical comparison of big data image processing with Apache Hadoop and Sun Grid Engine

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Weitendorf, Frederick D.; Plassard, Andrew J.; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A.

2017-03-01

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., "short" processing times and/or "large" datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply "large scale" processing transitions into "big data" and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and nonrelevant for medical imaging.

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

JP3D compressed-domain watermarking of volumetric medical data sets

NASA Astrophysics Data System (ADS)

Ouled Zaid, Azza; Makhloufi, Achraf; Olivier, Christian

2010-01-01

Increasing transmission of medical data across multiple user systems raises concerns for medical image watermarking. Additionaly, the use of volumetric images triggers the need for efficient compression techniques in picture archiving and communication systems (PACS), or telemedicine applications. This paper describes an hybrid data hiding/compression system, adapted to volumetric medical imaging. The central contribution is to integrate blind watermarking, based on turbo trellis-coded quantization (TCQ), to JP3D encoder. Results of our method applied to Magnetic Resonance (MR) and Computed Tomography (CT) medical images have shown that our watermarking scheme is robust to JP3D compression attacks and can provide relative high data embedding rate whereas keep a relative lower distortion.

Computed Tomography (CT) Imaging of Injuries from Blunt Abdominal Trauma: A Pictorial Essay.

PubMed

Hassan, Radhiana; Abd Aziz, Azian

2010-04-01

Blunt abdominal trauma can cause multiple internal injuries. However, these injuries are often difficult to accurately evaluate, particularly in the presence of more obvious external injuries. Computed tomography (CT) imaging is currently used to assess clinically stable patients with blunt abdominal trauma. CT can provide a rapid and accurate appraisal of the abdominal viscera, retroperitoneum and abdominal wall, as well as a limited assessment of the lower thoracic region and bony pelvis. This paper presents examples of various injuries in trauma patients depicted in abdominal CT images. We hope these images provide a resource for radiologists, surgeons and medical officers, as well as a learning tool for medical students.

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

PubMed

Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

2015-01-01

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

Iterative Minimum Variance Beamformer with Low Complexity for Medical Ultrasound Imaging.

PubMed

Deylami, Ali Mohades; Asl, Babak Mohammadzadeh

2018-06-04

Minimum variance beamformer (MVB) improves the resolution and contrast of medical ultrasound images compared with delay and sum (DAS) beamformer. The weight vector of this beamformer should be calculated for each imaging point independently, with a cost of increasing computational complexity. The large number of necessary calculations limits this beamformer to application in real-time systems. A beamformer is proposed based on the MVB with lower computational complexity while preserving its advantages. This beamformer avoids matrix inversion, which is the most complex part of the MVB, by solving the optimization problem iteratively. The received signals from two imaging points close together do not vary much in medical ultrasound imaging. Therefore, using the previously optimized weight vector for one point as initial weight vector for the new neighboring point can improve the convergence speed and decrease the computational complexity. The proposed method was applied on several data sets, and it has been shown that the method can regenerate the results obtained by the MVB while the order of complexity is decreased from O(L 3 ) to O(L 2 ). Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

An application of digital network technology to medical image management.

PubMed

Chu, W K; Smith, C L; Wobig, R K; Hahn, F A

1997-01-01

With the advent of network technology, there is considerable interest within the medical community to manage the storage and distribution of medical images by digital means. Higher workflow efficiency leading to better patient care is one of the commonly cited outcomes [1,2]. However, due to the size of medical image files and the unique requirements in detail and resolution, medical image management poses special challenges. Storage requirements are usually large, which implies expenses or investment costs make digital networking projects financially out of reach for many clinical institutions. New advances in network technology and telecommunication, in conjunction with the decreasing cost in computer devices, have made digital image management achievable. In our institution, we have recently completed a pilot project to distribute medical images both within the physical confines of the clinical enterprise as well as outside the medical center campus. The design concept and the configuration of a comprehensive digital image network is described in this report.

Compressive sensing in medical imaging

PubMed Central

Graff, Christian G.; Sidky, Emil Y.

2015-01-01

The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed. PMID:25968400

Medical applications for high-performance computers in SKIF-GRID network.

PubMed

Zhuchkov, Alexey; Tverdokhlebov, Nikolay

2009-01-01

The paper presents a set of software services for massive mammography image processing by using high-performance parallel computers of SKIF-family which are linked into a service-oriented grid-network. An experience of a prototype system implementation in two medical institutions is also described.

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.

Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging.

PubMed

Garcia-Hernandez, Jose Juan; Gomez-Flores, Wilfrido; Rubio-Loyola, Javier

2016-01-01

Medical images (MI) are relevant sources of information for detecting and diagnosing a large number of illnesses and abnormalities. Due to their importance, this study is focused on breast ultrasound (BUS), which is the main adjunct for mammography to detect common breast lesions among women worldwide. On the other hand, aiming to enhance data security, image fidelity, authenticity, and content verification in e-health environments, MI watermarking has been widely used, whose main goal is to embed patient meta-data into MI so that the resulting image keeps its original quality. In this sense, this paper deals with the comparison of two watermarking approaches, namely spread spectrum based on the discrete cosine transform (SS-DCT) and the high-capacity data-hiding (HCDH) algorithm, so that the watermarked BUS images are guaranteed to be adequate for a computer-aided diagnosis (CADx) system, whose two principal outcomes are lesion segmentation and classification. Experimental results show that HCDH algorithm is highly recommended for watermarking medical images, maintaining the image quality and without introducing distortion into the output of CADx. Copyright © 2015 Elsevier Ltd. All rights reserved.

From chalkboard, slides, and paper to e-learning: How computing technologies have transformed anatomical sciences education.

PubMed

Trelease, Robert B

2016-11-01

Until the late-twentieth century, primary anatomical sciences education was relatively unenhanced by advanced technology and dependent on the mainstays of printed textbooks, chalkboard- and photographic projection-based classroom lectures, and cadaver dissection laboratories. But over the past three decades, diffusion of innovations in computer technology transformed the practices of anatomical education and research, along with other aspects of work and daily life. Increasing adoption of first-generation personal computers (PCs) in the 1980s paved the way for the first practical educational applications, and visionary anatomists foresaw the usefulness of computers for teaching. While early computers lacked high-resolution graphics capabilities and interactive user interfaces, applications with video discs demonstrated the practicality of programming digital multimedia linking descriptive text with anatomical imaging. Desktop publishing established that computers could be used for producing enhanced lecture notes, and commercial presentation software made it possible to give lectures using anatomical and medical imaging, as well as animations. Concurrently, computer processing supported the deployment of medical imaging modalities, including computed tomography, magnetic resonance imaging, and ultrasound, that were subsequently integrated into anatomy instruction. Following its public birth in the mid-1990s, the World Wide Web became the ubiquitous multimedia networking technology underlying the conduct of contemporary education and research. Digital video, structural simulations, and mobile devices have been more recently applied to education. Progressive implementation of computer-based learning methods interacted with waves of ongoing curricular change, and such technologies have been deemed crucial for continuing medical education reforms, providing new challenges and opportunities for anatomical sciences educators. Anat Sci Educ 9: 583-602. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

Computer Assisted Thermography And Its Application In Ovulation Detection

NASA Astrophysics Data System (ADS)

Rao, K. H.; Shah, A. V.

1984-08-01

Hardware and software of a computer-assisted image analyzing system used for infrared images in medical applications are discussed. The application of computer-assisted thermography (CAT) as a complementary diagnostic tool in centralized diagnostic management is proposed. The authors adopted 'Computer Assisted Thermography' to study physiological changes in the breasts related to the hormones characterizing the menstrual cycle of a woman. Based on clinical experi-ments followed by thermal image analysis, they suggest that 'differential skin temperature (DST)1 be measured to detect the fertility interval in the menstrual cycle of a woman.

[Application of computer-assisted 3D imaging simulation for surgery].

PubMed

Matsushita, S; Suzuki, N

1994-03-01

This article describes trends in application of various imaging technology in surgical planning, navigation, and computer aided surgery. Imaging information is essential factor for simulation in medicine. It includes three dimensional (3D) image reconstruction, neuro-surgical navigation, creating substantial model based on 3D imaging data and etc. These developments depend mostly on 3D imaging technique, which is much contributed by recent computer technology. 3D imaging can offer new intuitive information to physician and surgeon, and this method is suitable for mechanical control. By utilizing simulated results, we can obtain more precise surgical orientation, estimation, and operation. For more advancement, automatic and high speed recognition of medical imaging is being developed.

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2008-03-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

A data colocation grid framework for big data medical image processing: backend design

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J.; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A.

2018-03-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework's performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop and HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

A Data Colocation Grid Framework for Big Data Medical Image Processing: Backend Design.

PubMed

Bao, Shunxing; Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A

2018-03-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework's performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop & HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

A Data Colocation Grid Framework for Big Data Medical Image Processing: Backend Design

PubMed Central

Huo, Yuankai; Parvathaneni, Prasanna; Plassard, Andrew J.; Bermudez, Camilo; Yao, Yuang; Lyu, Ilwoo; Gokhale, Aniruddha; Landman, Bennett A.

2018-01-01

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework’s performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop & HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available. PMID:29887668

Computational Intelligence for Medical Imaging Simulations.

PubMed

Chang, Victor

2017-11-25

This paper describes how to simulate medical imaging by computational intelligence to explore areas that cannot be easily achieved by traditional ways, including genes and proteins simulations related to cancer development and immunity. This paper has presented simulations and virtual inspections of BIRC3, BIRC6, CCL4, KLKB1 and CYP2A6 with their outputs and explanations, as well as brain segment intensity due to dancing. Our proposed MapReduce framework with the fusion algorithm can simulate medical imaging. The concept is very similar to the digital surface theories to simulate how biological units can get together to form bigger units, until the formation of the entire unit of biological subject. The M-Fusion and M-Update function by the fusion algorithm can achieve a good performance evaluation which can process and visualize up to 40 GB of data within 600 s. We conclude that computational intelligence can provide effective and efficient healthcare research offered by simulations and visualization.

Understanding the Role of Hemodynamics in the Initiation, Progression, Rupture, and Treatment Outcome of Cerebral Aneurysm from Medical Image-Based Computational Studies

PubMed Central

Castro, Marcelo A.

2013-01-01

About a decade ago, the first image-based computational hemodynamic studies of cerebral aneurysms were presented. Their potential for clinical applications was the result of a right combination of medical image processing, vascular reconstruction, and grid generation techniques used to reconstruct personalized domains for computational fluid and solid dynamics solvers and data analysis and visualization techniques. A considerable number of studies have captivated the attention of clinicians, neurosurgeons, and neuroradiologists, who realized the ability of those tools to help in understanding the role played by hemodynamics in the natural history and management of intracranial aneurysms. This paper intends to summarize the most relevant results in the field reported during the last years. PMID:24967285

Information granules in image histogram analysis.

PubMed

Wieclawek, Wojciech

2018-04-01

A concept of granular computing employed in intensity-based image enhancement is discussed. First, a weighted granular computing idea is introduced. Then, the implementation of this term in the image processing area is presented. Finally, multidimensional granular histogram analysis is introduced. The proposed approach is dedicated to digital images, especially to medical images acquired by Computed Tomography (CT). As the histogram equalization approach, this method is based on image histogram analysis. Yet, unlike the histogram equalization technique, it works on a selected range of the pixel intensity and is controlled by two parameters. Performance is tested on anonymous clinical CT series. Copyright © 2017 Elsevier Ltd. All rights reserved.

Radiation dose management for pediatric cardiac computed tomography: a report from the Image Gently 'Have-A-Heart' campaign.

PubMed

Rigsby, Cynthia K; McKenney, Sarah E; Hill, Kevin D; Chelliah, Anjali; Einstein, Andrew J; Han, B Kelly; Robinson, Joshua D; Sammet, Christina L; Slesnick, Timothy C; Frush, Donald P

2018-01-01

Children with congenital or acquired heart disease can be exposed to relatively high lifetime cumulative doses of ionizing radiation from necessary medical imaging procedures including radiography, fluoroscopic procedures including diagnostic and interventional cardiac catheterizations, electrophysiology examinations, cardiac computed tomography (CT) studies, and nuclear cardiology examinations. Despite the clinical necessity of these imaging studies, the related ionizing radiation exposure could pose an increased lifetime attributable cancer risk. The Image Gently "Have-A-Heart" campaign is promoting the appropriate use of medical imaging studies in children with congenital or acquired heart disease while minimizing radiation exposure. The focus of this manuscript is to provide a comprehensive review of radiation dose management and CT performance in children with congenital or acquired heart disease.

Creation of Anatomically Accurate Computer-Aided Design (CAD) Solid Models from Medical Images

NASA Technical Reports Server (NTRS)

Stewart, John E.; Graham, R. Scott; Samareh, Jamshid A.; Oberlander, Eric J.; Broaddus, William C.

1999-01-01

Most surgical instrumentation and implants used in the world today are designed with sophisticated Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM) software. This software automates the mechanical development of a product from its conceptual design through manufacturing. CAD software also provides a means of manipulating solid models prior to Finite Element Modeling (FEM). Few surgical products are designed in conjunction with accurate CAD models of human anatomy because of the difficulty with which these models are created. We have developed a novel technique that creates anatomically accurate, patient specific CAD solids from medical images in a matter of minutes.

Scalable, High-performance 3D Imaging Software Platform: System Architecture and Application to Virtual Colonoscopy

PubMed Central

Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli; Brett, Bevin

2013-01-01

One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. In this work, we have developed a software platform that is designed to support high-performance 3D medical image processing for a wide range of applications using increasingly available and affordable commodity computing systems: multi-core, clusters, and cloud computing systems. To achieve scalable, high-performance computing, our platform (1) employs size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D image processing algorithms; (2) supports task scheduling for efficient load distribution and balancing; and (3) consists of a layered parallel software libraries that allow a wide range of medical applications to share the same functionalities. We evaluated the performance of our platform by applying it to an electronic cleansing system in virtual colonoscopy, with initial experimental results showing a 10 times performance improvement on an 8-core workstation over the original sequential implementation of the system. PMID:23366803

Multilevel image recognition using discriminative patches and kernel covariance descriptor

NASA Astrophysics Data System (ADS)

Lu, Le; Yao, Jianhua; Turkbey, Evrim; Summers, Ronald M.

2014-03-01

Computer-aided diagnosis of medical images has emerged as an important tool to objectively improve the performance, accuracy and consistency for clinical workflow. To computerize the medical image diagnostic recognition problem, there are three fundamental problems: where to look (i.e., where is the region of interest from the whole image/volume), image feature description/encoding, and similarity metrics for classification or matching. In this paper, we exploit the motivation, implementation and performance evaluation of task-driven iterative, discriminative image patch mining; covariance matrix based descriptor via intensity, gradient and spatial layout; and log-Euclidean distance kernel for support vector machine, to address these three aspects respectively. To cope with often visually ambiguous image patterns for the region of interest in medical diagnosis, discovery of multilabel selective discriminative patches is desired. Covariance of several image statistics summarizes their second order interactions within an image patch and is proved as an effective image descriptor, with low dimensionality compared with joint statistics and fast computation regardless of the patch size. We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of degenerative disc disease (DDD) detection on cortical shell unwrapped CT maps and colitis detection on CT key images. The proposed approach is validated with promising quantitative results on these challenging tasks. Our experimental findings and discussion also unveil some interesting insights on the covariance feature composition with or without spatial layout for classification and retrieval, and different kernel constructions for SVM. This will also shed some light on future work using covariance feature and kernel classification for medical image analysis.

Prospective comparison of the usage of conventional film and PACS based computed radiography for portable chest x-ray imaging in a medical intensive care unit

NASA Astrophysics Data System (ADS)

Kundel, Harold L.; Seshadri, Sridhar B.; Langlotz, Curtis P.; Lanken, Paul N.; Horii, Steven C.; Polansky, Marcia; Kishore, Sheel; Finegold, Eric; Brikman, Inna; Bozzo, Mary T.; Redfern, Regina O.

1995-05-01

The purpose of this study was to compare the efficiency of image delivery, the effectiveness of image information transfer, and the timeliness of clinical actions in a medical intensive care unit (MICU) using either conventional screen-film imaging (SF-HC), computed radiography (CR-HC) or a CR based PACS. When the CR based PACS was in use, images could be viewed in the MICU on digital workstation (CR-WS) or in the radiology department as laser printed hard copy (CR-HC). Data were collected by daily interviews with the house-staff, by monitoring computer log-ons and other time stamped activities, and by observing film viewing times in the radiology department with surveillance cameras. The time at which image information was made available to the MICU physicians was decreased during the CR-PACS period as compared with either the SF-HC periods or the CR-HC periods but the image information was not accessed more quickly by the clinical staff. However, the time required to perform image related clinical actions for pulmonary and pleural problems was decreased when images were viewed on the workstation.

Use of mobile devices for medical imaging.

PubMed

Hirschorn, David S; Choudhri, Asim F; Shih, George; Kim, Woojin

2014-12-01

Mobile devices have fundamentally changed personal computing, with many people forgoing the desktop and even laptop computer altogether in favor of a smaller, lighter, and cheaper device with a touch screen. Doctors and patients are beginning to expect medical images to be available on these devices for consultative viewing, if not actual diagnosis. However, this raises serious concerns with regard to the ability of existing mobile devices and networks to quickly and securely move these images. Medical images often come in large sets, which can bog down a network if not conveyed in an intelligent manner, and downloaded data on a mobile device are highly vulnerable to a breach of patient confidentiality should that device become lost or stolen. Some degree of regulation is needed to ensure that the software used to view these images allows all relevant medical information to be visible and manipulated in a clinically acceptable manner. There also needs to be a quality control mechanism to ensure that a device's display accurately conveys the image content without loss of contrast detail. Furthermore, not all mobile displays are appropriate for all types of images. The smaller displays of smart phones, for example, are not well suited for viewing entire chest radiographs, no matter how small and numerous the pixels of the display may be. All of these factors should be taken into account when deciding where, when, and how to use mobile devices for the display of medical images. Copyright © 2014 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Processing the image gradient field using a topographic primal sketch approach.

PubMed

Gambaruto, A M

2015-03-01

The spatial derivatives of the image intensity provide topographic information that may be used to identify and segment objects. The accurate computation of the derivatives is often hampered in medical images by the presence of noise and a limited resolution. This paper focuses on accurate computation of spatial derivatives and their subsequent use to process an image gradient field directly, from which an image with improved characteristics can be reconstructed. The improvements include noise reduction, contrast enhancement, thinning object contours and the preservation of edges. Processing the gradient field directly instead of the image is shown to have numerous benefits. The approach is developed such that the steps are modular, allowing the overall method to be improved and possibly tailored to different applications. As presented, the approach relies on a topographic representation and primal sketch of an image. Comparisons with existing image processing methods on a synthetic image and different medical images show improved results and accuracy in segmentation. Here, the focus is on objects with low spatial resolution, which is often the case in medical images. The methods developed show the importance of improved accuracy in derivative calculation and the potential in processing the image gradient field directly. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising.

PubMed

Khanian, Maryam; Feizi, Awat; Davari, Ali

2014-01-01

Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

Confidential storage and transmission of medical image data.

PubMed

Norcen, R; Podesser, M; Pommer, A; Schmidt, H-P; Uhl, A

2003-05-01

We discuss computationally efficient techniques for confidential storage and transmission of medical image data. Two types of partial encryption techniques based on AES are proposed. The first encrypts a subset of bitplanes of plain image data whereas the second encrypts parts of the JPEG2000 bitstream. We find that encrypting between 20% and 50% of the visual data is sufficient to provide high confidentiality.

Imaging in anatomy: a comparison of imaging techniques in embalmed human cadavers

PubMed Central

2013-01-01

Background A large variety of imaging techniques is an integral part of modern medicine. Introducing radiological imaging techniques into the dissection course serves as a basis for improved learning of anatomy and multidisciplinary learning in pre-clinical medical education. Methods Four different imaging techniques (ultrasound, radiography, computed tomography, and magnetic resonance imaging) were performed in embalmed human body donors to analyse possibilities and limitations of the respective techniques in this peculiar setting. Results The quality of ultrasound and radiography images was poor, images of computed tomography and magnetic resonance imaging were of good quality. Conclusion Computed tomography and magnetic resonance imaging have a superior image quality in comparison to ultrasound and radiography and offer suitable methods for imaging embalmed human cadavers as a valuable addition to the dissection course. PMID:24156510

Italian Chapter of the International Society of Cardiovascular Ultrasound expert consensus document on coronary computed tomography angiography: overview and new insights.

PubMed

Sozzi, Fabiola B; Maiello, Maria; Pelliccia, Francesco; Parato, Vito Maurizio; Canetta, Ciro; Savino, Ketty; Lombardi, Federico; Palmiero, Pasquale

2016-09-01

Coronary computed tomography angiography is a noninvasive heart imaging test currently undergoing rapid development and advancement. The high resolution of the three-dimensional pictures of the moving heart and great vessels is performed during a coronary computed tomography to identify coronary artery disease and classify patient risk for atherosclerotic cardiovascular disease. The technique provides useful information about the coronary tree and atherosclerotic plaques beyond simple luminal narrowing and plaque type defined by calcium content. This application will improve image-guided prevention, medical therapy, and coronary interventions. The ability to interpret coronary computed tomography images is of utmost importance as we develop personalized medical care to enable therapeutic interventions stratified on the bases of plaque characteristics. This overview provides available data and expert's recommendations in the utilization of coronary computed tomography findings. We focus on the use of coronary computed tomography to detect coronary artery disease and stratify patients at risk, illustrating the implications of this test on patient management. We describe its diagnostic power in identifying patients at higher risk to develop acute coronary syndrome and its prognostic significance. Finally, we highlight the features of the vulnerable plaques imaged by coronary computed tomography angiography. © 2016, Wiley Periodicals, Inc.

Computer-aided diagnosis in radiological imaging: current status and future challenges

NASA Astrophysics Data System (ADS)

Doi, Kunio

2009-10-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. Many different types of CAD schemes are being developed for detection and/or characterization of various lesions in medical imaging, including conventional projection radiography, CT, MRI, and ultrasound imaging. Commercial systems for detection of breast lesions on mammograms have been developed and have received FDA approval for clinical use. CAD may be defined as a diagnosis made by a physician who takes into account the computer output as a "second opinion". The purpose of CAD is to improve the quality and productivity of physicians in their interpretation of radiologic images. The quality of their work can be improved in terms of the accuracy and consistency of their radiologic diagnoses. In addition, the productivity of radiologists is expected to be improved by a reduction in the time required for their image readings. The computer output is derived from quantitative analysis of radiologic images by use of various methods and techniques in computer vision, artificial intelligence, and artificial neural networks (ANNs). The computer output may indicate a number of important parameters, for example, the locations of potential lesions such as lung cancer and breast cancer, the likelihood of malignancy of detected lesions, and the likelihood of various diseases based on differential diagnosis in a given image and clinical parameters. In this review article, the basic concept of CAD is first defined, and the current status of CAD research is then described. In addition, the potential of CAD in the future is discussed and predicted.

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine

PubMed Central

Bao, Shunxing; Weitendorf, Frederick D.; Plassard, Andrew J.; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A.

2016-01-01

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., “short” processing times and/or “large” datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply “large scale” processing transitions into “big data” and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and non-relevant for medical imaging. PMID:28736473

Future Directions in Medical Physics: Models, Technology, and Translation to Medicine

NASA Astrophysics Data System (ADS)

Siewerdsen, Jeffrey

The application of physics in medicine has been integral to major advances in diagnostic and therapeutic medicine. Two primary areas represent the mainstay of medical physics research in the last century: in radiation therapy, physicists have propelled advances in conformal radiation treatment and high-precision image guidance; and in diagnostic imaging, physicists have advanced an arsenal of multi-modality imaging that includes CT, MRI, ultrasound, and PET as indispensible tools for noninvasive screening, diagnosis, and assessment of treatment response. In addition to their role in building such technologically rich fields of medicine, physicists have also become integral to daily clinical practice in these areas. The future suggests new opportunities for multi-disciplinary research bridging physics, biology, engineering, and computer science, and collaboration in medical physics carries a strong capacity for identification of significant clinical needs, access to clinical data, and translation of technologies to clinical studies. In radiation therapy, for example, the extraction of knowledge from large datasets on treatment delivery, image-based phenotypes, genomic profile, and treatment outcome will require innovation in computational modeling and connection with medical physics for the curation of large datasets. Similarly in imaging physics, the demand for new imaging technology capable of measuring physical and biological processes over orders of magnitude in scale (from molecules to whole organ systems) and exploiting new contrast mechanisms for greater sensitivity to molecular agents and subtle functional / morphological change will benefit from multi-disciplinary collaboration in physics, biology, and engineering. Also in surgery and interventional radiology, where needs for increased precision and patient safety meet constraints in cost and workflow, development of new technologies for imaging, image registration, and robotic assistance can leverage collaboration in physics, biomedical engineering, and computer science. In each area, there is major opportunity for multi-disciplinary collaboration with medical physics to accelerate the translation of such technologies to clinical use. Research supported by the National Institutes of Health, Siemens Healthcare, and Carestream Health.

Pilot study on the effects of a computer-based medical image system.

PubMed Central

Wu, S. C.; Smith, J. W.; Swan, J. E.

1996-01-01

Current medical imaging systems are developed for the purpose of data management. Evaluations of these systems are usually done by assessing users' subjective appreciation rather than objectively gauging performance influence. The present report discusses the evaluation of a medical image presentation system prototype utilizing a cognitive approach. Experimental results showed hypothesized performance improvement attributed to advanced presentation techniques. However, this improvement was almost inadvertently masked by users' previous strategies and interactions with new technology. Overall these data demonstrate the potential benefit of implementing such a system in actual practice as well as provide an example of applying the cognitive approach in evaluating the usability of medical systems. Images Figure 1 PMID:8947750

The LISS--a public database of common imaging signs of lung diseases for computer-aided detection and diagnosis research and medical education.

PubMed

Han, Guanghui; Liu, Xiabi; Han, Feifei; Santika, I Nyoman Tenaya; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2015-02-01

Lung computed tomography (CT) imaging signs play important roles in the diagnosis of lung diseases. In this paper, we review the significance of CT imaging signs in disease diagnosis and determine the inclusion criterion of CT scans and CT imaging signs of our database. We develop the software of abnormal regions annotation and design the storage scheme of CT images and annotation data. Then, we present a publicly available database of lung CT imaging signs, called LISS for short, which contains 271 CT scans and 677 abnormal regions in them. The 677 abnormal regions are divided into nine categories of common CT imaging signs of lung disease (CISLs). The ground truth of these CISLs regions and the corresponding categories are provided. Furthermore, to make the database publicly available, all private data in CT scans are eliminated or replaced with provisioned values. The main characteristic of our LISS database is that it is developed from a new perspective of CT imaging signs of lung diseases instead of commonly considered lung nodules. Thus, it is promising to apply to computer-aided detection and diagnosis research and medical education.

Opportunities for Fluorochlorozirconate and Other Glass-Ceramic Detectors in Medical Imaging Devices

PubMed Central

Johnson, Jacqueline A.; Leonard, Russell L.; Lubinsky, AR; Schweizer, Stefan

2017-01-01

This article gives an overview of fluorochlorozirconate glass-ceramic scintillators and storage phosphor materials: how they are synthesized, what their properties are, and how they can be used in medical imaging. Such materials can enhance imaging in x-ray radiography, especially mammography and dental imaging, computed tomography, and positron emission tomography. Although focusing on fluorochlorozirconate materials, the reader will find the discussion is relevant to other luminescent glass and glass-ceramic systems. PMID:28890955

Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images.

PubMed

Maier-Hein, Lena; Mersmann, Sven; Kondermann, Daniel; Bodenstedt, Sebastian; Sanchez, Alexandro; Stock, Christian; Kenngott, Hannes Gotz; Eisenmann, Mathias; Speidel, Stefanie

2014-01-01

Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available.

Platform-independent software for medical image processing on the Internet

NASA Astrophysics Data System (ADS)

Mancuso, Michael E.; Pathak, Sayan D.; Kim, Yongmin

1997-05-01

We have developed a software tool for image processing over the Internet. The tool is a general purpose, easy to use, flexible, platform independent image processing software package with functions most commonly used in medical image processing.It provides for processing of medical images located wither remotely on the Internet or locally. The software was written in Java - the new programming language developed by Sun Microsystems. It was compiled and tested using Microsoft's Visual Java 1.0 and Microsoft's Just in Time Compiler 1.00.6211. The software is simple and easy to use. In order to use the tool, the user needs to download the software from our site before he/she runs it using any Java interpreter, such as those supplied by Sun, Symantec, Borland or Microsoft. Future versions of the operating systems supplied by Sun, Microsoft, Apple, IBM, and others will include Java interpreters. The software is then able to access and process any image on the iNternet or on the local computer. Using a 512 X 512 X 8-bit image, a 3 X 3 convolution took 0.88 seconds on an Intel Pentium Pro PC running at 200 MHz with 64 Mbytes of memory. A window/level operation took 0.38 seconds while a 3 X 3 median filter took 0.71 seconds. These performance numbers demonstrate the feasibility of using this software interactively on desktop computes. Our software tool supports various image processing techniques commonly used in medical image processing and can run without the need of any specialized hardware. It can become an easily accessible resource over the Internet to promote the learning and of understanding image processing algorithms. Also, it could facilitate sharing of medical image databases and collaboration amongst researchers and clinicians, regardless of location.

High-performance image processing on the desktop

NASA Astrophysics Data System (ADS)

Jordan, Stephen D.

1996-04-01

The suitability of computers to the task of medical image visualization for the purposes of primary diagnosis and treatment planning depends on three factors: speed, image quality, and price. To be widely accepted the technology must increase the efficiency of the diagnostic and planning processes. This requires processing and displaying medical images of various modalities in real-time, with accuracy and clarity, on an affordable system. Our approach to meeting this challenge began with market research to understand customer image processing needs. These needs were translated into system-level requirements, which in turn were used to determine which image processing functions should be implemented in hardware. The result is a computer architecture for 2D image processing that is both high-speed and cost-effective. The architectural solution is based on the high-performance PA-RISC workstation with an HCRX graphics accelerator. The image processing enhancements are incorporated into the image visualization accelerator (IVX) which attaches to the HCRX graphics subsystem. The IVX includes a custom VLSI chip which has a programmable convolver, a window/level mapper, and an interpolator supporting nearest-neighbor, bi-linear, and bi-cubic modes. This combination of features can be used to enable simultaneous convolution, pan, zoom, rotate, and window/level control into 1 k by 1 k by 16-bit medical images at 40 frames/second.

Medical image denoising via optimal implementation of non-local means on hybrid parallel architecture.

PubMed

Nguyen, Tuan-Anh; Nakib, Amir; Nguyen, Huy-Nam

2016-06-01

The Non-local means denoising filter has been established as gold standard for image denoising problem in general and particularly in medical imaging due to its efficiency. However, its computation time limited its applications in real world application, especially in medical imaging. In this paper, a distributed version on parallel hybrid architecture is proposed to solve the computation time problem and a new method to compute the filters' coefficients is also proposed, where we focused on the implementation and the enhancement of filters' parameters via taking the neighborhood of the current voxel more accurately into account. In terms of implementation, our key contribution consists in reducing the number of shared memory accesses. The different tests of the proposed method were performed on the brain-web database for different levels of noise. Performances and the sensitivity were quantified in terms of speedup, peak signal to noise ratio, execution time, the number of floating point operations. The obtained results demonstrate the efficiency of the proposed method. Moreover, the implementation is compared to that of other techniques, recently published in the literature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Medical imaging and computers in the diagnosis of breast cancer

NASA Astrophysics Data System (ADS)

Giger, Maryellen L.

2014-09-01

Computer-aided diagnosis (CAD) and quantitative image analysis (QIA) methods (i.e., computerized methods of analyzing digital breast images: mammograms, ultrasound, and magnetic resonance images) can yield novel image-based tumor and parenchyma characteristics (i.e., signatures that may ultimately contribute to the design of patient-specific breast cancer management plans). The role of QIA/CAD has been expanding beyond screening programs towards applications in risk assessment, diagnosis, prognosis, and response to therapy as well as in data mining to discover relationships of image-based lesion characteristics with genomics and other phenotypes; thus, as they apply to disease states. These various computer-based applications are demonstrated through research examples from the Giger Lab.

The holographic display of three-dimensional medical objects through the usage of a shiftable cylindrical lens

NASA Astrophysics Data System (ADS)

Teng, Dongdong; Liu, Lilin; Zhang, Yueli; Pang, Zhiyong; Wang, Biao

2014-09-01

Through the creative usage of a shiftable cylindrical lens, a wide-view-angle holographic display system is developed for medical object display in real three-dimensional (3D) space based on a time-multiplexing method. The two-dimensional (2D) source images for all computer generated holograms (CGHs) needed by the display system are only one group of computerized tomography (CT) or magnetic resonance imaging (MRI) slices from the scanning device. Complicated 3D message reconstruction on the computer is not necessary. A pelvis is taken as the target medical object to demonstrate this method and the obtained horizontal viewing angle reaches 28°.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

[A computer-aided image diagnosis and study system].

PubMed

Li, Zhangyong; Xie, Zhengxiang

2004-08-01

The revolution in information processing, particularly the digitizing of medicine, has changed the medical study, work and management. This paper reports a method to design a system for computer-aided image diagnosis and study. Combined with some good idea of graph-text system and picture archives communicate system (PACS), the system was realized and used for "prescription through computer", "managing images" and "reading images under computer and helping the diagnosis". Also typical examples were constructed in a database and used to teach the beginners. The system was developed by the visual developing tools based on object oriented programming (OOP) and was carried into operation on the Windows 9X platform. The system possesses friendly man-machine interface.

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.

Medical imaging and registration in computer assisted surgery.

PubMed

Simon, D A; Lavallée, S

1998-09-01

Imaging, sensing, and computing technologies that are being introduced to aid in the planning and execution of surgical procedures are providing orthopaedic surgeons with a powerful new set of tools for improving clinical accuracy, reliability, and patient outcomes while reducing costs and operating times. Current computer assisted surgery systems typically include a measurement process for collecting patient specific medical data, a decision making process for generating a surgical plan, a registration process for aligning the surgical plan to the patient, and an action process for accurately achieving the goals specified in the plan. Some of the key concepts in computer assisted surgery applied to orthopaedics with a focus on the basic framework and underlying technologies is outlined. In addition, technical challenges and future trends in the field are discussed.

Emerging Computer Media: On Image Interaction

NASA Astrophysics Data System (ADS)

Lippman, Andrew B.

1982-01-01

Emerging technologies such as inexpensive, powerful local computing, optical digital videodiscs, and the technologies of human-machine interaction are initiating a revolution in both image storage systems and image interaction systems. This paper will present a review of new approaches to computer media predicated upon three dimensional position sensing, speech recognition, and high density image storage. Examples will be shown such as the Spatial Data Management Systems wherein the free use of place results in intuitively clear retrieval systems and potentials for image association; the Movie-Map, wherein inherently static media generate dynamic views of data, and conferencing work-in-progress wherein joint processing is stressed. Application to medical imaging will be suggested, but the primary emphasis is on the general direction of imaging and reference systems. We are passing the age of simple possibility of computer graphics and image porcessing and entering the age of ready usability.

Ultrasound introscopic image quantitative characteristics for medical diagnosis

NASA Astrophysics Data System (ADS)

Novoselets, Mikhail K.; Sarkisov, Sergey S.; Gridko, Alexander N.; Tcheban, Anatoliy K.

1993-09-01

The results on computer aided extraction of quantitative characteristics (QC) of ultrasound introscopic images for medical diagnosis are presented. Thyroid gland (TG) images of Chernobil Accident sufferers are considered. It is shown that TG diseases can be associated with some values of selected QCs of random echo distribution in the image. The possibility of these QCs usage for TG diseases recognition in accordance with calculated values is analyzed. The role of speckle noise elimination in the solution of the problem on TG diagnosis is considered too.

Techniques on semiautomatic segmentation using the Adobe Photoshop

NASA Astrophysics Data System (ADS)

Park, Jin Seo; Chung, Min Suk; Hwang, Sung Bae

2005-04-01

The purpose of this research is to enable anybody to semiautomatically segment the anatomical structures in the MRIs, CTs, and other medical images on the personal computer. The segmented images are used for making three-dimensional images, which are helpful in medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was MR scanned to make 557 MRIs, which were transferred to a personal computer. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL; successively, manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a likewise manner, 11 anatomical structures in the 8,500 anatomcial images were segmented. Also, 12 brain and 10 heart anatomical structures in anatomical images were segmented. Proper segmentation was verified by making and examining the coronal, sagittal, and three-dimensional images from the segmented images. During semiautomatic segmentation on Adobe Photoshop, suitable algorithm could be used, the extent of automatization could be regulated, convenient user interface could be used, and software bugs rarely occurred. The techniques of semiautomatic segmentation using Adobe Photoshop are expected to be widely used for segmentation of the anatomical structures in various medical images.

MDA-image: an environment of networked desktop computers for teleradiology/pathology.

PubMed

Moffitt, M E; Richli, W R; Carrasco, C H; Wallace, S; Zimmerman, S O; Ayala, A G; Benjamin, R S; Chee, S; Wood, P; Daniels, P

1991-04-01

MDA-Image, a project of The University of Texas M. D. Anderson Cancer Center, is an environment of networked desktop computers for teleradiology/pathology. Radiographic film is digitized with a film scanner and histopathologic slides are digitized using a red, green, and blue (RGB) video camera connected to a microscope. Digitized images are stored on a data server connected to the institution's computer communication network (Ethernet) and can be displayed from authorized desktop computers connected to Ethernet. Images are digitized for cases presented at the Bone Tumor Management Conference, a multidisciplinary conference in which treatment options are discussed among clinicians, surgeons, radiologists, pathologists, radiotherapists, and medical oncologists. These radiographic and histologic images are shown on a large screen computer monitor during the conference. They are available for later review for follow-up or representation.

KAMEDIN: a telemedicine system for computer supported cooperative work and remote image analysis in radiology.

PubMed

Handels, H; Busch, C; Encarnação, J; Hahn, C; Kühn, V; Miehe, J; Pöppl, S I; Rinast, E; Rossmanith, C; Seibert, F; Will, A

1997-03-01

The software system KAMEDIN (Kooperatives Arbeiten und MEdizinische Diagnostik auf Innovativen Netzen) is a multimedia telemedicine system for exchange, cooperative diagnostics, and remote analysis of digital medical image data. It provides components for visualisation, processing, and synchronised audio-visual discussion of medical images. Techniques of computer supported cooperative work (CSCW) synchronise user interactions during a teleconference. Visibility of both local and remote cursor on the conference workstations facilitates telepointing and reinforces the conference partner's telepresence. Audio communication during teleconferences is supported by an integrated audio component. Furthermore, brain tissue segmentation with artificial neural networks can be performed on an external supercomputer as a remote image analysis procedure. KAMEDIN is designed as a low cost CSCW tool for ISDN based telecommunication. However it can be used on any TCP/IP supporting network. In a field test, KAMEDIN was installed in 15 clinics and medical departments to validate the systems' usability. The telemedicine system KAMEDIN has been developed, tested, and evaluated within a research project sponsored by German Telekom.

A Multimodal Search Engine for Medical Imaging Studies.

PubMed

Pinho, Eduardo; Godinho, Tiago; Valente, Frederico; Costa, Carlos

2017-02-01

The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics. This paper proposes an extensible platform for multimodal medical image retrieval, integrated in an open-source PACS software with profile-based CBIR capabilities. In this article, we detail a technical approach to the problem by describing its main architecture and each sub-component, as well as the available web interfaces and the multimodal query techniques applied. Finally, we assess our implementation of the engine with computational performance benchmarks.

[Computerization and robotics in medical practice].

PubMed

Dervaderics, J

1997-10-26

The article gives the outlines of all principles used in computing included the non-electrical and analog computers and the artifical intelligence followed by citing examples as well. The principles and medical utilization of virtual reality are also mentioned. There are discussed: surgical planning, image guided surgery, robotic surgery, telepresence and telesurgery, and telemedicine implemented partially via Internet.

Content-independent embedding scheme for multi-modal medical image watermarking.

PubMed

Nyeem, Hussain; Boles, Wageeh; Boyd, Colin

2015-02-04

As the increasing adoption of information technology continues to offer better distant medical services, the distribution of, and remote access to digital medical images over public networks continues to grow significantly. Such use of medical images raises serious concerns for their continuous security protection, which digital watermarking has shown great potential to address. We present a content-independent embedding scheme for medical image watermarking. We observe that the perceptual content of medical images varies widely with their modalities. Recent medical image watermarking schemes are image-content dependent and thus they may suffer from inconsistent embedding capacity and visual artefacts. To attain the image content-independent embedding property, we generalise RONI (region of non-interest, to the medical professionals) selection process and use it for embedding by utilising RONI's least significant bit-planes. The proposed scheme thus avoids the need for RONI segmentation that incurs capacity and computational overheads. Our experimental results demonstrate that the proposed embedding scheme performs consistently over a dataset of 370 medical images including their 7 different modalities. Experimental results also verify how the state-of-the-art reversible schemes can have an inconsistent performance for different modalities of medical images. Our scheme has MSSIM (Mean Structural SIMilarity) larger than 0.999 with a deterministically adaptable embedding capacity. Our proposed image-content independent embedding scheme is modality-wise consistent, and maintains a good image quality of RONI while keeping all other pixels in the image untouched. Thus, with an appropriate watermarking framework (i.e., with the considerations of watermark generation, embedding and detection functions), our proposed scheme can be viable for the multi-modality medical image applications and distant medical services such as teleradiology and eHealth.

The Computer as a Tool for Learning

PubMed Central

Starkweather, John A.

1986-01-01

Experimenters from the beginning recognized the advantages computers might offer in medical education. Several medical schools have gained experience in such programs in automated instruction. Television images and graphic display combined with computer control and user interaction are effective for teaching problem solving. The National Board of Medical Examiners has developed patient-case simulation for examining clinical skills, and the National Library of Medicine has experimented with combining media. Advances from the field of artificial intelligence and the availability of increasingly powerful microcomputers at lower cost will aid further development. Computers will likely affect existing educational methods, adding new capabilities to laboratory exercises, to self-assessment and to continuing education. PMID:3544511

Using CamiTK for rapid prototyping of interactive computer assisted medical intervention applications.

PubMed

Promayon, Emmanuel; Fouard, Céline; Bailet, Mathieu; Deram, Aurélien; Fiard, Gaëlle; Hungr, Nikolai; Luboz, Vincent; Payan, Yohan; Sarrazin, Johan; Saubat, Nicolas; Selmi, Sonia Yuki; Voros, Sandrine; Cinquin, Philippe; Troccaz, Jocelyne

2013-01-01

Computer Assisted Medical Intervention (CAMI hereafter) is a complex multi-disciplinary field. CAMI research requires the collaboration of experts in several fields as diverse as medicine, computer science, mathematics, instrumentation, signal processing, mechanics, modeling, automatics, optics, etc. CamiTK is a modular framework that helps researchers and clinicians to collaborate together in order to prototype CAMI applications by regrouping the knowledge and expertise from each discipline. It is an open-source, cross-platform generic and modular tool written in C++ which can handle medical images, surgical navigation, biomedicals simulations and robot control. This paper presents the Computer Assisted Medical Intervention ToolKit (CamiTK) and how it is used in various applications in our research team.

Imaging and Analytics: The changing face of Medical Imaging

NASA Astrophysics Data System (ADS)

Foo, Thomas

There have been significant technological advances in imaging capability over the past 40 years. Medical imaging capabilities have developed rapidly, along with technology development in computational processing speed and miniaturization. Moving to all-digital, the number of images that are acquired in a routine clinical examination has increased dramatically from under 50 images in the early days of CT and MRI to more than 500-1000 images today. The staggering number of images that are routinely acquired poses significant challenges for clinicians to interpret the data and to correctly identify the clinical problem. Although the time provided to render a clinical finding has not substantially changed, the amount of data available for interpretation has grown exponentially. In addition, the image quality (spatial resolution) and information content (physiologically-dependent image contrast) has also increased significantly with advances in medical imaging technology. On its current trajectory, medical imaging in the traditional sense is unsustainable. To assist in filtering and extracting the most relevant data elements from medical imaging, image analytics will have a much larger role. Automated image segmentation, generation of parametric image maps, and clinical decision support tools will be needed and developed apace to allow the clinician to manage, extract and utilize only the information that will help improve diagnostic accuracy and sensitivity. As medical imaging devices continue to improve in spatial resolution, functional and anatomical information content, image/data analytics will be more ubiquitous and integral to medical imaging capability.

[Medical image compression: a review].

PubMed

Noreña, Tatiana; Romero, Eduardo

2013-01-01

Modern medicine is an increasingly complex activity , based on the evidence ; it consists of information from multiple sources : medical record text , sound recordings , images and videos generated by a large number of devices . Medical imaging is one of the most important sources of information since they offer comprehensive support of medical procedures for diagnosis and follow-up . However , the amount of information generated by image capturing gadgets quickly exceeds storage availability in radiology services , generating additional costs in devices with greater storage capacity . Besides , the current trend of developing applications in cloud computing has limitations, even though virtual storage is available from anywhere, connections are made through internet . In these scenarios the optimal use of information necessarily requires powerful compression algorithms adapted to medical activity needs . In this paper we present a review of compression techniques used for image storage , and a critical analysis of them from the point of view of their use in clinical settings.

Integrating DICOM structure reporting (SR) into the medical imaging informatics data grid

NASA Astrophysics Data System (ADS)

Lee, Jasper; Le, Anh; Liu, Brent

2008-03-01

The Medical Imaging Informatics (MI2) Data Grid developed at the USC Image Processing and Informatics Laboratory enables medical images to be shared securely between multiple imaging centers. Current applications include an imaging-based clinical trial setting where multiple field sites perform image acquisition and a centralized radiology core performs image analysis, often using computer-aided diagnosis tools (CAD) that generate a DICOM-SR to report their findings and measurements. As more and more CAD tools are being developed in the radiology field, the generated DICOM Structure Reports (SR) holding key radiological findings and measurements that are not part of the DICOM image need to be integrated into the existing Medical Imaging Informatics Data Grid with the corresponding imaging studies. We will discuss the significance and method involved in adapting DICOM-SR into the Medical Imaging Informatics Data Grid. The result is a MI2 Data Grid repository from which users can send and receive DICOM-SR objects based on the imaging-based clinical trial application. The services required to extract and categorize information from the structured reports will be discussed, and the workflow to store and retrieve a DICOM-SR file into the existing MI2 Data Grid will be shown.

Computer-assisted learning in anatomy at the international medical school in Debrecen, Hungary: a preliminary report.

PubMed

Kish, Gary; Cook, Samuel A; Kis, Gréta

2013-01-01

The University of Debrecen's Faculty of Medicine has an international, multilingual student population with anatomy courses taught in English to all but Hungarian students. An elective computer-assisted gross anatomy course, the Computer Human Anatomy (CHA), has been taught in English at the Anatomy Department since 2008. This course focuses on an introduction to anatomical digital images along with clinical cases. This low-budget course has a large visual component using images from magnetic resonance imaging and computer axial tomogram scans, ultrasound clinical studies, and readily available anatomy software that presents topics which run in parallel to the university's core anatomy curriculum. From the combined computer images and CHA lecture information, students are asked to solve computer-based clinical anatomy problems in the CHA computer laboratory. A statistical comparison was undertaken of core anatomy oral examination performances of English program first-year medical students who took the elective CHA course and those who did not in the three academic years 2007-2008, 2008-2009, and 2009-2010. The results of this study indicate that the CHA-enrolled students improved their performance on required anatomy core curriculum oral examinations (P < 0.001), suggesting that computer-assisted learning may play an active role in anatomy curriculum improvement. These preliminary results have prompted ongoing evaluation of what specific aspects of CHA are valuable and which students benefit from computer-assisted learning in a multilingual and diverse cultural environment. Copyright © 2012 American Association of Anatomists.

[Medical Image Registration Method Based on a Semantic Model with Directional Visual Words].

PubMed

Jin, Yufei; Ma, Meng; Yang, Xin

2016-04-01

Medical image registration is very challenging due to the various imaging modality,image quality,wide inter-patients variability,and intra-patient variability with disease progressing of medical images,with strict requirement for robustness.Inspired by semantic model,especially the recent tremendous progress in computer vision tasks under bag-of-visual-word framework,we set up a novel semantic model to match medical images.Since most of medical images have poor contrast,small dynamic range,and involving only intensities and so on,the traditional visual word models do not perform very well.To benefit from the advantages from the relative works,we proposed a novel visual word model named directional visual words,which performs better on medical images.Then we applied this model to do medical registration.In our experiment,the critical anatomical structures were first manually specified by experts.Then we adopted the directional visual word,the strategy of spatial pyramid searching from coarse to fine,and the k-means algorithm to help us locating the positions of the key structures accurately.Sequentially,we shall register corresponding images by the areas around these positions.The results of the experiments which were performed on real cardiac images showed that our method could achieve high registration accuracy in some specific areas.

The use of postmortem computed tomography in the diagnosis of intentional medication overdose.

PubMed

Burke, Michael P; O'Donnell, Chris; Bassed, Richard

2012-09-01

The recognition of a well defined basal layer of radio dense material on the postmortem computed tomography (CT) images, in the setting of typical scene findings of an intentional medication overdose and unremarkable external examination of the deceased's body can, in certain circumstances, permit such cases to be managed without routine full autopsy examination. Preliminary toxicological analysis can be targeted to such cases to provide further supportive evidence of intentional medication overdose. In cases where the scene findings are ambiguous or have been contaminated the postmortem CT images may alert the pathologist of the possibility of overdose in an otherwise apparently natural death. We reviewed 61 cases of documented intentional therapeutic medication overdose and 61 control cases. In the majority of the cases of confirmed intentional therapeutic medication overdose the CT images showed no diagnostic features. However, in many cases a well defined basal layer of radio-opaque material was clearly seen to line the gastric mucosa. The postmortem CT pattern which we believe to be highly suggestive of intentional medication overdose must be differentiated from other causes of increased radio density in the stomach which include CT artefacts.

Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.

PubMed

Bao, Shunxing; Damon, Stephen M; Landman, Bennett A; Gokhale, Aniruddha

2016-02-27

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical-Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for-use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline.

Performance management of high performance computing for medical image processing in Amazon Web Services

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Damon, Stephen M.; Landman, Bennett A.; Gokhale, Aniruddha

2016-03-01

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical- Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for- use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline.

Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services

PubMed Central

Bao, Shunxing; Damon, Stephen M.; Landman, Bennett A.; Gokhale, Aniruddha

2016-01-01

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical-Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for-use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline. PMID:27127335

Hyperbolic Harmonic Mapping for Surface Registration

PubMed Central

Shi, Rui; Zeng, Wei; Su, Zhengyu; Jiang, Jian; Damasio, Hanna; Lu, Zhonglin; Wang, Yalin; Yau, Shing-Tung; Gu, Xianfeng

2016-01-01

Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture inducstries. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquers this problem by changing the Riemannian metric on the target surface to a hyperbolic metric so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on Ricci flow and nonlinear heat diffusion methods. The approach is general and robust. We employ our algorithm to study the constrained surface registration problem which applies to both computer vision and medical imaging applications. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic and achieve relatively high performance when evaluated with some popular surface registration evaluation standards. PMID:27187948

Interactive tele-radiological segmentation systems for treatment and diagnosis.

PubMed

Zimeras, S; Gortzis, L G

2012-01-01

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

Grid-Enabled Quantitative Analysis of Breast Cancer

DTIC Science & Technology

2010-10-01

large-scale, multi-modality computerized image analysis . The central hypothesis of this research is that large-scale image analysis for breast cancer...research, we designed a pilot study utilizing large scale parallel Grid computing harnessing nationwide infrastructure for medical image analysis . Also

Cost-effective handling of digital medical images in the telemedicine environment.

PubMed

Choong, Miew Keen; Logeswaran, Rajasvaran; Bister, Michel

2007-09-01

This paper concentrates on strategies for less costly handling of medical images. Aspects of digitization using conventional digital cameras, lossy compression with good diagnostic quality, and visualization through less costly monitors are discussed. For digitization of film-based media, subjective evaluation of the suitability of digital cameras as an alternative to the digitizer was undertaken. To save on storage, bandwidth and transmission time, the acceptable degree of compression with diagnostically no loss of important data was studied through randomized double-blind tests of the subjective image quality when compression noise was kept lower than the inherent noise. A diagnostic experiment was undertaken to evaluate normal low cost computer monitors as viable viewing displays for clinicians. The results show that conventional digital camera images of X-ray images were diagnostically similar to the expensive digitizer. Lossy compression, when used moderately with the imaging noise to compression noise ratio (ICR) greater than four, can bring about image improvement with better diagnostic quality than the original image. Statistical analysis shows that there is no diagnostic difference between expensive high quality monitors and conventional computer monitors. The results presented show good potential in implementing the proposed strategies to promote widespread cost-effective telemedicine and digital medical environments. 2006 Elsevier Ireland Ltd

Automatic glaucoma diagnosis through medical imaging informatics.

PubMed

Liu, Jiang; Zhang, Zhuo; Wong, Damon Wing Kee; Xu, Yanwu; Yin, Fengshou; Cheng, Jun; Tan, Ngan Meng; Kwoh, Chee Keong; Xu, Dong; Tham, Yih Chung; Aung, Tin; Wong, Tien Yin

2013-01-01

Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.

Hiding Electronic Patient Record (EPR) in medical images: A high capacity and computationally efficient technique for e-healthcare applications.

PubMed

Loan, Nazir A; Parah, Shabir A; Sheikh, Javaid A; Akhoon, Jahangir A; Bhat, Ghulam M

2017-09-01

A high capacity and semi-reversible data hiding scheme based on Pixel Repetition Method (PRM) and hybrid edge detection for scalable medical images has been proposed in this paper. PRM has been used to scale up the small sized image (seed image) and hybrid edge detection ensures that no important edge information is missed. The scaled up version of seed image has been divided into 2×2 non overlapping blocks. In each block there is one seed pixel whose status decides the number of bits to be embedded in the remaining three pixels of that block. The Electronic Patient Record (EPR)/data have been embedded by using Least Significant and Intermediate Significant Bit Substitution (ISBS). The RC4 encryption has been used to add an additional security layer for embedded EPR/data. The proposed scheme has been tested for various medical and general images and compared with some state of art techniques in the field. The experimental results reveal that the proposed scheme besides being semi-reversible and computationally efficient is capable of handling high payload and as such can be used effectively for electronic healthcare applications. Copyright © 2017. Published by Elsevier Inc.

Segmenting Images for a Better Diagnosis

NASA Technical Reports Server (NTRS)

2004-01-01

NASA's Hierarchical Segmentation (HSEG) software has been adapted by Bartron Medical Imaging, LLC, for use in segmentation feature extraction, pattern recognition, and classification of medical images. Bartron acquired licenses from NASA Goddard Space Flight Center for application of the HSEG concept to medical imaging, from the California Institute of Technology/Jet Propulsion Laboratory to incorporate pattern-matching software, and from Kennedy Space Center for data-mining and edge-detection programs. The Med-Seg[TM] united developed by Bartron provides improved diagnoses for a wide range of medical images, including computed tomography scans, positron emission tomography scans, magnetic resonance imaging, ultrasound, digitized Z-ray, digitized mammography, dental X-ray, soft tissue analysis, and moving object analysis. It also can be used in analysis of soft-tissue slides. Bartron's future plans include the application of HSEG technology to drug development. NASA is advancing it's HSEG software to learn more about the Earth's magnetosphere.

A feasibility study of X-ray phase-contrast mammographic tomography at the Imaging and Medical beamline of the Australian Synchrotron.

PubMed

Nesterets, Yakov I; Gureyev, Timur E; Mayo, Sheridan C; Stevenson, Andrew W; Thompson, Darren; Brown, Jeremy M C; Kitchen, Marcus J; Pavlov, Konstantin M; Lockie, Darren; Brun, Francesco; Tromba, Giuliana

2015-11-01

Results are presented of a recent experiment at the Imaging and Medical beamline of the Australian Synchrotron intended to contribute to the implementation of low-dose high-sensitivity three-dimensional mammographic phase-contrast imaging, initially at synchrotrons and subsequently in hospitals and medical imaging clinics. The effect of such imaging parameters as X-ray energy, source size, detector resolution, sample-to-detector distance, scanning and data processing strategies in the case of propagation-based phase-contrast computed tomography (CT) have been tested, quantified, evaluated and optimized using a plastic phantom simulating relevant breast-tissue characteristics. Analysis of the data collected using a Hamamatsu CMOS Flat Panel Sensor, with a pixel size of 100 µm, revealed the presence of propagation-based phase contrast and demonstrated significant improvement of the quality of phase-contrast CT imaging compared with conventional (absorption-based) CT, at medically acceptable radiation doses.

ChRIS--A web-based neuroimaging and informatics system for collecting, organizing, processing, visualizing and sharing of medical data.

PubMed

Pienaar, Rudolph; Rannou, Nicolas; Bernal, Jorge; Hahn, Daniel; Grant, P Ellen

2015-01-01

The utility of web browsers for general purpose computing, long anticipated, is only now coming into fruition. In this paper we present a web-based medical image data and information management software platform called ChRIS ([Boston] Children's Research Integration System). ChRIS' deep functionality allows for easy retrieval of medical image data from resources typically found in hospitals, organizes and presents information in a modern feed-like interface, provides access to a growing library of plugins that process these data - typically on a connected High Performance Compute Cluster, allows for easy data sharing between users and instances of ChRIS and provides powerful 3D visualization and real time collaboration.

A Cloud Computing Based Patient Centric Medical Information System

NASA Astrophysics Data System (ADS)

Agarwal, Ankur; Henehan, Nathan; Somashekarappa, Vivek; Pandya, A. S.; Kalva, Hari; Furht, Borko

This chapter discusses an emerging concept of a cloud computing based Patient Centric Medical Information System framework that will allow various authorized users to securely access patient records from various Care Delivery Organizations (CDOs) such as hospitals, urgent care centers, doctors, laboratories, imaging centers among others, from any location. Such a system must seamlessly integrate all patient records including images such as CT-SCANS and MRI'S which can easily be accessed from any location and reviewed by any authorized user. In such a scenario the storage and transmission of medical records will have be conducted in a totally secure and safe environment with a very high standard of data integrity, protecting patient privacy and complying with all Health Insurance Portability and Accountability Act (HIPAA) regulations.

Communication in science.

PubMed

Deda, H; Yakupoglu, H

2002-01-01

Science must have a common language. For centuries, Latin language carried out this job, but the progress in computer technology and internet world through the last 20 years, began to produce a new language with the new century; the computer language. The information masses, which need data language standardization, are the followings; Digital libraries and medical education systems, Consumer health informatics, Medical education systems, World Wide Web Applications, Database systems, Medical language processing, Automatic indexing systems, Image processing units, Telemedicine, New Generation Internet (NGI).

Computers in imaging and health care: now and in the future.

PubMed

Arenson, R L; Andriole, K P; Avrin, D E; Gould, R G

2000-11-01

Early picture archiving and communication systems (PACS) were characterized by the use of very expensive hardware devices, cumbersome display stations, duplication of database content, lack of interfaces to other clinical information systems, and immaturity in their understanding of the folder manager concepts and workflow reengineering. They were implemented historically at large academic medical centers by biomedical engineers and imaging informaticists. PACS were nonstandard, home-grown projects with mixed clinical acceptance. However, they clearly showed the great potential for PACS and filmless medical imaging. Filmless radiology is a reality today. The advent of efficient softcopy display of images provides a means for dealing with the ever-increasing number of studies and number of images per study. Computer power has increased, and archival storage cost has decreased to the extent that the economics of PACS is justifiable with respect to film. Network bandwidths have increased to allow large studies of many megabytes to arrive at display stations within seconds of examination completion. PACS vendors have recognized the need for efficient workflow and have built systems with intelligence in the management of patient data. Close integration with the hospital information system (HIS)-radiology information system (RIS) is critical for system functionality. Successful implementation of PACS requires integration or interoperation with hospital and radiology information systems. Besides the economic advantages, secure rapid access to all clinical information on patients, including imaging studies, anytime and anywhere, enhances the quality of patient care, although it is difficult to quantify. Medical image management systems are maturing, providing access outside of the radiology department to images and clinical information throughout the hospital or the enterprise via the Internet. Small and medium-sized community hospitals, private practices, and outpatient centers in rural areas will begin realizing the benefits of PACS already realized by the large tertiary care academic medical centers and research institutions. Hand-held devices and the Worldwide Web are going to change the way people communicate and do business. The impact on health care will be huge, including radiology. Computer-aided diagnosis, decision support tools, virtual imaging, and guidance systems will transform our practice as value-added applications utilizing the technologies pushed by PACS development efforts. Outcomes data and the electronic medical record (EMR) will drive our interactions with referring physicians and we expect the radiologist to become the informaticist, a new version of the medical management consultant.

JPEG2000 still image coding quality.

PubMed

Chen, Tzong-Jer; Lin, Sheng-Chieh; Lin, You-Chen; Cheng, Ren-Gui; Lin, Li-Hui; Wu, Wei

2013-10-01

This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r > 0.98, p < 0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.

3D medical volume reconstruction using web services.

PubMed

Kooper, Rob; Shirk, Andrew; Lee, Sang-Chul; Lin, Amy; Folberg, Robert; Bajcsy, Peter

2008-04-01

We address the problem of 3D medical volume reconstruction using web services. The use of proposed web services is motivated by the fact that the problem of 3D medical volume reconstruction requires significant computer resources and human expertise in medical and computer science areas. Web services are implemented as an additional layer to a dataflow framework called data to knowledge. In the collaboration between UIC and NCSA, pre-processed input images at NCSA are made accessible to medical collaborators for registration. Every time UIC medical collaborators inspected images and selected corresponding features for registration, the web service at NCSA is contacted and the registration processing query is executed using the image to knowledge library of registration methods. Co-registered frames are returned for verification by medical collaborators in a new window. In this paper, we present 3D volume reconstruction problem requirements and the architecture of the developed prototype system at http://isda.ncsa.uiuc.edu/MedVolume. We also explain the tradeoffs of our system design and provide experimental data to support our system implementation. The prototype system has been used for multiple 3D volume reconstructions of blood vessels and vasculogenic mimicry patterns in histological sections of uveal melanoma studied by fluorescent confocal laser scanning microscope.

Stereolithography: a potential new tool in forensic medicine.

PubMed

Dolz, M S; Cina, S J; Smith, R

2000-06-01

Stereolithography is a computer-mediated method that can be used to quickly create anatomically correct three-dimensional epoxy and acrylic resin models from various types of medical data. Multiple imaging modalities can be exploited, including computed tomography and magnetic resonance imaging. The technology was first developed and used in 1986 to overcome limitations in previous computer-aided manufacturing/milling techniques. Stereolithography is presently used to accurately reproduce both the external and internal anatomy of body structures. Current medical uses of stereolithography include preoperative planning of orthopedic and maxillofacial surgeries, the fabrication of custom prosthetic devices; and the assessment of the degree of bony and soft-tissue injury caused by trauma. We propose that there is a useful, as yet untapped, potential for this technology in forensic medicine.

An automated distinction of DICOM images for lung cancer CAD system

NASA Astrophysics Data System (ADS)

Suzuki, H.; Saita, S.; Kubo, M.; Kawata, Y.; Niki, N.; Nishitani, H.; Ohmatsu, H.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2009-02-01

Automated distinction of medical images is an important preprocessing in Computer-Aided Diagnosis (CAD) systems. The CAD systems have been developed using medical image sets with specific scan conditions and body parts. However, varied examinations are performed in medical sites. The specification of the examination is contained into DICOM textual meta information. Most DICOM textual meta information can be considered reliable, however the body part information cannot always be considered reliable. In this paper, we describe an automated distinction of DICOM images as a preprocessing for lung cancer CAD system. Our approach uses DICOM textual meta information and low cost image processing. Firstly, the textual meta information such as scan conditions of DICOM image is distinguished. Secondly, the DICOM image is set to distinguish the body parts which are identified by image processing. The identification of body parts is based on anatomical structure which is represented by features of three regions, body tissue, bone, and air. The method is effective to the practical use of lung cancer CAD system in medical sites.

DICOM relay over the cloud.

PubMed

Silva, Luís A Bastião; Costa, Carlos; Oliveira, José Luis

2013-05-01

Healthcare institutions worldwide have adopted picture archiving and communication system (PACS) for enterprise access to images, relying on Digital Imaging Communication in Medicine (DICOM) standards for data exchange. However, communication over a wider domain of independent medical institutions is not well standardized. A DICOM-compliant bridge was developed for extending and sharing DICOM services across healthcare institutions without requiring complex network setups or dedicated communication channels. A set of DICOM routers interconnected through a public cloud infrastructure was implemented to support medical image exchange among institutions. Despite the advantages of cloud computing, new challenges were encountered regarding data privacy, particularly when medical data are transmitted over different domains. To address this issue, a solution was introduced by creating a ciphered data channel between the entities sharing DICOM services. Two main DICOM services were implemented in the bridge: Storage and Query/Retrieve. The performance measures demonstrated it is quite simple to exchange information and processes between several institutions. The solution can be integrated with any currently installed PACS-DICOM infrastructure. This method works transparently with well-known cloud service providers. Cloud computing was introduced to augment enterprise PACS by providing standard medical imaging services across different institutions, offering communication privacy and enabling creation of wider PACS scenarios with suitable technical solutions.

Distributed nuclear medicine applications using World Wide Web and Java technology.

PubMed

Knoll, P; Höll, K; Mirzaei, S; Koriska, K; Köhn, H

2000-01-01

At present, medical applications applying World Wide Web (WWW) technology are mainly used to view static images and to retrieve some information. The Java platform is a relative new way of computing, especially designed for network computing and distributed applications which enables interactive connection between user and information via the WWW. The Java 2 Software Development Kit (SDK) including Java2D API, Java Remote Method Invocation (RMI) technology, Object Serialization and the Java Advanced Imaging (JAI) extension was used to achieve a robust, platform independent and network centric solution. Medical image processing software based on this technology is presented and adequate performance capability of Java is demonstrated by an iterative reconstruction algorithm for single photon emission computerized tomography (SPECT).

Segmentation, surface rendering, and surface simplification of 3-D skull images for the repair of a large skull defect

NASA Astrophysics Data System (ADS)

Wan, Weibing; Shi, Pengfei; Li, Shuguang

2009-10-01

Given the potential demonstrated by research into bone-tissue engineering, the use of medical image data for the rapid prototyping (RP) of scaffolds is a subject worthy of research. Computer-aided design and manufacture and medical imaging have created new possibilities for RP. Accurate and efficient design and fabrication of anatomic models is critical to these applications. We explore the application of RP computational methods to the repair of a pediatric skull defect. The focus of this study is the segmentation of the defect region seen in computerized tomography (CT) slice images of this patient's skull and the three-dimensional (3-D) surface rendering of the patient's CT-scan data. We see if our segmentation and surface rendering software can improve the generation of an implant model to fill a skull defect.

Volume estimation of brain abnormalities in MRI data

NASA Astrophysics Data System (ADS)

Suprijadi, Pratama, S. H.; Haryanto, F.

2014-02-01

The abnormality of brain tissue always becomes a crucial issue in medical field. This medical condition can be recognized through segmentation of certain region from medical images obtained from MRI dataset. Image processing is one of computational methods which very helpful to analyze the MRI data. In this study, combination of segmentation and rendering image were used to isolate tumor and stroke. Two methods of thresholding were employed to segment the abnormality occurrence, followed by filtering to reduce non-abnormality area. Each MRI image is labeled and then used for volume estimations of tumor and stroke-attacked area. The algorithms are shown to be successful in isolating tumor and stroke in MRI images, based on thresholding parameter and stated detection accuracy.

Enabling outsourcing XDS for imaging on the public cloud.

PubMed

Ribeiro, Luís S; Rodrigues, Renato P; Costa, Carlos; Oliveira, José Luís

2013-01-01

Picture Archiving and Communication System (PACS) has been the main paradigm in supporting medical imaging workflows during the last decades. Despite its consolidation, the appearance of Cross-Enterprise Document Sharing for imaging (XDS-I), within IHE initiative, constitutes a great opportunity to readapt PACS workflow for inter-institutional data exchange. XDS-I provides a centralized discovery of medical imaging and associated reports. However, the centralized XDS-I actors (document registry and repository) must be deployed in a trustworthy node in order to safeguard patient privacy, data confidentiality and integrity. This paper presents XDS for Protected Imaging (XDS-p), a new approach to XDS-I that is capable of being outsourced (e.g. Cloud Computing) while maintaining privacy, confidentiality, integrity and legal concerns about patients' medical information.

Technical report on semiautomatic segmentation using the Adobe Photoshop.

PubMed

Park, Jin Seo; Chung, Min Suk; Hwang, Sung Bae; Lee, Yong Sook; Har, Dong-Hwan

2005-12-01

The purpose of this research is to enable users to semiautomatically segment the anatomical structures in magnetic resonance images (MRIs), computerized tomographs (CTs), and other medical images on a personal computer. The segmented images are used for making 3D images, which are helpful to medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was scanned to make 557 MRIs. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL and manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a similar manner, 13 anatomical structures in 8,590 anatomical images were segmented. Proper segmentation was verified by making 3D images from the segmented images. Semiautomatic segmentation using Adobe Photoshop is expected to be widely used for segmentation of anatomical structures in various medical images.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

PubMed Central

Hoo-Chang, Shin; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel

2016-01-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. PMID:26886976

Container-Based Clinical Solutions for Portable and Reproducible Image Analysis.

PubMed

Matelsky, Jordan; Kiar, Gregory; Johnson, Erik; Rivera, Corban; Toma, Michael; Gray-Roncal, William

2018-05-08

Medical imaging analysis depends on the reproducibility of complex computation. Linux containers enable the abstraction, installation, and configuration of environments so that software can be both distributed in self-contained images and used repeatably by tool consumers. While several initiatives in neuroimaging have adopted approaches for creating and sharing more reliable scientific methods and findings, Linux containers are not yet mainstream in clinical settings. We explore related technologies and their efficacy in this setting, highlight important shortcomings, demonstrate a simple use-case, and endorse the use of Linux containers for medical image analysis.

Challenges for data storage in medical imaging research.

PubMed

Langer, Steve G

2011-04-01

Researchers in medical imaging have multiple challenges for storing, indexing, maintaining viability, and sharing their data. Addressing all these concerns requires a constellation of tools, but not all of them need to be local to the site. In particular, the data storage challenges faced by researchers can begin to require professional information technology skills. With limited human resources and funds, the medical imaging researcher may be better served with an outsourcing strategy for some management aspects. This paper outlines an approach to manage the main objectives faced by medical imaging scientists whose work includes processing and data mining on non-standard file formats, and relating those files to the their DICOM standard descendents. The capacity of the approach scales as the researcher's need grows by leveraging the on-demand provisioning ability of cloud computing.

GPU-accelerated Lattice Boltzmann method for anatomical extraction in patient-specific computational hemodynamics

NASA Astrophysics Data System (ADS)

Yu, H.; Wang, Z.; Zhang, C.; Chen, N.; Zhao, Y.; Sawchuk, A. P.; Dalsing, M. C.; Teague, S. D.; Cheng, Y.

2014-11-01

Existing research of patient-specific computational hemodynamics (PSCH) heavily relies on software for anatomical extraction of blood arteries. Data reconstruction and mesh generation have to be done using existing commercial software due to the gap between medical image processing and CFD, which increases computation burden and introduces inaccuracy during data transformation thus limits the medical applications of PSCH. We use lattice Boltzmann method (LBM) to solve the level-set equation over an Eulerian distance field and implicitly and dynamically segment the artery surfaces from radiological CT/MRI imaging data. The segments seamlessly feed to the LBM based CFD computation of PSCH thus explicit mesh construction and extra data management are avoided. The LBM is ideally suited for GPU (graphic processing unit)-based parallel computing. The parallel acceleration over GPU achieves excellent performance in PSCH computation. An application study will be presented which segments an aortic artery from a chest CT dataset and models PSCH of the segmented artery.

A Scientific Workflow Platform for Generic and Scalable Object Recognition on Medical Images

NASA Astrophysics Data System (ADS)

Möller, Manuel; Tuot, Christopher; Sintek, Michael

In the research project THESEUS MEDICO we aim at a system combining medical image information with semantic background knowledge from ontologies to give clinicians fully cross-modal access to biomedical image repositories. Therefore joint efforts have to be made in more than one dimension: Object detection processes have to be specified in which an abstraction is performed starting from low-level image features across landmark detection utilizing abstract domain knowledge up to high-level object recognition. We propose a system based on a client-server extension of the scientific workflow platform Kepler that assists the collaboration of medical experts and computer scientists during development and parameter learning.

Software phantom with realistic speckle modeling for validation of image analysis methods in echocardiography

NASA Astrophysics Data System (ADS)

Law, Yuen C.; Tenbrinck, Daniel; Jiang, Xiaoyi; Kuhlen, Torsten

2014-03-01

Computer-assisted processing and interpretation of medical ultrasound images is one of the most challenging tasks within image analysis. Physical phenomena in ultrasonographic images, e.g., the characteristic speckle noise and shadowing effects, make the majority of standard methods from image analysis non optimal. Furthermore, validation of adapted computer vision methods proves to be difficult due to missing ground truth information. There is no widely accepted software phantom in the community and existing software phantoms are not exible enough to support the use of specific speckle models for different tissue types, e.g., muscle and fat tissue. In this work we propose an anatomical software phantom with a realistic speckle pattern simulation to _ll this gap and provide a exible tool for validation purposes in medical ultrasound image analysis. We discuss the generation of speckle patterns and perform statistical analysis of the simulated textures to obtain quantitative measures of the realism and accuracy regarding the resulting textures.

CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS.

PubMed

Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

2013-12-31

Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.

iMAGE cloud: medical image processing as a service for regional healthcare in a hybrid cloud environment.

PubMed

Liu, Li; Chen, Weiping; Nie, Min; Zhang, Fengjuan; Wang, Yu; He, Ailing; Wang, Xiaonan; Yan, Gen

2016-11-01

To handle the emergence of the regional healthcare ecosystem, physicians and surgeons in various departments and healthcare institutions must process medical images securely, conveniently, and efficiently, and must integrate them with electronic medical records (EMRs). In this manuscript, we propose a software as a service (SaaS) cloud called the iMAGE cloud. A three-layer hybrid cloud was created to provide medical image processing services in the smart city of Wuxi, China, in April 2015. In the first step, medical images and EMR data were received and integrated via the hybrid regional healthcare network. Then, traditional and advanced image processing functions were proposed and computed in a unified manner in the high-performance cloud units. Finally, the image processing results were delivered to regional users using the virtual desktop infrastructure (VDI) technology. Security infrastructure was also taken into consideration. Integrated information query and many advanced medical image processing functions-such as coronary extraction, pulmonary reconstruction, vascular extraction, intelligent detection of pulmonary nodules, image fusion, and 3D printing-were available to local physicians and surgeons in various departments and healthcare institutions. Implementation results indicate that the iMAGE cloud can provide convenient, efficient, compatible, and secure medical image processing services in regional healthcare networks. The iMAGE cloud has been proven to be valuable in applications in the regional healthcare system, and it could have a promising future in the healthcare system worldwide.

Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.

PubMed

Hu, Yu-Chi; Grossberg, Michael; Mageras, Gikas

2016-04-01

Volumetric medical images of a single subject can be acquired using different imaging modalities, such as computed tomography, magnetic resonance imaging (MRI), and positron emission tomography. In this work, we present a semiautomatic segmentation algorithm that can leverage the synergies between different image modalities while integrating interactive human guidance. The algorithm provides a statistical segmentation framework partly automating the segmentation task while still maintaining critical human oversight. The statistical models presented are trained interactively using simple brush strokes to indicate tumor and nontumor tissues and using intermediate results within a patient's image study. To accomplish the segmentation, we construct the energy function in the conditional random field (CRF) framework. For each slice, the energy function is set using the estimated probabilities from both user brush stroke data and prior approved segmented slices within a patient study. The progressive segmentation is obtained using a graph-cut-based minimization. Although no similar semiautomated algorithm is currently available, we evaluated our method with an MRI data set from Medical Image Computing and Computer Assisted Intervention Society multimodal brain segmentation challenge (BRATS 2012 and 2013) against a similar fully automatic method based on CRF and a semiautomatic method based on grow-cut, and our method shows superior performance.

Medical Imaging.

ERIC Educational Resources Information Center

Jaffe, C. Carl

1982-01-01

Describes principle imaging techniques, their applications, and their limitations in terms of diagnostic capability and possible adverse biological effects. Techniques include film radiography, computed tomography, nuclear medicine, positron emission tomography (PET), ultrasonography, nuclear magnetic resonance, and digital radiography. PET has…

A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval.

PubMed

Yang, Liu; Jin, Rong; Mummert, Lily; Sukthankar, Rahul; Goode, Adam; Zheng, Bin; Hoi, Steven C H; Satyanarayanan, Mahadev

2010-01-01

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.

Bedside imaging of intracranial hemorrhage in the neonate using light: comparison with ultrasound, computed tomography, and magnetic resonance imaging.

PubMed

Hintz, S R; Cheong, W F; van Houten, J P; Stevenson, D K; Benaron, D A

1999-01-01

Medical optical imaging (MOI) uses light emitted into opaque tissues to determine the interior structure. Previous reports detailed a portable time-of-flight and absorbance system emitting pulses of near infrared light into tissues and measuring the emerging light. Using this system, optical images of phantoms, whole rats, and pathologic neonatal brain specimens have been tomographically reconstructed. We have now modified the existing instrumentation into a clinically relevant headband-based system to be used for optical imaging of structure in the neonatal brain at the bedside. Eight medical optical imaging studies in the neonatal intensive care unit were performed in a blinded clinical comparison of optical images with ultrasound, computed tomography, and magnetic resonance imaging. Optical images were interpreted as correct in six of eight cases, with one error attributed to the age of the clot, and one small clot not seen. In addition, one disagreement with ultrasound, not reported as an error, was found to be the result of a mislabeled ultrasound report rather than because of an inaccurate optical scan. Optical scan correlated well with computed tomography and magnetic resonance imaging findings in one patient. We conclude that light-based imaging using a portable time-of-flight system is feasible and represents an important new noninvasive diagnostic technique, with potential for continuous monitoring of critically ill neonates at risk for intraventricular hemorrhage or stroke. Further studies are now underway to further investigate the functional imaging capabilities of this new diagnostic tool.

[Non-rigid medical image registration based on mutual information and thin-plate spline].

PubMed

Cao, Guo-gang; Luo, Li-min

2009-01-01

To get precise and complete details, the contrast in different images is needed in medical diagnosis and computer assisted treatment. The image registration is the basis of contrast, but the regular rigid registration does not satisfy the clinic requirements. A non-rigid medical image registration method based on mutual information and thin-plate spline was present. Firstly, registering two images globally based on mutual information; secondly, dividing reference image and global-registered image into blocks and registering them; then getting the thin-plate spline transformation according to the shift of blocks' center; finally, applying the transformation to the global-registered image. The results show that the method is more precise than the global rigid registration based on mutual information and it reduces the complexity of getting control points and satisfy the clinic requirements better by getting control points of the thin-plate transformation automatically.

GPU accelerated fuzzy connected image segmentation by using CUDA.

PubMed

Zhuge, Ying; Cao, Yong; Miller, Robert W

2009-01-01

Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.

WE-B-BRD-01: Innovation in Radiation Therapy Planning II: Cloud Computing in RT

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

Moore, K; Kagadis, G; Xing, L

As defined by the National Institute of Standards and Technology, cloud computing is “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” Despite the omnipresent role of computers in radiotherapy, cloud computing has yet to achieve widespread adoption in clinical or research applications, though the transition to such “on-demand” access is underway. As this transition proceeds, new opportunities for aggregate studies and efficient use of computational resources are set againstmore » new challenges in patient privacy protection, data integrity, and management of clinical informatics systems. In this Session, current and future applications of cloud computing and distributed computational resources will be discussed in the context of medical imaging, radiotherapy research, and clinical radiation oncology applications. Learning Objectives: Understand basic concepts of cloud computing. Understand how cloud computing could be used for medical imaging applications. Understand how cloud computing could be employed for radiotherapy research.4. Understand how clinical radiotherapy software applications would function in the cloud.« less

Detection of cyst using image segmentation and building knowledge-based intelligent decision support system as an aid to telemedicine

NASA Astrophysics Data System (ADS)

Janet, J.; Natesan, T. R.; Santhosh, Ramamurthy; Ibramsha, Mohideen

2005-02-01

An intelligent decision support tool to the Radiologist in telemedicine is described. Medical prescriptions are given based on the images of cyst that has been transmitted over computer networks to the remote medical center. The digital image, acquired by sonography, is converted into an intensity image. This image is then subjected to image preprocessing which involves correction methods to eliminate specific artifacts. The image is resized into a 256 x 256 matrix by using bilinear interpolation method. The background area is detected using distinct block operation. The area of the cyst is calculated by removing the background area from the original image. Boundary enhancement and morphological operations are done to remove unrelated pixels. This gives us the cyst volume. This segmented image of the cyst is sent to the remote medical center for analysis by Knowledge based artificial Intelligent Decision Support System (KIDSS). The type of cyst is detected and reported to the control mechanism of KIDSS. Then the inference engine compares this with the knowledge base and gives appropriate medical prescriptions or treatment recommendations by applying reasoning mechanisms at the remote medical center.

Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

NASA Astrophysics Data System (ADS)

Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi

2010-03-01

In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

Mpeg2 codec HD improvements with medical and robotic imaging benefits

NASA Astrophysics Data System (ADS)

Picard, Wayne F. J.

2010-02-01

In this report, we propose an efficient scheme to use High Definition Television (HDTV) in a console or notebook format as a computer terminal in addition to their role as TV display unit. In the proposed scheme, we assume that the main computer is situated at a remote location. The computer raster in the remote server is compressed using an HD E- >Mpeg2 encoder and transmitted to the terminal at home. The built-in E->Mpeg2 decoder in the terminal decompresses the compressed bit stream, and displays the raster. The terminal will be fitted with a mouse and keyboard, through which the interaction with the remote computer server can be performed via a communications back channel. The terminal in a notebook format can thus be used as a high resolution computer and multimedia device. We will consider developments such as the required HD enhanced Mpeg2 resolution (E->Mpeg2) and its medical ramifications due to improvements on compressed image quality with 2D to 3D conversion (Mpeg3) and using the compressed Discrete Cosine Transform coefficients in the reality compression of vision and control of medical robotic surgeons.

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

PubMed Central

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2–100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms. PMID:28487831

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

PubMed

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.

Virtual probing system for medical volume data

NASA Astrophysics Data System (ADS)

Xiao, Yongfei; Fu, Yili; Wang, Shuguo

2007-12-01

Because of the huge computation in 3D medical data visualization, looking into its inner data interactively is always a problem to be resolved. In this paper, we present a novel approach to explore 3D medical dataset in real time by utilizing a 3D widget to manipulate the scanning plane. With the help of the 3D texture property in modern graphics card, a virtual scanning probe is used to explore oblique clipping plane of medical volume data in real time. A 3D model of the medical dataset is also rendered to illustrate the relationship between the scanning-plane image and the other tissues in medical data. It will be a valuable tool in anatomy education and understanding of medical images in the medical research.

Real-time dynamic display of registered 4D cardiac MR and ultrasound images using a GPU

NASA Astrophysics Data System (ADS)

Zhang, Q.; Huang, X.; Eagleson, R.; Guiraudon, G.; Peters, T. M.

2007-03-01

In minimally invasive image-guided surgical interventions, different imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and real-time three-dimensional (3D) ultrasound (US), can provide complementary, multi-spectral image information. Multimodality dynamic image registration is a well-established approach that permits real-time diagnostic information to be enhanced by placing lower-quality real-time images within a high quality anatomical context. For the guidance of cardiac procedures, it would be valuable to register dynamic MRI or CT with intraoperative US. However, in practice, either the high computational cost prohibits such real-time visualization of volumetric multimodal images in a real-world medical environment, or else the resulting image quality is not satisfactory for accurate guidance during the intervention. Modern graphics processing units (GPUs) provide the programmability, parallelism and increased computational precision to begin to address this problem. In this work, we first outline our research on dynamic 3D cardiac MR and US image acquisition, real-time dual-modality registration and US tracking. Then we describe image processing and optimization techniques for 4D (3D + time) cardiac image real-time rendering. We also present our multimodality 4D medical image visualization engine, which directly runs on a GPU in real-time by exploiting the advantages of the graphics hardware. In addition, techniques such as multiple transfer functions for different imaging modalities, dynamic texture binding, advanced texture sampling and multimodality image compositing are employed to facilitate the real-time display and manipulation of the registered dual-modality dynamic 3D MR and US cardiac datasets.

Volumetric visualization algorithm development for an FPGA-based custom computing machine

NASA Astrophysics Data System (ADS)

Sallinen, Sami J.; Alakuijala, Jyrki; Helminen, Hannu; Laitinen, Joakim

1998-05-01

Rendering volumetric medical images is a burdensome computational task for contemporary computers due to the large size of the data sets. Custom designed reconfigurable hardware could considerably speed up volume visualization if an algorithm suitable for the platform is used. We present an algorithm and speedup techniques for visualizing volumetric medical CT and MR images with a custom-computing machine based on a Field Programmable Gate Array (FPGA). We also present simulated performance results of the proposed algorithm calculated with a software implementation running on a desktop PC. Our algorithm is capable of generating perspective projection renderings of single and multiple isosurfaces with transparency, simulated X-ray images, and Maximum Intensity Projections (MIP). Although more speedup techniques exist for parallel projection than for perspective projection, we have constrained ourselves to perspective viewing, because of its importance in the field of radiotherapy. The algorithm we have developed is based on ray casting, and the rendering is sped up by three different methods: shading speedup by gradient precalculation, a new generalized version of Ray-Acceleration by Distance Coding (RADC), and background ray elimination by speculative ray selection.

Computer-assisted virtual autopsy using surgical navigation techniques.

PubMed

Ebert, Lars Christian; Ruder, Thomas D; Martinez, Rosa Maria; Flach, Patricia M; Schweitzer, Wolf; Thali, Michael J; Ampanozi, Garyfalia

2015-01-01

OBJECTIVE; Virtual autopsy methods, such as postmortem CT and MRI, are increasingly being used in forensic medicine. Forensic investigators with little to no training in diagnostic radiology and medical laypeople such as state's attorneys often find it difficult to understand the anatomic orientation of axial postmortem CT images. We present a computer-assisted system that permits postmortem CT datasets to be quickly and intuitively resliced in real time at the body to narrow the gap between radiologic imaging and autopsy. Our system is a potentially valuable tool for planning autopsies, showing findings to medical laypeople, and teaching CT anatomy, thus further closing the gap between radiology and forensic pathology.

Tensor scale-based fuzzy connectedness image segmentation

NASA Astrophysics Data System (ADS)

Saha, Punam K.; Udupa, Jayaram K.

2003-05-01

Tangible solutions to image segmentation are vital in many medical imaging applications. Toward this goal, a framework based on fuzzy connectedness was developed in our laboratory. A fundamental notion called "affinity" - a local fuzzy hanging togetherness relation on voxels - determines the effectiveness of this segmentation framework in real applications. In this paper, we introduce the notion of "tensor scale" - a recently developed local morphometric parameter - in affinity definition and study its effectiveness. Although, our previous notion of "local scale" using the spherical model successfully incorporated local structure size into affinity and resulted in measureable improvements in segmentation results, a major limitation of the previous approach was that it ignored local structural orientation and anisotropy. The current approach of using tensor scale in affinity computation allows an effective utilization of local size, orientation, and ansiotropy in a unified manner. Tensor scale is used for computing both the homogeneity- and object-feature-based components of affinity. Preliminary results of the proposed method on several medical images and computer generated phantoms of realistic shapes are presented. Further extensions of this work are discussed.

A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

PubMed Central

Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

2013-01-01

Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K-means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications. PMID:24308014

Segmentation and Estimation of the Histological Composition of the Tumor Mass in Computed Tomographic Images of Neuroblastoma

DTIC Science & Technology

2001-10-25

a CT image, each voxel contains an integer number which is the CT value, in Hounsfield units (HU), of the voxel. Therefore, the standard method of...Task Number Work Unit Number Performing Organization Name(s) and Address(es) Department of Electrical and Computer Engineering, University of...34, Journal of Pediatric Surgery, vol 24(7), pp. 708-711, 1989. [4] I. N. Bankman, editor, Handbook of Medical Image Analysis, Academic Press, London, UK

3D-Printed Tissue-Mimicking Phantoms for Medical Imaging and Computational Validation Applications

PubMed Central

Shahmirzadi, Danial; Li, Ronny X.; Doyle, Barry J.; Konofagou, Elisa E.; McGloughlin, Tim M.

2014-01-01

Abstract Abdominal aortic aneurysm (AAA) is a permanent, irreversible dilation of the distal region of the aorta. Recent efforts have focused on improved AAA screening and biomechanics-based failure prediction. Idealized and patient-specific AAA phantoms are often employed to validate numerical models and imaging modalities. To produce such phantoms, the investment casting process is frequently used, reconstructing the 3D vessel geometry from computed tomography patient scans. In this study the alternative use of 3D printing to produce phantoms is investigated. The mechanical properties of flexible 3D-printed materials are benchmarked against proven elastomers. We demonstrate the utility of this process with particular application to the emerging imaging modality of ultrasound-based pulse wave imaging, a noninvasive diagnostic methodology being developed to obtain regional vascular wall stiffness properties, differentiating normal and pathologic tissue in vivo. Phantom wall displacements under pulsatile loading conditions were observed, showing good correlation to fluid–structure interaction simulations and regions of peak wall stress predicted by finite element analysis. 3D-printed phantoms show a strong potential to improve medical imaging and computational analysis, potentially helping bridge the gap between experimental and clinical diagnostic tools. PMID:28804733

Investigating existing medical CT segmentation techniques within automated baggage and package inspection

NASA Astrophysics Data System (ADS)

Megherbi, Najla; Breckon, Toby P.; Flitton, Greg T.

2013-10-01

3D Computed Tomography (CT) image segmentation is already well established tool in medical research and in routine daily clinical practice. However, such techniques have not been used in the context of 3D CT image segmentation for baggage and package security screening using CT imagery. CT systems are increasingly used in airports for security baggage examination. We propose in this contribution an investigation of the current 3D CT medical image segmentation methods for use in this new domain. Experimental results of 3D segmentation on real CT baggage security imagery using a range of techniques are presented and discussed.

Detection of immune-related adverse events by medical imaging in patients treated with anti-programmed cell death 1.

PubMed

Mekki, Ahmed; Dercle, Laurent; Lichtenstein, Philip; Marabelle, Aurélien; Michot, Jean-Marie; Lambotte, Olivier; Le Pavec, Jérôme; De Martin, Eleonora; Balleyguier, Corinne; Champiat, Stéphane; Ammari, Samy

2018-06-01

Programmed death receptor-1 blocking antibodies (anti-PD1) are a new standard of care in many cancer types. Patients benefit from improved survival but have the risk of immune-related adverse events (irAE). We evaluated if medical imaging procedures, used for anti-tumour response assessment, can detect irAEs. All consecutive patients treated with anti-PD1 and with a medical imaging acquisition performed within 2 weeks with irAEs ≥2 were retrospectively included. Data were gathered from June 2014 to February 2017, and a central review was performed. The primary and secondary end-points were i) to evaluate the overall detection rate of irAEs by medical imaging and ii) to provide a comprehensive radiological description of irAEs. Fifty-three patients (31 women, 22 men; average age: 61 years) were included. The primary tumour was melanoma (n = 32), lung cancer (n = 18) and other (n = 3). Patients were treated with nivolumab (n = 27) or pembrolizumab (n = 26). Of 74 medical imaging procedures analysed (ratio = 1.4 medical imaging per patient), 55 irAE were detected. The detection rate was overall: 74% (95 confidence interval: 63-84%), positron emission tomography with 18F-fludeoxyglucose integrated with computed tomography (18F-FDG PET/CT): 83% (n = 10/12), magnetic resonance imaging: 83% (n = 5/6), computed tomography scan: 79% (n = 19/24), ultrasonography: 70% (n = 19/27), standard X-rays: 40% (n = 2/5), lung/mediastinum: 100% (n = 7/7), enterocolitis: 100% (n = 8/8), hypophysitis: 100% (n = 3/3), thyroiditis: 75% (n = 15/20), hepatitis: 67% (n = 2/3), arthralgia or arthritis: 40% (n = 2/5) and pancreas: 28% (n = 2/7). Medical imaging detected 74% of irAE in patients treated with anti-PD1. Beyond response assessment, medical imaging can detect irAE and guide towards specific management. We described the most frequent sites and patterns of imaging findings. Copyright © 2018 Elsevier Ltd. All rights reserved.

What Is A Picture Archiving And Communication System (PACS)?

NASA Astrophysics Data System (ADS)

Marceau, Carla

1982-01-01

A PACS is a digital system for acquiring, storing, moving and displaying picture or image information. It is an alternative to film jackets that has been made possible by recent breakthroughs in computer technology: telecommunications, local area nets and optical disks. The fundamental concept of the digital representation of image information is introduced. It is shown that freeing images from a material representation on film or paper leads to a dramatic increase in flexibility in our use of the images. The ultimate goal of a medical PACS system is a radiology department without film jackets. The inherent nature of digital images and the power of the computer allow instant free "copies" of images to be made and thrown away. These copies can be transmitted to distant sites in seconds, without the "original" ever leaving the archives of the radiology department. The result is a radiology department with much freer access to patient images and greater protection against lost or misplaced image information. Finally, images in digital form can be treated as data for the computer in image processing, which includes enhancement, reconstruction and even computer-aided analysis.

Bridging the gap between basic and clinical sciences: A description of a radiological anatomy course.

PubMed

Torres, Anna; Staśkiewicz, Grzegorz J; Lisiecka, Justyna; Pietrzyk, Łukasz; Czekajlo, Michael; Arancibia, Carlos U; Maciejewski, Ryszard; Torres, Kamil

2016-05-06

A wide variety of medical imaging techniques pervade modern medicine, and the changing portability and performance of tools like ultrasound imaging have brought these medical imaging techniques into the everyday practice of many specialties outside of radiology. However, proper interpretation of ultrasonographic and computed tomographic images requires the practitioner to not only hone certain technical skills, but to command an excellent knowledge of sectional anatomy and an understanding of the pathophysiology of the examined areas as well. Yet throughout many medical curricula there is often a large gap between traditional anatomy coursework and clinical training in imaging techniques. The authors present a radiological anatomy course developed to teach sectional anatomy with particular emphasis on ultrasonography and computed tomography, while incorporating elements of medical simulation. To assess students' overall opinions about the course and to examine its impact on their self-perceived improvement in their knowledge of radiological anatomy, anonymous evaluation questionnaires were provided to the students. The questionnaires were prepared using standard survey methods. A five-point Likert scale was applied to evaluate agreement with statements regarding the learning experience. The majority of students considered the course very useful and beneficial in terms of improving three-dimensional and cross-sectional knowledge of anatomy, as well as for developing practical skills in ultrasonography and computed tomography. The authors found that a small-group, hands-on teaching model in radiological anatomy was perceived as useful both by the students and the clinical teachers involved in their clinical education. In addition, the model was introduced using relatively few resources and only two faculty members. Anat Sci Educ 9: 295-303. © 2015 American Association of Anatomists. © 2015 American Association of Anatomists.

PACS 2000: quality control using the task allocation chart

NASA Astrophysics Data System (ADS)

Norton, Gary S.; Romlein, John R.; Lyche, David K.; Richardson, Ronald R., Jr.

2000-05-01

Medical imaging's technological evolution in the next century will continue to include Picture Archive and Communication Systems (PACS) and teleradiology. It is difficult to predict radiology's future in the new millennium with both computed radiography and direct digital capture competing as the primary image acquisition methods for routine radiography. Changes in Computed Axial Tomography (CT) and Magnetic Resonance Imaging (MRI) continue to amaze the healthcare community. No matter how the acquisition, display, and archive functions change, Quality Control (QC) of the radiographic imaging chain will remain an important step in the imaging process. The Task Allocation Chart (TAC) is a tool that can be used in a medical facility's QC process to indicate the testing responsibilities of the image stakeholders and the medical informatics department. The TAC shows a grid of equipment to be serviced, tasks to be performed, and the organization assigned to perform each task. Additionally, skills, tasks, time, and references for each task can be provided. QC of the PACS must be stressed as a primary element of a PACS' implementation. The TAC can be used to clarify responsibilities during warranty and paid maintenance periods. Establishing a TAC a part of a PACS implementation has a positive affect on patient care and clinical acceptance.

Wireless live streaming video of laparoscopic surgery: a bandwidth analysis for handheld computers.

PubMed

Gandsas, Alex; McIntire, Katherine; George, Ivan M; Witzke, Wayne; Hoskins, James D; Park, Adrian

2002-01-01

Over the last six years, streaming media has emerged as a powerful tool for delivering multimedia content over networks. Concurrently, wireless technology has evolved, freeing users from desktop boundaries and wired infrastructures. At the University of Kentucky Medical Center, we have integrated these technologies to develop a system that can wirelessly transmit live surgery from the operating room to a handheld computer. This study establishes the feasibility of using our system to view surgeries and describes the effect of bandwidth on image quality. A live laparoscopic ventral hernia repair was transmitted to a single handheld computer using five encoding speeds at a constant frame rate, and the quality of the resulting streaming images was evaluated. No video images were rendered when video data were encoded at 28.8 kilobytes per second (Kbps), the slowest encoding bitrate studied. The highest quality images were rendered at encoding speeds greater than or equal to 150 Kbps. Of note, a 15 second transmission delay was experienced using all four encoding schemes that rendered video images. We believe that the wireless transmission of streaming video to handheld computers has tremendous potential to enhance surgical education. For medical students and residents, the ability to view live surgeries, lectures, courses and seminars on handheld computers means a larger number of learning opportunities. In addition, we envision that wireless enabled devices may be used to telemonitor surgical procedures. However, bandwidth availability and streaming delay are major issues that must be addressed before wireless telementoring becomes a reality.

The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading.

PubMed

Hainc, Nicolin; Federau, Christian; Stieltjes, Bram; Blatow, Maria; Bink, Andrea; Stippich, Christoph

2017-01-01

Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine.

Web-based system for surgical planning and simulation

NASA Astrophysics Data System (ADS)

Eldeib, Ayman M.; Ahmed, Mohamed N.; Farag, Aly A.; Sites, C. B.

1998-10-01

The growing scientific knowledge and rapid progress in medical imaging techniques has led to an increasing demand for better and more efficient methods of remote access to high-performance computer facilities. This paper introduces a web-based telemedicine project that provides interactive tools for surgical simulation and planning. The presented approach makes use of client-server architecture based on new internet technology where clients use an ordinary web browser to view, send, receive and manipulate patients' medical records while the server uses the supercomputer facility to generate online semi-automatic segmentation, 3D visualization, surgical simulation/planning and neuroendoscopic procedures navigation. The supercomputer (SGI ONYX 1000) is located at the Computer Vision and Image Processing Lab, University of Louisville, Kentucky. This system is under development in cooperation with the Department of Neurological Surgery, Alliant Health Systems, Louisville, Kentucky. The server is connected via a network to the Picture Archiving and Communication System at Alliant Health Systems through a DICOM standard interface that enables authorized clients to access patients' images from different medical modalities.

SemVisM: semantic visualizer for medical image

NASA Astrophysics Data System (ADS)

Landaeta, Luis; La Cruz, Alexandra; Baranya, Alexander; Vidal, María.-Esther

2015-01-01

SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically, combining rendering techniques, and segmentation algorithms. SemVisM comprises two main components: i) AMORE (A Modest vOlume REgister) to handle input data (RAW, DAT or DICOM) and to initially annotate the images using terms defined on medical ontologies (e.g., MesH, FMA or RadLex), and ii) VOLPROB (VOlume PRObability Builder) for generating the annotated volumetric data containing the classified voxels that belong to a particular tissue. SemVisM is built on top of the semantic visualizer ANISE.1

Assessment of Restoration Methods of X-Ray Images with Emphasis on Medical Photogrammetric Usage

NASA Astrophysics Data System (ADS)

Hosseinian, S.; Arefi, H.

2016-06-01

Nowadays, various medical X-ray imaging methods such as digital radiography, computed tomography and fluoroscopy are used as important tools in diagnostic and operative processes especially in the computer and robotic assisted surgeries. The procedures of extracting information from these images require appropriate deblurring and denoising processes on the pre- and intra-operative images in order to obtain more accurate information. This issue becomes more considerable when the X-ray images are planned to be employed in the photogrammetric processes for 3D reconstruction from multi-view X-ray images since, accurate data should be extracted from images for 3D modelling and the quality of X-ray images affects directly on the results of the algorithms. For restoration of X-ray images, it is essential to consider the nature and characteristics of these kinds of images. X-ray images exhibit severe quantum noise due to limited X-ray photons involved. The assumptions of Gaussian modelling are not appropriate for photon-limited images such as X-ray images, because of the nature of signal-dependant quantum noise. These images are generally modelled by Poisson distribution which is the most common model for low-intensity imaging. In this paper, existing methods are evaluated. For this purpose, after demonstrating the properties of medical X-ray images, the more efficient and recommended methods for restoration of X-ray images would be described and assessed. After explaining these approaches, they are implemented on samples from different kinds of X-ray images. By considering the results, it is concluded that using PURE-LET, provides more effective and efficient denoising than other examined methods in this research.

Discovering Hominins - Application of Medical Computed Tomography (CT) to Fossil-Bearing Rocks from the Site of Malapa, South Africa.

PubMed

Smilg, Jacqueline S; Berger, Lee R

2015-01-01

In the South African context, computed tomography (CT) has been used applied to individually prepared fossils and small rocks containing fossils, but has not been utilized on large breccia blocks as a means of discovering fossils, and particularly fossil hominins. Previous attempts at CT imaging of rocks from other South African sites for this purpose yielded disappointing results. For this study, 109 fossil- bearing rocks from the site of Malapa, South Africa were scanned with medical CT prior to manual preparation. The resultant images were assessed for accuracy of fossil identification and characterization against the standard of manual preparation. The accurate identification of fossils, including those of early hominins, that were not visible on the surface of individual blocks, is shown to be possible. The discovery of unexpected fossils is reduced, thus lowering the potential that fossils could be damaged through accidental encounter during routine preparation, or even entirely missed. This study should significantly change the way fossil discovery, recovery and preparation is done in the South African context and has potential for application in other palaeontological situations. Medical CT imaging is shown to be reliable, readily available, cost effective and accurate in finding fossils within matrix conglomerates. Improvements in CT equipment and in CT image quality are such that medical CT is now a viable imaging modality for this palaeontological application.

Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

NASA Astrophysics Data System (ADS)

Georgiou, Harris

2009-10-01

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

Discovering Hominins - Application of Medical Computed Tomography (CT) to Fossil-Bearing Rocks from the Site of Malapa, South Africa

PubMed Central

Smilg, Jacqueline S.; Berger, Lee R.

2015-01-01

In the South African context, computed tomography (CT) has been used applied to individually prepared fossils and small rocks containing fossils, but has not been utilized on large breccia blocks as a means of discovering fossils, and particularly fossil hominins. Previous attempts at CT imaging of rocks from other South African sites for this purpose yielded disappointing results. For this study, 109 fossil- bearing rocks from the site of Malapa, South Africa were scanned with medical CT prior to manual preparation. The resultant images were assessed for accuracy of fossil identification and characterization against the standard of manual preparation. The accurate identification of fossils, including those of early hominins, that were not visible on the surface of individual blocks, is shown to be possible. The discovery of unexpected fossils is reduced, thus lowering the potential that fossils could be damaged through accidental encounter during routine preparation, or even entirely missed. This study should significantly change the way fossil discovery, recovery and preparation is done in the South African context and has potential for application in other palaeontological situations. Medical CT imaging is shown to be reliable, readily available, cost effective and accurate in finding fossils within matrix conglomerates. Improvements in CT equipment and in CT image quality are such that medical CT is now a viable imaging modality for this palaeontological application. PMID:26684299

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2007-03-01

Multislice CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multislice CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. Moreover, we have provided diagnostic assistance methods to medical screening specialists by using a lung cancer screening algorithm built into mobile helical CT scanner for the lung cancer mass screening done in the region without the hospital. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

Your brain on drugs: imaging of drug-related changes in the central nervous system.

PubMed

Tamrazi, Benita; Almast, Jeevak

2012-01-01

Drug abuse is a substantial problem in society today and is associated with significant morbidity and mortality. Various drugs are associated with serious complications affecting the brain, and it is critical to recognize the imaging findings of these complications to provide prompt medical management. The central nervous system (CNS) is a target organ for drugs of abuse as well as specific prescribed medications. Drugs of abuse affecting the CNS include cocaine, heroin, alcohol, amphetamines, toluene, and cannabis. Prescribed medications or medical therapies that can affect the CNS include immunosuppressants, antiepileptics, nitrous oxide, and total parenteral nutrition. The CNS complications of these drugs include neurovascular complications, encephalopathy, atrophy, infection, changes in the corpus callosum, and other miscellaneous changes. Imaging abnormalities indicative of these complications can be appreciated at both magnetic resonance (MR) imaging and computed tomography (CT). It is critical for radiologists to recognize complications related to drugs of abuse as well as iatrogenic effects of various medications. Therefore, diagnostic imaging modalities such as MR imaging and CT can play a pivotal role in the recognition and timely management of drug-related complications in the CNS.

Fast semivariogram computation using FPGA architectures

NASA Astrophysics Data System (ADS)

Lagadapati, Yamuna; Shirvaikar, Mukul; Dong, Xuanliang

2015-02-01

The semivariogram is a statistical measure of the spatial distribution of data and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. The semivariogram is a plot of semivariances for different lag distances between pixels. A semi-variance, γ(h), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h. Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O(n2). Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz, but they can perform tens of thousands of calculations per clock cycle while operating in the low range of power. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. The design consists of several modules dedicated to the constituent computational tasks. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. Anisotropic semivariogram implementation is anticipated to be an extension of the current architecture, ostensibly based on refinements to the current modules. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from MRI scans are utilized for the experiments. Computational speedup is measured with respect to Matlab implementation on a personal computer with an Intel i7 multi-core processor. Preliminary simulation results indicate that a significant advantage in speed can be attained by the architectures, making the algorithm viable for implementation in medical devices

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

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

Medical Image Compression Using a New Subband Coding Method

NASA Technical Reports Server (NTRS)

Kossentini, Faouzi; Smith, Mark J. T.; Scales, Allen; Tucker, Doug

1995-01-01

A recently introduced iterative complexity- and entropy-constrained subband quantization design algorithm is generalized and applied to medical image compression. In particular, the corresponding subband coder is used to encode Computed Tomography (CT) axial slice head images, where statistical dependencies between neighboring image subbands are exploited. Inter-slice conditioning is also employed for further improvements in compression performance. The subband coder features many advantages such as relatively low complexity and operation over a very wide range of bit rates. Experimental results demonstrate that the performance of the new subband coder is relatively good, both objectively and subjectively.

Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

2016-03-01

Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate this approach,using a publicly available head and neck CT dataset. We also show that a deep neural network of similar depth, if trained directly using backpropagation, cannot acheive the tasks achieved using our layer wise training paradigm.

Computational high-resolution optical imaging of the living human retina

NASA Astrophysics Data System (ADS)

Shemonski, Nathan D.; South, Fredrick A.; Liu, Yuan-Zhi; Adie, Steven G.; Scott Carney, P.; Boppart, Stephen A.

2015-07-01

High-resolution in vivo imaging is of great importance for the fields of biology and medicine. The introduction of hardware-based adaptive optics (HAO) has pushed the limits of optical imaging, enabling high-resolution near diffraction-limited imaging of previously unresolvable structures. In ophthalmology, when combined with optical coherence tomography, HAO has enabled a detailed three-dimensional visualization of photoreceptor distributions and individual nerve fibre bundles in the living human retina. However, the introduction of HAO hardware and supporting software adds considerable complexity and cost to an imaging system, limiting the number of researchers and medical professionals who could benefit from the technology. Here we demonstrate a fully automated computational approach that enables high-resolution in vivo ophthalmic imaging without the need for HAO. The results demonstrate that computational methods in coherent microscopy are applicable in highly dynamic living systems.

Spectrum of tablet computer use by medical students and residents at an academic medical center.

PubMed

Robinson, Robert

2015-01-01

Introduction. The value of tablet computer use in medical education is an area of considerable interest, with preliminary investigations showing that the majority of medical trainees feel that tablet computers added value to the curriculum. This study investigated potential differences in tablet computer use between medical students and resident physicians. Materials & Methods. Data collection for this survey was accomplished with an anonymous online questionnaire shared with the medical students and residents at Southern Illinois University School of Medicine (SIU-SOM) in July and August of 2012. Results. There were 76 medical student responses (26% response rate) and 66 resident/fellow responses to this survey (21% response rate). Residents/fellows were more likely to use tablet computers several times daily than medical students (32% vs. 20%, p = 0.035). The most common reported uses were for accessing medical reference applications (46%), e-Books (45%), and board study (32%). Residents were more likely than students to use a tablet computer to access an electronic medical record (41% vs. 21%, p = 0.010), review radiology images (27% vs. 12%, p = 0.019), and enter patient care orders (26% vs. 3%, p < 0.001). Discussion. This study shows a high prevalence and frequency of tablet computer use among physicians in training at this academic medical center. Most residents and students use tablet computers to access medical references, e-Books, and to study for board exams. Residents were more likely to use tablet computers to complete clinical tasks. Conclusions. Tablet computer use among medical students and resident physicians was common in this survey. All learners used tablet computers for point of care references and board study. Resident physicians were more likely to use tablet computers to access the EMR, enter patient care orders, and review radiology studies. This difference is likely due to the differing educational and professional demands placed on resident physicians. Further study is needed better understand how tablet computers and other mobile devices may assist in medical education and patient care.

Spectrum of tablet computer use by medical students and residents at an academic medical center

PubMed Central

2015-01-01

Introduction. The value of tablet computer use in medical education is an area of considerable interest, with preliminary investigations showing that the majority of medical trainees feel that tablet computers added value to the curriculum. This study investigated potential differences in tablet computer use between medical students and resident physicians. Materials & Methods. Data collection for this survey was accomplished with an anonymous online questionnaire shared with the medical students and residents at Southern Illinois University School of Medicine (SIU-SOM) in July and August of 2012. Results. There were 76 medical student responses (26% response rate) and 66 resident/fellow responses to this survey (21% response rate). Residents/fellows were more likely to use tablet computers several times daily than medical students (32% vs. 20%, p = 0.035). The most common reported uses were for accessing medical reference applications (46%), e-Books (45%), and board study (32%). Residents were more likely than students to use a tablet computer to access an electronic medical record (41% vs. 21%, p = 0.010), review radiology images (27% vs. 12%, p = 0.019), and enter patient care orders (26% vs. 3%, p < 0.001). Discussion. This study shows a high prevalence and frequency of tablet computer use among physicians in training at this academic medical center. Most residents and students use tablet computers to access medical references, e-Books, and to study for board exams. Residents were more likely to use tablet computers to complete clinical tasks. Conclusions. Tablet computer use among medical students and resident physicians was common in this survey. All learners used tablet computers for point of care references and board study. Resident physicians were more likely to use tablet computers to access the EMR, enter patient care orders, and review radiology studies. This difference is likely due to the differing educational and professional demands placed on resident physicians. Further study is needed better understand how tablet computers and other mobile devices may assist in medical education and patient care. PMID:26246973

High-resolution PET [Positron Emission Tomography] for Medical Science Studies

DOE R&D Accomplishments Database

Budinger, T. F.; Derenzo, S. E.; Huesman, R. H.; Jagust, W. J.; Valk, P. E.

1989-09-01

One of the unexpected fruits of basic physics research and the computer revolution is the noninvasive imaging power available to today's physician. Technologies that were strictly the province of research scientists only a decade or two ago now serve as the foundations for such standard diagnostic tools as x-ray computer tomography (CT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), ultrasound, single photon emission computed tomography (SPECT), and positron emission tomography (PET). Furthermore, prompted by the needs of both the practicing physician and the clinical researcher, efforts to improve these technologies continue. This booklet endeavors to describe the advantages of achieving high resolution in PET imaging.

Image Registration for Stability Testing of MEMS

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; LeMoigne, Jacqueline; Blake, Peter N.; Morey, Peter A.; Landsman, Wayne B.; Chambers, Victor J.; Moseley, Samuel H.

2011-01-01

Image registration, or alignment of two or more images covering the same scenes or objects, is of great interest in many disciplines such as remote sensing, medical imaging. astronomy, and computer vision. In this paper, we introduce a new application of image registration algorithms. We demonstrate how through a wavelet based image registration algorithm, engineers can evaluate stability of Micro-Electro-Mechanical Systems (MEMS). In particular, we applied image registration algorithms to assess alignment stability of the MicroShutters Subsystem (MSS) of the Near Infrared Spectrograph (NIRSpec) instrument of the James Webb Space Telescope (JWST). This work introduces a new methodology for evaluating stability of MEMS devices to engineers as well as a new application of image registration algorithms to computer scientists.

[Research on Three-dimensional Medical Image Reconstruction and Interaction Based on HTML5 and Visualization Toolkit].

PubMed

Gao, Peng; Liu, Peng; Su, Hongsen; Qiao, Liang

2015-04-01

Integrating visualization toolkit and the capability of interaction, bidirectional communication and graphics rendering which provided by HTML5, we explored and experimented on the feasibility of remote medical image reconstruction and interaction in pure Web. We prompted server-centric method which did not need to download the big medical data to local connections and avoided considering network transmission pressure and the three-dimensional (3D) rendering capability of client hardware. The method integrated remote medical image reconstruction and interaction into Web seamlessly, which was applicable to lower-end computers and mobile devices. Finally, we tested this method in the Internet and achieved real-time effects. This Web-based 3D reconstruction and interaction method, which crosses over internet terminals and performance limited devices, may be useful for remote medical assistant.

Cerebral toxoplasmosis combined with disseminated tuberculosis.

PubMed

Hwang, Eui Ho; Ahn, Poong Gi; Lee, Dong Min; Kim, Hyeok Su

2012-05-01

A 24-year-old man presented with mental change, fever, abdominal pain, tenderness and palpable mass on the lower abdomen. He was a non-Korean engineer and did not accompany a legal guardian, so medical history taking was difficult due to his mental status. Brain magnetic resonance imaging showed multiple rim-enhanced lesions of the brain, and abdominal computed tomography showed huge paraspinal abscess. Chest X-ray and computed tomography showed poorly defined nodular opacities. We initially thought that this patient was infected with toxoplasmosis with typical cerebral image finding and immunoglobulin laboratory finding of cerebrospinal fluid and serum study. The abdominal abscess was confirmed as tuberculosis through the pathologic finding of caseous necrosis. We used anti-tuberculosis medication and anti-toxoplasmosis medication for almost 4 months, and then his clinical state and radiological findings were considerably improved.

Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram.

PubMed

Jung, Younhyun; Kim, Jinman; Kumar, Ashnil; Feng, David Dagan; Fulham, Michael

2016-07-01

'Visibility' is a fundamental optical property that represents the observable, by users, proportion of the voxels in a volume during interactive volume rendering. The manipulation of this 'visibility' improves the volume rendering processes; for instance by ensuring the visibility of regions of interest (ROIs) or by guiding the identification of an optimal rendering view-point. The construction of visibility histograms (VHs), which represent the distribution of all the visibility of all voxels in the rendered volume, enables users to explore the volume with real-time feedback about occlusion patterns among spatially related structures during volume rendering manipulations. Volume rendered medical images have been a primary beneficiary of VH given the need to ensure that specific ROIs are visible relative to the surrounding structures, e.g. the visualisation of tumours that may otherwise be occluded by neighbouring structures. VH construction and its subsequent manipulations, however, are computationally expensive due to the histogram binning of the visibilities. This limits the real-time application of VH to medical images that have large intensity ranges and volume dimensions and require a large number of histogram bins. In this study, we introduce an efficient adaptive binned visibility histogram (AB-VH) in which a smaller number of histogram bins are used to represent the visibility distribution of the full VH. We adaptively bin medical images by using a cluster analysis algorithm that groups the voxels according to their intensity similarities into a smaller subset of bins while preserving the distribution of the intensity range of the original images. We increase efficiency by exploiting the parallel computation and multiple render targets (MRT) extension of the modern graphical processing units (GPUs) and this enables efficient computation of the histogram. We show the application of our method to single-modality computed tomography (CT), magnetic resonance (MR) imaging and multi-modality positron emission tomography-CT (PET-CT). In our experiments, the AB-VH markedly improved the computational efficiency for the VH construction and thus improved the subsequent VH-driven volume manipulations. This efficiency was achieved without major degradation in the VH visually and numerical differences between the AB-VH and its full-bin counterpart. We applied several variants of the K-means clustering algorithm with varying Ks (the number of clusters) and found that higher values of K resulted in better performance at a lower computational gain. The AB-VH also had an improved performance when compared to the conventional method of down-sampling of the histogram bins (equal binning) for volume rendering visualisation. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Physics of Physical Examinations.

ERIC Educational Resources Information Center

Patterson, James D.

1989-01-01

Discussed are several topics on medical imaging including x-rays and Computer Assisted Tomography (CAT) scans, magnetic resonance imaging, fiber optics endoscopy, nuclear medicine and bone scans, positron-emission tomography, and ultrasound. The concepts of radiation dosage, electrocardiograms, and laser therapy are included. (YP)

TRIIG - Time-lapse reproduction of images through interactive graphics. [digital processing of quality hard copy

NASA Technical Reports Server (NTRS)

Buckner, J. D.; Council, H. W.; Edwards, T. R.

1974-01-01

Description of the hardware and software implementing the system of time-lapse reproduction of images through interactive graphics (TRIIG). The system produces a quality hard copy of processed images in a fast and inexpensive manner. This capability allows for optimal development of processing software through the rapid viewing of many image frames in an interactive mode. Three critical optical devices are used to reproduce an image: an Optronics photo reader/writer, the Adage Graphics Terminal, and Polaroid Type 57 high speed film. Typical sources of digitized images are observation satellites, such as ERTS or Mariner, computer coupled electron microscopes for high-magnification studies, or computer coupled X-ray devices for medical research.

Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications.

PubMed

Parks, Connie L; Monson, Keith L

2017-04-01

The recognizability of facial images extracted from publically available medical scans raises patient privacy concerns. This study examined how accurately facial images extracted from computed tomography (CT) scans are objectively matched with corresponding photographs of the scanned individuals. The test subjects were 128 adult Americans ranging in age from 18 to 60 years, representing both sexes and three self-identified population (ancestral descent) groups (African, European, and Hispanic). Using facial recognition software, the 2D images of the extracted facial models were compared for matches against five differently sized photo galleries. Depending on the scanning protocol and gallery size, in 6-61 % of the cases, a correct life photo match for a CT-derived facial image was the top ranked image in the generated candidate lists, even when blind searching in excess of 100,000 images. In 31-91 % of the cases, a correct match was located within the top 50 images. Few significant differences (p > 0.05) in match rates were observed between the sexes or across the three age cohorts. Highly significant differences (p < 0.01) were, however, observed across the three ancestral cohorts and between the two CT scanning protocols. Results suggest that the probability of a match between a facial image extracted from a medical scan and a photograph of the individual is moderately high. The facial image data inherent in commonly employed medical imaging modalities may need to consider a potentially identifiable form of "comparable" facial imagery and protected as such under patient privacy legislation.

Waterborne Pathogens: The Protozoans.

PubMed

Moss, Joseph Anthony

2016-09-01

Waterborne diseases associated with polluted recreational and potable waters have been documented for more than a century. Key microbial protozoan parasites, such as Cryptosporidium and Giardia, are causative agents for gastrointestinal disease worldwide. Although not a first-line diagnostic approach for these diseases, medical imaging, such as radiography, computed tomography, magnetic resonance imaging, ultrasonography, and nuclear medicine technologies, can be used to evaluate patients with long-term effects. This article describes protozoan pathogens that affect human health, treatment of common waterborne pathogen-related diseases, and associated medical imaging. ©2016 American Society of Radiologic Technologists.

77 FR 21574 - Prospective Grant of Exclusive License: Method for Segmenting Medical Images and Detecting...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-10

... methods help solve imaging problems such as image ``leakage,'' which causes distortion, overloads datasets... enhance detection. This is helpful to identify harmful features such as precancerous polyps or other anomalies. The field of use may be limited to ``computer aided detection in colonography.'' The prospective...

Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device.

PubMed

Holub, Joseph; Winer, Eliot

2017-12-01

Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7" iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework.

Adapting smartphones for low-cost optical medical imaging

NASA Astrophysics Data System (ADS)

Pratavieira, Sebastião.; Vollet-Filho, José D.; Carbinatto, Fernanda M.; Blanco, Kate; Inada, Natalia M.; Bagnato, Vanderlei S.; Kurachi, Cristina

2015-06-01

Optical images have been used in several medical situations to improve diagnosis of lesions or to monitor treatments. However, most systems employ expensive scientific (CCD or CMOS) cameras and need computers to display and save the images, usually resulting in a high final cost for the system. Additionally, this sort of apparatus operation usually becomes more complex, requiring more and more specialized technical knowledge from the operator. Currently, the number of people using smartphone-like devices with built-in high quality cameras is increasing, which might allow using such devices as an efficient, lower cost, portable imaging system for medical applications. Thus, we aim to develop methods of adaptation of those devices to optical medical imaging techniques, such as fluorescence. Particularly, smartphones covers were adapted to connect a smartphone-like device to widefield fluorescence imaging systems. These systems were used to detect lesions in different tissues, such as cervix and mouth/throat mucosa, and to monitor ALA-induced protoporphyrin-IX formation for photodynamic treatment of Cervical Intraepithelial Neoplasia. This approach may contribute significantly to low-cost, portable and simple clinical optical imaging collection.

[Application of medical imaging to general thoracic surgery].

PubMed

Oizumi, Hiroyuki

2014-07-01

Medical imaging technology is rapidly progressing. Positron emission tomography (PET) has played major role in the staging and choice of treatment modality in lung cancer patients. Magnetic resonance imaging (MRI) is now routinely used for mediastinal tumors and the use of diffusion-weighted images (DWI) may help in the diagnosis of malignancies including lung cancers. The benefits of medical imaging technology are not limited to diagnostics, and include simulation or navigation for complex lung resection and other procedures. Multidetector row computed tomography (MDCT) shortens imaging time to obtain detailed and precise volume data, which improves diagnosis of small-sized lung cancers. 3-dimensional reconstruction of the volume data allows the safe performance of thoracoscopic surgery. For lung lobectomy, identification of the branching structures, diameter, and length of the arteries is useful in selecting the procedure for blood vessel treatment. For lung segmentectomy, visualization of venous branches in the affected segments and intersegmental veins has facilitated the preoperative determination of the anatomical intersegmental plane. Therefore, the application of medical imaging technology is useful in general thoracic surgery.

Implementation of a low-cost mobile devices to support medical diagnosis.

PubMed

García Sánchez, Carlos; Botella Juan, Guillermo; Ayuso Márquez, Fermín; González Rodríguez, Diego; Prieto-Matías, Manuel; Tirado Fernández, Francisco

2013-01-01

Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.

Implementation of a Low-Cost Mobile Devices to Support Medical Diagnosis

PubMed Central

García Sánchez, Carlos; Botella Juan, Guillermo; Ayuso Márquez, Fermín; González Rodríguez, Diego; Prieto-Matías, Manuel; Tirado Fernández, Francisco

2013-01-01

Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics. PMID:24489600

The caBIG annotation and image Markup project.

PubMed

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

2010-04-01

Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM.

ELPIDA: a general architecture for medical imaging systems supporting telemedicine applications

NASA Astrophysics Data System (ADS)

Lymberopoulos, Dimitris C.; Spiropoulos, Kostas V.; Anastassopoulos, George C.; Kotsopoulos, Stavros A.; Solomou, Katerina G.

1995-01-01

During the next years, profound changes are expected in computer and communication technologies that will offer the medical imaging systems (MIS) industry a challenge to develop advanced telemedicine applications of high performance. Medical industry, vendors, and specialists need to agree on a universal MIS structure that will provide a stack of functions, protocols, and interfaces suitable for coordination and management of high-level image consults, reports, and review activities. Doctors and engineers have worked together to determine the types, targets, and range of such activities within a medical group working domain and to posit their impact on MIS structure. As a result, the fundamental MIS functions have been posed and organized in the form of a general MIS architecture, denoted as ELPIDA. The structure of this architecture was kept as simple as possible to allow its extension to diverse multimode operational schemes handling medical and conversational audiovisual information of different classes. The fundamentals of ELPIDA and pulmonary image diagnostic aspects have been employed for the development of a prototype MIS.

Comparing the Microsoft Kinect to a traditional mouse for adjusting the viewed tissue densities of three-dimensional anatomical structures

NASA Astrophysics Data System (ADS)

Juhnke, Bethany; Berron, Monica; Philip, Adriana; Williams, Jordan; Holub, Joseph; Winer, Eliot

2013-03-01

Advancements in medical image visualization in recent years have enabled three-dimensional (3D) medical images to be volume-rendered from magnetic resonance imaging (MRI) and computed tomography (CT) scans. Medical data is crucial for patient diagnosis and medical education, and analyzing these three-dimensional models rather than two-dimensional (2D) slices would enable more efficient analysis by surgeons and physicians, especially non-radiologists. An interaction device that is intuitive, robust, and easily learned is necessary to integrate 3D modeling software into the medical community. The keyboard and mouse configuration does not readily manipulate 3D models because these traditional interface devices function within two degrees of freedom, not the six degrees of freedom presented in three dimensions. Using a familiar, commercial-off-the-shelf (COTS) device for interaction would minimize training time and enable maximum usability with 3D medical images. Multiple techniques are available to manipulate 3D medical images and provide doctors more innovative ways of visualizing patient data. One such example is windowing. Windowing is used to adjust the viewed tissue density of digital medical data. A software platform available at the Virtual Reality Applications Center (VRAC), named Isis, was used to visualize and interact with the 3D representations of medical data. In this paper, we present the methodology and results of a user study that examined the usability of windowing 3D medical imaging using a Kinect™ device compared to a traditional mouse.

Imaging-related medications: a class overview

PubMed Central

2007-01-01

Imaging-related medications (contrast agents) are commonly utilized to improve visualization of radiographic, computed tomography (CT), and magnetic resonance (MR) images. While traditional medications are used specifically for their pharmacological actions, the ideal imaging agent provides enhanced contrast with little biological interaction. The radiopaque agents, barium sulfate and iodinated contrast agents, confer “contrast” to x-ray films by their physical ability to directly absorb x-rays. Gadolinium-based MR agents enhance visualization of tissues when exposed to a magnetic field. Ferrous-ferric oxide–based paramagnetic agents provide negative contrast for MR liver studies. This article provides an overview of clinically relevant information for the imaging-related medications commonly in use. It reviews the safety improvements in new generations of drugs; risk factors and precautions for the reduction of severe adverse reactions (i.e., extravasation, contrast-induced nephropathy, metformin-induced lactic acidosis, and nephrogenic fibrosing dermopathy/nephrogenic systemic fibrosis); and the significance of diligent patient screening before contrast exposure and appropriate monitoring after exposure. PMID:17948119

Computer Assisted Multi-Center Creation of Medical Knowledge Bases

PubMed Central

Giuse, Nunzia Bettinsoli; Giuse, Dario A.; Miller, Randolph A.

1988-01-01

Computer programs which support different aspects of medical care have been developed in recent years. Their capabilities range from diagnosis to medical imaging, and include hospital management systems and therapy prescription. In spite of their diversity these systems have one commonality: their reliance on a large body of medical knowledge in computer-readable form. This knowledge enables such programs to draw inferences, validate hypotheses, and in general to perform their intended task. As has been clear to developers of such systems, however, the creation and maintenance of medical knowledge bases are very expensive. Practical and economical difficulties encountered during this long-term process have discouraged most attempts. This paper discusses knowledge base creation and maintenance, with special emphasis on medical applications. We first describe the methods currently used and their limitations. We then present our recent work on developing tools and methodologies which will assist in the process of creating a medical knowledge base. We focus, in particular, on the possibility of multi-center creation of the knowledge base.

Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm.

PubMed

Liu, Xiaozheng; Yuan, Zhenming; Zhu, Junming; Xu, Dongrong

2013-12-07

The demons algorithm is a popular algorithm for non-rigid image registration because of its computational efficiency and simple implementation. The deformation forces of the classic demons algorithm were derived from image gradients by considering the deformation to decrease the intensity dissimilarity between images. However, the methods using the difference of image intensity for medical image registration are easily affected by image artifacts, such as image noise, non-uniform imaging and partial volume effects. The gradient magnitude image is constructed from the local information of an image, so the difference in a gradient magnitude image can be regarded as more reliable and robust for these artifacts. Then, registering medical images by considering the differences in both image intensity and gradient magnitude is a straightforward selection. In this paper, based on a diffeomorphic demons algorithm, we propose a chain-type diffeomorphic demons algorithm by combining the differences in both image intensity and gradient magnitude for medical image registration. Previous work had shown that the classic demons algorithm can be considered as an approximation of a second order gradient descent on the sum of the squared intensity differences. By optimizing the new dissimilarity criteria, we also present a set of new demons forces which were derived from the gradients of the image and gradient magnitude image. We show that, in controlled experiments, this advantage is confirmed, and yields a fast convergence.

A comparative study of deep learning models for medical image classification

NASA Astrophysics Data System (ADS)

Dutta, Suvajit; Manideep, B. C. S.; Rai, Shalva; Vijayarajan, V.

2017-11-01

Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities. Neurosciences has already exploited DL techniques, thus portrayed itself as an inspirational source for researchers exploring the domain of Machine learning. DL enthusiasts cover the areas of vision, speech recognition, motion planning and NLP as well, moving back and forth among fields. This concerns with building models that can successfully solve variety of tasks requiring intelligence and distributed representation. The accessibility to faster CPUs, introduction of GPUs-performing complex vector and matrix computations, supported agile connectivity to network. Enhanced software infrastructures for distributed computing worked in strengthening the thought that made researchers suffice DL methodologies. The paper emphases on the following DL procedures to traditional approaches which are performed manually for classifying medical images. The medical images are used for the study Diabetic Retinopathy(DR) and computed tomography (CT) emphysema data. Both DR and CT data diagnosis is difficult task for normal image classification methods. The initial work was carried out with basic image processing along with K-means clustering for identification of image severity levels. After determining image severity levels ANN has been applied on the data to get the basic classification result, then it is compared with the result of DNNs (Deep Neural Networks), which performed efficiently because of its multiple hidden layer features basically which increases accuracy factors, but the problem of vanishing gradient in DNNs made to consider Convolution Neural Networks (CNNs) as well for better results. The CNNs are found to be providing better outcomes when compared to other learning models aimed at classification of images. CNNs are favoured as they provide better visual processing models successfully classifying the noisy data as well. The work centres on the detection on Diabetic Retinopathy-loss in vision and recognition of computed tomography (CT) emphysema data measuring the severity levels for both cases. The paper discovers how various Machine Learning algorithms can be implemented ensuing a supervised approach, so as to get accurate results with less complexity possible.

Histopathological Image Analysis: A Review

PubMed Central

Gurcan, Metin N.; Boucheron, Laura; Can, Ali; Madabhushi, Anant; Rajpoot, Nasir; Yener, Bulent

2010-01-01

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement to the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe. PMID:20671804

Design of point-of-care (POC) microfluidic medical diagnostic devices

NASA Astrophysics Data System (ADS)

Leary, James F.

2018-02-01

Design of inexpensive and portable hand-held microfluidic flow/image cytometry devices for initial medical diagnostics at the point of initial patient contact by emergency medical personnel in the field requires careful design in terms of power/weight requirements to allow for realistic portability as a hand-held, point-of-care medical diagnostics device. True portability also requires small micro-pumps for high-throughput capability. Weight/power requirements dictate use of super-bright LEDs and very small silicon photodiodes or nanophotonic sensors that can be powered by batteries. Signal-to-noise characteristics can be greatly improved by appropriately pulsing the LED excitation sources and sampling and subtracting noise in between excitation pulses. The requirements for basic computing, imaging, GPS and basic telecommunications can be simultaneously met by use of smartphone technologies, which become part of the overall device. Software for a user-interface system, limited real-time computing, real-time imaging, and offline data analysis can be accomplished through multi-platform software development systems that are well-suited to a variety of currently available cellphone technologies which already contain all of these capabilities. Microfluidic cytometry requires judicious use of small sample volumes and appropriate statistical sampling by microfluidic cytometry or imaging for adequate statistical significance to permit real-time (typically < 15 minutes) medical decisions for patients at the physician's office or real-time decision making in the field. One or two drops of blood obtained by pin-prick should be able to provide statistically meaningful results for use in making real-time medical decisions without the need for blood fractionation, which is not realistic in the field.

From Roentgen to magnetic resonance imaging: the history of medical imaging.

PubMed

Scatliff, James H; Morris, Peter J

2014-01-01

Medical imaging has advanced in remarkable ways since the discovery of x-rays 120 years ago. Today's radiologists can image the human body in intricate detail using computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and various other modalities. Such technology allows for improved screening, diagnosis, and monitoring of disease, but it also comes with risks. Many imaging modalities expose patients to ionizing radiation, which potentially increases their risk of developing cancer in the future, and imaging may also be associated with possible allergic reactions or risks related to the use of intravenous contrast agents. In addition, the financial costs of imaging are taxing our health care system, and incidental findings can trigger anxiety and further testing. This issue of the NCMJ addresses the pros and cons of medical imaging and discusses in detail the following uses of medical imaging: screening for breast cancer with mammography, screening for osteoporosis and monitoring of bone mineral density with dual-energy x-ray absorptiometry, screening for congenital hip dysplasia in infants with ultrasound, and evaluation of various heart conditions with cardiac imaging. Together, these articles show the challenges that must be met as we seek to harness the power of today's imaging technologies, as well as the potential benefits that can be achieved when these hurdles are overcome.

e-Science platform for translational biomedical imaging research: running, statistics, and analysis

NASA Astrophysics Data System (ADS)

Wang, Tusheng; Yang, Yuanyuan; Zhang, Kai; Wang, Mingqing; Zhao, Jun; Xu, Lisa; Zhang, Jianguo

2015-03-01

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 and presented this work in SPIE Medical Imaging conference held in San Diego in 2012. In past the two-years, we implemented a biomedical image chain including communication, storage, cooperation and computing based on this e-Science platform. In this presentation, we presented the operating status of this system in supporting biomedical imaging research, analyzed and discussed results of this system in supporting multi-disciplines collaboration cross-multiple institutions.

PDE based scheme for multi-modal medical image watermarking.

PubMed

Aherrahrou, N; Tairi, H

2015-11-25

This work deals with copyright protection of digital images, an issue that needs protection of intellectual property rights. It is an important issue with a large number of medical images interchanged on the Internet every day. So, it is a challenging task to ensure the integrity of received images as well as authenticity. Digital watermarking techniques have been proposed as valid solution for this problem. It is worth mentioning that the Region Of Interest (ROI)/Region Of Non Interest (RONI) selection can be seen as a significant limitation from which suffers most of ROI/RONI based watermarking schemes and that in turn affects and limit their applicability in an effective way. Generally, the ROI/RONI is defined by a radiologist or a computer-aided selection tool. And thus, this will not be efficient for an institute or health care system, where one has to process a large number of images. Therefore, developing an automatic ROI/RONI selection is a challenge task. The major aim of this work is to develop an automatic selection algorithm of embedding region based on the so called Partial Differential Equation (PDE) method. Thus avoiding ROI/RONI selection problems including: (1) computational overhead, (2) time consuming, and (3) modality dependent selection. The algorithm is evaluated in terms of imperceptibility, robustness, tamper localization and recovery using MRI, Ultrasound, CT and X-ray grey scale medical images. From experimental results that we have conducted on a database of 100 medical images of four modalities, it can be inferred that our method can achieve high imperceptibility, while showing good robustness against attacks. Furthermore, the experiment results confirm the effectiveness of the proposed algorithm in detecting and recovering the various types of tampering. The highest PSNR value reached over the 100 images is 94,746 dB, while the lowest PSNR value is 60,1272 dB, which demonstrates the higher imperceptibility nature of the proposed method. Moreover, the Normalized Correlation (NC) between the original watermark and the corresponding extracted watermark for 100 images is computed. We get a NC value greater than or equal to 0.998. This indicates that the extracted watermark is very similar to the original watermark for all modalities. The key features of our proposed method are to (1) increase the robustness of the watermark against attacks; (2) provide more transparency to the embedded watermark. (3) provide more authenticity and integrity protection of the content of medical images. (4) provide minimum ROI/RONI selection complexity.

Interactive radiographic image retrieval system.

PubMed

Kundu, Malay Kumar; Chowdhury, Manish; Das, Sudeb

2017-02-01

Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the "semantic gap" problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Fast ray-tracing of human eye optics on Graphics Processing Units.

PubMed

Wei, Qi; Patkar, Saket; Pai, Dinesh K

2014-05-01

We present a new technique for simulating retinal image formation by tracing a large number of rays from objects in three dimensions as they pass through the optic apparatus of the eye to objects. Simulating human optics is useful for understanding basic questions of vision science and for studying vision defects and their corrections. Because of the complexity of computing such simulations accurately, most previous efforts used simplified analytical models of the normal eye. This makes them less effective in modeling vision disorders associated with abnormal shapes of the ocular structures which are hard to be precisely represented by analytical surfaces. We have developed a computer simulator that can simulate ocular structures of arbitrary shapes, for instance represented by polygon meshes. Topographic and geometric measurements of the cornea, lens, and retina from keratometer or medical imaging data can be integrated for individualized examination. We utilize parallel processing using modern Graphics Processing Units (GPUs) to efficiently compute retinal images by tracing millions of rays. A stable retinal image can be generated within minutes. We simulated depth-of-field, accommodation, chromatic aberrations, as well as astigmatism and correction. We also show application of the technique in patient specific vision correction by incorporating geometric models of the orbit reconstructed from clinical medical images. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A new approach of building 3D visualization framework for multimodal medical images display and computed assisted diagnosis

NASA Astrophysics Data System (ADS)

Li, Zhenwei; Sun, Jianyong; Zhang, Jianguo

2012-02-01

As more and more CT/MR studies are scanning with larger volume of data sets, more and more radiologists and clinician would like using PACS WS to display and manipulate these larger data sets of images with 3D rendering features. In this paper, we proposed a design method and implantation strategy to develop 3D image display component not only with normal 3D display functions but also with multi-modal medical image fusion as well as compute-assisted diagnosis of coronary heart diseases. The 3D component has been integrated into the PACS display workstation of Shanghai Huadong Hospital, and the clinical practice showed that it is easy for radiologists and physicians to use these 3D functions such as multi-modalities' (e.g. CT, MRI, PET, SPECT) visualization, registration and fusion, and the lesion quantitative measurements. The users were satisfying with the rendering speeds and quality of 3D reconstruction. The advantages of the component include low requirements for computer hardware, easy integration, reliable performance and comfortable application experience. With this system, the radiologists and the clinicians can manipulate with 3D images easily, and use the advanced visualization tools to facilitate their work with a PACS display workstation at any time.

Manifold learning of brain MRIs by deep learning.

PubMed

Brosch, Tom; Tam, Roger

2013-01-01

Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with pplications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are (1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 x 128 x 128 practical, and (2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.

Addressing the coming radiology crisis-the Society for Computer Applications in Radiology transforming the radiological interpretation process (TRIP) initiative.

PubMed

Andriole, Katherine P; Morin, Richard L; Arenson, Ronald L; Carrino, John A; Erickson, Bradley J; Horii, Steven C; Piraino, David W; Reiner, Bruce I; Seibert, J Anthony; Siegel, Eliot

2004-12-01

The Society for Computer Applications in Radiology (SCAR) Transforming the Radiological Interpretation Process (TRIP) Initiative aims to spearhead research, education, and discovery of innovative solutions to address the problem of information and image data overload. The initiative will foster interdisciplinary research on technological, environmental and human factors to better manage and exploit the massive amounts of data. TRIP will focus on the following basic objectives: improving the efficiency of interpretation of large data sets, improving the timeliness and effectiveness of communication, and decreasing medical errors. The ultimate goal of the initiative is to improve the quality and safety of patient care. Interdisciplinary research into several broad areas will be necessary to make progress in managing the ever-increasing volume of data. The six concepts involved are human perception, image processing and computer-aided detection (CAD), visualization, navigation and usability, databases and integration, and evaluation and validation of methods and performance. The result of this transformation will affect several key processes in radiology, including image interpretation; communication of imaging results; workflow and efficiency within the health care enterprise; diagnostic accuracy and a reduction in medical errors; and, ultimately, the overall quality of care.

NVIDIA OptiX ray-tracing engine as a new tool for modelling medical imaging systems

NASA Astrophysics Data System (ADS)

Pietrzak, Jakub; Kacperski, Krzysztof; Cieślar, Marek

2015-03-01

The most accurate technique to model the X- and gamma radiation path through a numerically defined object is the Monte Carlo simulation which follows single photons according to their interaction probabilities. A simplified and much faster approach, which just integrates total interaction probabilities along selected paths, is known as ray tracing. Both techniques are used in medical imaging for simulating real imaging systems and as projectors required in iterative tomographic reconstruction algorithms. These approaches are ready for massive parallel implementation e.g. on Graphics Processing Units (GPU), which can greatly accelerate the computation time at a relatively low cost. In this paper we describe the application of the NVIDIA OptiX ray-tracing engine, popular in professional graphics and rendering applications, as a new powerful tool for X- and gamma ray-tracing in medical imaging. It allows the implementation of a variety of physical interactions of rays with pixel-, mesh- or nurbs-based objects, and recording any required quantities, like path integrals, interaction sites, deposited energies, and others. Using the OptiX engine we have implemented a code for rapid Monte Carlo simulations of Single Photon Emission Computed Tomography (SPECT) imaging, as well as the ray-tracing projector, which can be used in reconstruction algorithms. The engine generates efficient, scalable and optimized GPU code, ready to run on multi GPU heterogeneous systems. We have compared the results our simulations with the GATE package. With the OptiX engine the computation time of a Monte Carlo simulation can be reduced from days to minutes.

Population-based imaging biobanks as source of big data.

PubMed

Gatidis, Sergios; Heber, Sophia D; Storz, Corinna; Bamberg, Fabian

2017-06-01

Advances of computational sciences over the last decades have enabled the introduction of novel methodological approaches in biomedical research. Acquiring extensive and comprehensive data about a research subject and subsequently extracting significant information has opened new possibilities in gaining insight into biological and medical processes. This so-called big data approach has recently found entrance into medical imaging and numerous epidemiological studies have been implementing advanced imaging to identify imaging biomarkers that provide information about physiological processes, including normal development and aging but also on the development of pathological disease states. The purpose of this article is to present existing epidemiological imaging studies and to discuss opportunities, methodological and organizational aspects, and challenges that population imaging poses to the field of big data research.

Content-based image retrieval by matching hierarchical attributed region adjacency graphs

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Thies, Christian J.; Guld, Mark O.; Lehmann, Thomas M.

2004-05-01

Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical images. Additionally, it has been shown that global features characterizing the whole image do not lead to acceptable results in the medical context or that they are only suitable for specific applications. For a general purpose content-based comparison of medical images, local, i.e. regional features that are collected on multiple scales must be used. A hierarchical attributed region adjacency graph (HARAG) provides such a representation and transfers image comparison to graph matching. However, building a HARAG from an image requires a restriction in size to be computationally feasible while at the same time all visually plausible information must be preserved. For this purpose, mechanisms for the reduction of the graph size are presented. Even with a reduced graph, the problem of graph matching remains NP-complete. In this paper, the Similarity Flooding approach and Hopfield-style neural networks are adapted from the graph matching community to the needs of HARAG comparison. Based on synthetic image material build from simple geometric objects, all visually similar regions were matched accordingly showing the framework's general applicability to content-based image retrieval of medical images.

Investigating Architectural Issues in Neuromorphic Computing

DTIC Science & Technology

2009-06-01

An example of this is Diffusion Tensor Imaging ( DTI ), a variant of fMRI, which detects water diffusion. DTI is routinely applied at medical...model computed for a subfield positioned over a section of the silhouette dog’s hind leg . The illustrated angles roughly correspond to orientation

Automated flight path planning for virtual endoscopy.

PubMed

Paik, D S; Beaulieu, C F; Jeffrey, R B; Rubin, G D; Napel, S

1998-05-01

In this paper, a novel technique for rapid and automatic computation of flight paths for guiding virtual endoscopic exploration of three-dimensional medical images is described. While manually planning flight paths is a tedious and time consuming task, our algorithm is automated and fast. Our method for positioning the virtual camera is based on the medial axis transform but is much more computationally efficient. By iteratively correcting a path toward the medial axis, the necessity of evaluating simple point criteria during morphological thinning is eliminated. The virtual camera is also oriented in a stable viewing direction, avoiding sudden twists and turns. We tested our algorithm on volumetric data sets of eight colons, one aorta and one bronchial tree. The algorithm computed the flight paths in several minutes per volume on an inexpensive workstation with minimal computation time added for multiple paths through branching structures (10%-13% per extra path). The results of our algorithm are smooth, centralized paths that aid in the task of navigation in virtual endoscopic exploration of three-dimensional medical images.

Microarthroscopy System With Image Processing Technology Developed for Minimally Invasive Surgery

NASA Technical Reports Server (NTRS)

Steele, Gynelle C.

2001-01-01

In a joint effort, NASA, Micro Medical Devices, and the Cleveland Clinic have developed a microarthroscopy system with digital image processing. This system consists of a disposable endoscope the size of a needle that is aimed at expanding the use of minimally invasive surgery on the knee, ankle, and other small joints. This device not only allows surgeons to make smaller incisions (by improving the clarity and brightness of images), but it gives them a better view of the injured area to make more accurate diagnoses. Because of its small size, the endoscope helps reduce physical trauma and speeds patient recovery. The faster recovery rate also makes the system cost effective for patients. The digital image processing software used with the device was originally developed by the NASA Glenn Research Center to conduct computer simulations of satellite positioning in space. It was later modified to reflect lessons learned in enhancing photographic images in support of the Center's microgravity program. Glenn's Photovoltaic Branch and Graphics and Visualization Lab (G-VIS) computer programmers and software developers enhanced and speed up graphic imaging for this application. Mary Vickerman at Glenn developed algorithms that enabled Micro Medical Devices to eliminate interference and improve the images.

Local gray level S-curve transformation - A generalized contrast enhancement technique for medical images.

PubMed

Gandhamal, Akash; Talbar, Sanjay; Gajre, Suhas; Hani, Ahmad Fadzil M; Kumar, Dileep

2017-04-01

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Medical computer-aided detection method based on deep learning].

PubMed

Tao, Pan; Fu, Zhongliang; Zhu, Kai; Wang, Lili

2018-03-01

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

Image-guided tissue engineering of anatomically shaped implants via MRI and micro-CT using injection molding.

PubMed

Ballyns, Jeffery J; Gleghorn, Jason P; Niebrzydowski, Vicki; Rawlinson, Jeremy J; Potter, Hollis G; Maher, Suzanne A; Wright, Timothy M; Bonassar, Lawrence J

2008-07-01

This study demonstrates for the first time the development of engineered tissues based on anatomic geometries derived from widely used medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). Computer-aided design and tissue injection molding techniques have demonstrated the ability to generate living implants of complex geometry. Due to its complex geometry, the meniscus of the knee was used as an example of this technique's capabilities. MRI and microcomputed tomography (microCT) were used to design custom-printed molds that enabled the generation of anatomically shaped constructs that retained shape throughout 8 weeks of culture. Engineered constructs showed progressive tissue formation indicated by increases in extracellular matrix content and mechanical properties. The paradigm of interfacing tissue injection molding technology can be applied to other medical imaging techniques that render 3D models of anatomy, demonstrating the potential to apply the current technique to engineering of many tissues and organs.

High-accuracy and real-time 3D positioning, tracking system for medical imaging applications based on 3D digital image correlation

NASA Astrophysics Data System (ADS)

Xue, Yuan; Cheng, Teng; Xu, Xiaohai; Gao, Zeren; Li, Qianqian; Liu, Xiaojing; Wang, Xing; Song, Rui; Ju, Xiangyang; Zhang, Qingchuan

2017-01-01

This paper presents a system for positioning markers and tracking the pose of a rigid object with 6 degrees of freedom in real-time using 3D digital image correlation, with two examples for medical imaging applications. Traditional DIC method was improved to meet the requirements of the real-time by simplifying the computations of integral pixel search. Experiments were carried out and the results indicated that the new method improved the computational efficiency by about 4-10 times in comparison with the traditional DIC method. The system was aimed for orthognathic surgery navigation in order to track the maxilla segment after LeFort I osteotomy. Experiments showed noise for the static point was at the level of 10-3 mm and the measurement accuracy was 0.009 mm. The system was demonstrated on skin surface shape evaluation of a hand for finger stretching exercises, which indicated a great potential on tracking muscle and skin movements.

Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy

NASA Astrophysics Data System (ADS)

Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli

2014-03-01

One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. For this purpose, we previously developed a software platform for high-performance 3D medical image processing, called HPC 3D-MIP platform, which employs increasingly available and affordable commodity computing systems such as the multicore, cluster, and cloud computing systems. To achieve scalable high-performance computing, the platform employed size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D-MIP algorithms, supported task scheduling for efficient load distribution and balancing, and consisted of a layered parallel software libraries that allow image processing applications to share the common functionalities. We evaluated the performance of the HPC 3D-MIP platform by applying it to computationally intensive processes in virtual colonoscopy. Experimental results showed a 12-fold performance improvement on a workstation with 12-core CPUs over the original sequential implementation of the processes, indicating the efficiency of the platform. Analysis of performance scalability based on the Amdahl's law for symmetric multicore chips showed the potential of a high performance scalability of the HPC 3DMIP platform when a larger number of cores is available.

A medical ontology for intelligent web-based skin lesions image retrieval.

PubMed

Maragoudakis, Manolis; Maglogiannis, Ilias

2011-06-01

Researchers have applied increasing efforts towards providing formal computational frameworks to consolidate the plethora of concepts and relations used in the medical domain. In the domain of skin related diseases, the variability of semantic features contained within digital skin images is a major barrier to the medical understanding of the symptoms and development of early skin cancers. The desideratum of making these standards machine-readable has led to their formalization in ontologies. In this work, in an attempt to enhance an existing Core Ontology for skin lesion images, hand-coded from image features, high quality images were analyzed by an autonomous ontology creation engine. We show that by exploiting agglomerative clustering methods with distance criteria upon the existing ontological structure, the original domain model could be enhanced with new instances, attributes and even relations, thus allowing for better classification and retrieval of skin lesion categories from the web.

Radiation Risk From Medical Imaging

PubMed Central

Lin, Eugene C.

2010-01-01

This review provides a practical overview of the excess cancer risks related to radiation from medical imaging. Primary care physicians should have a basic understanding of these risks. Because of recent attention to this issue, patients are more likely to express concerns over radiation risk. In addition, physicians can play a role in reducing radiation risk to their patients by considering these risks when making imaging referrals. This review provides a brief overview of the evidence pertaining to low-level radiation and excess cancer risks and addresses the radiation doses and risks from common medical imaging studies. Specific subsets of patients may be at greater risk from radiation exposure, and radiation risk should be considered carefully in these patients. Recent technical innovations have contributed to lowering the radiation dose from computed tomography, and the referring physician should be aware of these innovations in making imaging referrals. PMID:21123642

The challenge of ubiquitous computing in health care: technology, concepts and solutions. Findings from the IMIA Yearbook of Medical Informatics 2005.

PubMed

Bott, O J; Ammenwerth, E; Brigl, B; Knaup, P; Lang, E; Pilgram, R; Pfeifer, B; Ruderich, F; Wolff, A C; Haux, R; Kulikowski, C

2005-01-01

To review recent research efforts in the field of ubiquitous computing in health care. To identify current research trends and further challenges for medical informatics. Analysis of the contents of the Yearbook on Medical Informatics 2005 of the International Medical Informatics Association (IMIA). The Yearbook of Medical Informatics 2005 includes 34 original papers selected from 22 peer-reviewed scientific journals related to several distinct research areas: health and clinical management, patient records, health information systems, medical signal processing and biomedical imaging, decision support, knowledge representation and management, education and consumer informatics as well as bioinformatics. A special section on ubiquitous health care systems is devoted to recent developments in the application of ubiquitous computing in health care. Besides additional synoptical reviews of each of the sections the Yearbook includes invited reviews concerning E-Health strategies, primary care informatics and wearable healthcare. Several publications demonstrate the potential of ubiquitous computing to enhance effectiveness of health services delivery and organization. But ubiquitous computing is also a societal challenge, caused by the surrounding but unobtrusive character of this technology. Contributions from nearly all of the established sub-disciplines of medical informatics are demanded to turn the visions of this promising new research field into reality.

Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.

PubMed

Dilsizian, Steven E; Siegel, Eliot L

2014-01-01

Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.

Advantages of semiconductor CZT for medical imaging

NASA Astrophysics Data System (ADS)

Wagenaar, Douglas J.; Parnham, Kevin; Sundal, Bjorn; Maehlum, Gunnar; Chowdhury, Samir; Meier, Dirk; Vandehei, Thor; Szawlowski, Marek; Patt, Bradley E.

2007-09-01

Cadmium zinc telluride (CdZnTe, or CZT) is a room-temperature semiconductor radiation detector that has been developed in recent years for a variety of applications. CZT has been investigated for many potential uses in medical imaging, especially in the field of single photon emission computed tomography (SPECT). CZT can also be used in positron emission tomography (PET) as well as photon-counting and integration-mode x-ray radiography and computed tomography (CT). The principal advantages of CZT are 1) direct conversion of x-ray or gamma-ray energy into electron-hole pairs; 2) energy resolution; 3) high spatial resolution and hence high space-bandwidth product; 4) room temperature operation, stable performance, high density, and small volume; 5) depth-of-interaction (DOI) available through signal processing. These advantages will be described in detail with examples from our own CZT systems. The ability to operate at room temperature, combined with DOI and very small pixels, make the use of multiple, stationary CZT "mini-gamma cameras" a realistic alternative to today's large Anger-type cameras that require motion to obtain tomographic sampling. The compatibility of CZT with Magnetic Resonance Imaging (MRI)-fields is demonstrated for a new type of multi-modality medical imaging, namely SPECT/MRI. For pre-clinical (i.e., laboratory animal) imaging, the advantages of CZT lie in spatial and energy resolution, small volume, automated quality control, and the potential for DOI for parallax removal in pinhole imaging. For clinical imaging, the imaging of radiographically dense breasts with CZT enables scatter rejection and hence improved contrast. Examples of clinical breast images with a dual-head CZT system are shown.

Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system.

PubMed

Iyatomi, Hitoshi; Oka, Hiroshi; Saito, Masataka; Miyake, Ayako; Kimoto, Masayuki; Yamagami, Jun; Kobayashi, Seiichiro; Tanikawa, Akiko; Hagiwara, Masafumi; Ogawa, Koichi; Argenziano, Giuseppe; Soyer, H Peter; Tanaka, Masaru

2006-04-01

The aims of this study were to provide a quantitative assessment of the tumour area extracted by dermatologists and to evaluate computer-based methods from dermoscopy images for refining a computer-based melanoma diagnostic system. Dermoscopic images of 188 Clark naevi, 56 Reed naevi and 75 melanomas were examined. Five dermatologists manually drew the border of each lesion with a tablet computer. The inter-observer variability was evaluated and the standard tumour area (STA) for each dermoscopy image was defined. Manual extractions by 10 non-medical individuals and by two computer-based methods were evaluated with STA-based assessment criteria: precision and recall. Our new computer-based method introduced the region-growing approach in order to yield results close to those obtained by dermatologists. The effectiveness of our extraction method with regard to diagnostic accuracy was evaluated. Two linear classifiers were built using the results of conventional and new computer-based tumour area extraction methods. The final diagnostic accuracy was evaluated by drawing the receiver operating curve (ROC) of each classifier, and the area under each ROC was evaluated. The standard deviations of the tumour area extracted by five dermatologists and 10 non-medical individuals were 8.9% and 10.7%, respectively. After assessment of the extraction results by dermatologists, the STA was defined as the area that was selected by more than two dermatologists. Dermatologists selected the melanoma area with statistically smaller divergence than that of Clark naevus or Reed naevus (P = 0.05). By contrast, non-medical individuals did not show this difference. Our new computer-based extraction algorithm showed superior performance (precision, 94.1%; recall, 95.3%) to the conventional thresholding method (precision, 99.5%; recall, 87.6%). These results indicate that our new algorithm extracted a tumour area close to that obtained by dermatologists and, in particular, the border part of the tumour was adequately extracted. With this refinement, the area under the ROC increased from 0.795 to 0.875 and the diagnostic accuracy showed an increase of approximately 20% in specificity when the sensitivity was 80%. It can be concluded that our computer-based tumour extraction algorithm extracted almost the same area as that obtained by dermatologists and provided improved computer-based diagnostic accuracy.

Medical Student Preferences for Self-Directed Study Resources in Gross Anatomy

ERIC Educational Resources Information Center

Choi-Lundberg, Derek L.; Low, Tze Feng; Patman, Phillip; Turner, Paul; Sinha, Sankar N.

2016-01-01

Gross anatomy instruction in medical curricula involve a range of resources and activities including dissection, prosected specimens, anatomical models, radiological images, surface anatomy, textbooks, atlases, and computer-assisted learning (CAL). These resources and activities are underpinned by the expectation that students will actively engage…

[The procedure for documentation of digital images in forensic medical histology].

PubMed

Putintsev, V A; Bogomolov, D V; Fedulova, M V; Gribunov, Iu P; Kul'bitskiĭ, B N

2012-01-01

This paper is devoted to the novel computer technologies employed in the studies of histological preparations. These technologies allow to visualize digital images, structurize the data obtained and store the results in computer memory. The authors emphasize the necessity to properly document digital images obtained during forensic-histological studies and propose the procedure for the formulation of electronic documents in conformity with the relevant technical and legal requirements. It is concluded that the use of digital images as a new study object permits to obviate the drawbacks inherent in the work with the traditional preparations and pass from descriptive microscopy to their quantitative analysis.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

A web-based computer aided system for liver surgery planning: initial implementation on RayPlus

NASA Astrophysics Data System (ADS)

Luo, Ming; Yuan, Rong; Sun, Zhi; Li, Tianhong; Xie, Qingguo

2016-03-01

At present, computer aided systems for liver surgery design and risk evaluation are widely used in clinical all over the world. However, most systems are local applications that run on high-performance workstations, and the images have to processed offline. Compared with local applications, a web-based system is accessible anywhere and for a range of regardless of relative processing power or operating system. RayPlus (http://rayplus.life.hust.edu.cn), a B/S platform for medical image processing, was developed to give a jump start on web-based medical image processing. In this paper, we implement a computer aided system for liver surgery planning on the architecture of RayPlus. The system consists of a series of processing to CT images including filtering, segmentation, visualization and analyzing. Each processing is packaged into an executable program and runs on the server side. CT images in DICOM format are processed step by to interactive modeling on browser with zero-installation and server-side computing. The system supports users to semi-automatically segment the liver, intrahepatic vessel and tumor from the pre-processed images. Then, surface and volume models are built to analyze the vessel structure and the relative position between adjacent organs. The results show that the initial implementation meets satisfactorily its first-order objectives and provide an accurate 3D delineation of the liver anatomy. Vessel labeling and resection simulation are planned to add in the future. The system is available on Internet at the link mentioned above and an open username for testing is offered.

The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading

PubMed Central

Hainc, Nicolin; Federau, Christian; Stieltjes, Bram; Blatow, Maria; Bink, Andrea; Stippich, Christoph

2017-01-01

Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine. PMID:28983278

Microscopic medical image classification framework via deep learning and shearlet transform.

PubMed

Rezaeilouyeh, Hadi; Mollahosseini, Ali; Mahoor, Mohammad H

2016-10-01

Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computer-aided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the magnitude and phase of shearlet coefficients. Then we feed shearlet features along with the original images to our CNN consisting of multiple layers of convolution, max pooling, and fully connected layers. Our experiments show that using the magnitude and phase of shearlet coefficients as extra information to the network can improve the accuracy of detection and generalize better compared to the state-of-the-art methods that rely on hand-crafted features. This study expands the application of deep neural networks into the field of medical image analysis, which is a difficult domain considering the limited medical data available for such analysis.

A medical application integrating remote 3D visualization tools to access picture archiving and communication system on mobile devices.

PubMed

He, Longjun; Ming, Xing; Liu, Qian

2014-04-01

With computing capability and display size growing, the mobile device has been used as a tool to help clinicians view patient information and medical images anywhere and anytime. However, for direct interactive 3D visualization, which plays an important role in radiological diagnosis, the mobile device cannot provide a satisfactory quality of experience for radiologists. This paper developed a medical system that can get medical images from the picture archiving and communication system on the mobile device over the wireless network. In the proposed application, the mobile device got patient information and medical images through a proxy server connecting to the PACS server. Meanwhile, the proxy server integrated a range of 3D visualization techniques, including maximum intensity projection, multi-planar reconstruction and direct volume rendering, to providing shape, brightness, depth and location information generated from the original sectional images for radiologists. Furthermore, an algorithm that changes remote render parameters automatically to adapt to the network status was employed to improve the quality of experience. Finally, performance issues regarding the remote 3D visualization of the medical images over the wireless network of the proposed application were also discussed. The results demonstrated that this proposed medical application could provide a smooth interactive experience in the WLAN and 3G networks.

From macro-scale to micro-scale computational anatomy: a perspective on the next 20 years.

PubMed

Mori, Kensaku

2016-10-01

This paper gives our perspective on the next two decades of computational anatomy, which has made great strides in the recognition and understanding of human anatomy from conventional clinical images. The results from this field are now used in a variety of medical applications, including quantitative analysis of organ shapes, interventional assistance, surgical navigation, and population analysis. Several anatomical models have also been used in computational anatomy, and these mainly target millimeter-scale shapes. For example, liver-shape models are almost completely modeled at the millimeter scale, and shape variations are described at such scales. Most clinical 3D scanning devices have had just under 1 or 0.5 mm per voxel resolution for over 25 years, and this resolution has not changed drastically in that time. Although Z-axis (head-to-tail direction) resolution has been drastically improved by the introduction of multi-detector CT scanning devices, in-plane resolutions have not changed very much either. When we look at human anatomy, we can see different anatomical structures at different scales. For example, pulmonary blood vessels and lung lobes can be observed in millimeter-scale images. If we take 10-µm-scale images of a lung specimen, the alveoli and bronchiole regions can be located in them. Most work in millimeter-scale computational anatomy has been done by the medical-image analysis community. In the next two decades, we encourage our community to focus on micro-scale computational anatomy. In this perspective paper, we briefly review the achievements of computational anatomy and its impacts on clinical applications; furthermore, we show several possibilities from the viewpoint of microscopic computational anatomy by discussing experimental results from our recent research activities. Copyright © 2016 Elsevier B.V. All rights reserved.

Recent advances in 3D computed tomography techniques for simulation and navigation in hepatobiliary pancreatic surgery.

PubMed

Uchida, Masafumi

2014-04-01

A few years ago it could take several hours to complete a 3D image using a 3D workstation. Thanks to advances in computer science, obtaining results of interest now requires only a few minutes. Many recent 3D workstations or multimedia computers are equipped with onboard 3D virtual patient modeling software, which enables patient-specific preoperative assessment and virtual planning, navigation, and tool positioning. Although medical 3D imaging can now be conducted using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasonography (US) among others, the highest quality images are obtained using CT data, and CT images are now the most commonly used source of data for 3D simulation and navigation image. If the 2D source image is bad, no amount of 3D image manipulation in software will provide a quality 3D image. In this exhibition, the recent advances in CT imaging technique and 3D visualization of the hepatobiliary and pancreatic abnormalities are featured, including scan and image reconstruction technique, contrast-enhanced techniques, new application of advanced CT scan techniques, and new virtual reality simulation and navigation imaging. © 2014 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential

PubMed Central

Doi, Kunio

2007-01-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a “second opinion” and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists’ accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists. PMID:17349778

Artificial Intelligence in Medical Practice: The Question to the Answer?

PubMed

Miller, D Douglas; Brown, Eric W

2018-02-01

Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials. Copyright © 2018 Elsevier Inc. All rights reserved.

Fast estimation of first-order scattering in a medical x-ray computed tomography scanner using a ray-tracing technique.

PubMed

Liu, Xin

2014-01-01

This study describes a deterministic method for simulating the first-order scattering in a medical computed tomography scanner. The method was developed based on a physics model of x-ray photon interactions with matter and a ray tracing technique. The results from simulated scattering were compared to the ones from an actual scattering measurement. Two phantoms with homogeneous and heterogeneous material distributions were used in the scattering simulation and measurement. It was found that the simulated scatter profile was in agreement with the measurement result, with an average difference of 25% or less. Finally, tomographic images with artifacts caused by scatter were corrected based on the simulated scatter profiles. The image quality improved significantly.

Morphology filter bank for extracting nodular and linear patterns in medical images.

PubMed

Hashimoto, Ryutaro; Uchiyama, Yoshikazu; Uchimura, Keiichi; Koutaki, Gou; Inoue, Tomoki

2017-04-01

Using image processing to extract nodular or linear shadows is a key technique of computer-aided diagnosis schemes. This study proposes a new method for extracting nodular and linear patterns of various sizes in medical images. We have developed a morphology filter bank that creates multiresolution representations of an image. Analysis bank of this filter bank produces nodular and linear patterns at each resolution level. Synthesis bank can then be used to perfectly reconstruct the original image from these decomposed patterns. Our proposed method shows better performance based on a quantitative evaluation using a synthesized image compared with a conventional method based on a Hessian matrix, often used to enhance nodular and linear patterns. In addition, experiments show that our method can be applied to the followings: (1) microcalcifications of various sizes in mammograms can be extracted, (2) blood vessels of various sizes in retinal fundus images can be extracted, and (3) thoracic CT images can be reconstructed while removing normal vessels. Our proposed method is useful for extracting nodular and linear shadows or removing normal structures in medical images.

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures.

PubMed

Shirvaikar, Mukul; Lagadapati, Yamuna; Dong, Xuanliang

2017-03-01

Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ ( h ), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h . Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O ( n 2 ) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor.

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures

PubMed Central

Shirvaikar, Mukul; Lagadapati, Yamuna; Dong, Xuanliang

2016-01-01

Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ(h), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h. Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O (n2) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor. PMID:28428829

Are CT Scans Safe? Is It True That CT Scans May Increase My Risk of Cancer?

MedlinePlus

... products: Computed tomography (CT). U.S. Food and Drug Administration. http://www.fda.gov/Radiation-EmittingProducts/RadiationEmittingProductsandProcedures/MedicalImaging/MedicalX-Rays/ucm115317.htm. Accessed Jan. 19, 2018. Lee C, et al. Radiation-related risks of ...

Fundamental Concepts of Digital Image Processing

DOE R&D Accomplishments Database

Twogood, R. E.

1983-03-01

The field of a digital-image processing has experienced dramatic growth and increasingly widespread applicability in recent years. Fortunately, advances in computer technology have kept pace with the rapid growth in volume of image data in these and other applications. Digital image processing has become economical in many fields of research and in industrial and military applications. While each application has requirements unique from the others, all are concerned with faster, cheaper, more accurate, and more extensive computation. The trend is toward real-time and interactive operations, where the user of the system obtains preliminary results within a short enough time that the next decision can be made by the human processor without loss of concentration on the task at hand. An example of this is the obtaining of two-dimensional (2-D) computer-aided tomography (CAT) images. A medical decision might be made while the patient is still under observation rather than days later.

Roles of universal three-dimensional image analysis devices that assist surgical operations.

PubMed

Sakamoto, Tsuyoshi

2014-04-01

The circumstances surrounding medical image analysis have undergone rapid evolution. In such a situation, it can be said that "imaging" obtained through medical imaging modality and the "analysis" that we employ have become amalgamated. Recently, we feel the distance between "imaging" and "analysis" has become closer regarding the imaging analysis of any organ system, as if both terms mentioned above have become integrated. The history of medical image analysis started with the appearance of the computer. The invention of multi-planar reconstruction (MPR) used in the helical scan had a significant impact and became the basis for recent image analysis. Subsequently, curbed MPR (CPR) and other methods were developed, and the 3D diagnostic imaging and image analysis of the human body have started on a full scale. Volume rendering: the development of a new rendering algorithm and the significant improvement of memory and CPUs contributed to the development of "volume rendering," which allows 3D views with retained internal information. A new value was created by this development; computed tomography (CT) images that used to be for "diagnosis" before that time have become "applicable to treatment." In the past, before the development of volume rendering, a clinician had to mentally reconstruct an image reconfigured for diagnosis into a 3D image, but these developments have allowed the depiction of a 3D image on a monitor. Current technology: Currently, in Japan, the estimation of the liver volume and the perfusion area of the portal vein and hepatic vein are vigorously being adopted during preoperative planning for hepatectomy. Such a circumstance seems to be brought by the substantial improvement of said basic techniques and by upgrading the user interface, allowing doctors easy manipulation by themselves. The following describes the specific techniques. Future of post-processing technology: It is expected, in terms of the role of image analysis, for better or worse, that computer-aided diagnosis (CAD) will develop to a highly advanced level in every diagnostic field. Further, it is also expected in the treatment field that a technique coordinating various devices will be strongly required as a surgery navigator. Actually, surgery using an image navigator is being widely studied, and coordination with hardware, including robots, will also be developed. © 2014 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

Fusing MRI and Mechanical Imaging for Improved Prostate Cancer Diagnosis

DTIC Science & Technology

2016-10-01

Western Reserve University. - PI is participating weekly Prostate Imaging Reporting and Data System meeting in the Department of Radiology, Case Medical...Literary Guild (LG) seminar, Case Western Reserve University. Hosted by PI’s mentor. - PI is participating the majority of Imaging Hour meeting...Ernest Feleppa4, Dean Barratt2, Lee Ponsky5, Anant Madabhushi1 1 Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve

A diabetic retinopathy detection method using an improved pillar K-means algorithm.

PubMed

Gogula, Susmitha Valli; Divakar, Ch; Satyanarayana, Ch; Rao, Allam Appa

2014-01-01

The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.

Grid-Enabled Quantitative Analysis of Breast Cancer

DTIC Science & Technology

2009-10-01

large-scale, multi-modality computerized image analysis . The central hypothesis of this research is that large-scale image analysis for breast cancer...pilot study to utilize large scale parallel Grid computing to harness the nationwide cluster infrastructure for optimization of medical image ... analysis parameters. Additionally, we investigated the use of cutting edge dataanalysis/ mining techniques as applied to Ultrasound, FFDM, and DCE-MRI Breast

3D marker-controlled watershed for kidney segmentation in clinical CT exams.

PubMed

Wieclawek, Wojciech

2018-02-27

Image segmentation is an essential and non trivial task in computer vision and medical image analysis. Computed tomography (CT) is one of the most accessible medical examination techniques to visualize the interior of a patient's body. Among different computer-aided diagnostic systems, the applications dedicated to kidney segmentation represent a relatively small group. In addition, literature solutions are verified on relatively small databases. The goal of this research is to develop a novel algorithm for fully automated kidney segmentation. This approach is designed for large database analysis including both physiological and pathological cases. This study presents a 3D marker-controlled watershed transform developed and employed for fully automated CT kidney segmentation. The original and the most complex step in the current proposition is an automatic generation of 3D marker images. The final kidney segmentation step is an analysis of the labelled image obtained from marker-controlled watershed transform. It consists of morphological operations and shape analysis. The implementation is conducted in a MATLAB environment, Version 2017a, using i.a. Image Processing Toolbox. 170 clinical CT abdominal studies have been subjected to the analysis. The dataset includes normal as well as various pathological cases (agenesis, renal cysts, tumors, renal cell carcinoma, kidney cirrhosis, partial or radical nephrectomy, hematoma and nephrolithiasis). Manual and semi-automated delineations have been used as a gold standard. Wieclawek Among 67 delineated medical cases, 62 cases are 'Very good', whereas only 5 are 'Good' according to Cohen's Kappa interpretation. The segmentation results show that mean values of Sensitivity, Specificity, Dice, Jaccard, Cohen's Kappa and Accuracy are 90.29, 99.96, 91.68, 85.04, 91.62 and 99.89% respectively. All 170 medical cases (with and without outlines) have been classified by three independent medical experts as 'Very good' in 143-148 cases, as 'Good' in 15-21 cases and as 'Moderate' in 6-8 cases. An automatic kidney segmentation approach for CT studies to compete with commonly known solutions was developed. The algorithm gives promising results, that were confirmed during validation procedure done on a relatively large database, including 170 CTs with both physiological and pathological cases.

An automatic system to detect and extract texts in medical images for de-identification

NASA Astrophysics Data System (ADS)

Zhu, Yingxuan; Singh, P. D.; Siddiqui, Khan; Gillam, Michael

2010-03-01

Recently, there is an increasing need to share medical images for research purpose. In order to respect and preserve patient privacy, most of the medical images are de-identified with protected health information (PHI) before research sharing. Since manual de-identification is time-consuming and tedious, so an automatic de-identification system is necessary and helpful for the doctors to remove text from medical images. A lot of papers have been written about algorithms of text detection and extraction, however, little has been applied to de-identification of medical images. Since the de-identification system is designed for end-users, it should be effective, accurate and fast. This paper proposes an automatic system to detect and extract text from medical images for de-identification purposes, while keeping the anatomic structures intact. First, considering the text have a remarkable contrast with the background, a region variance based algorithm is used to detect the text regions. In post processing, geometric constraints are applied to the detected text regions to eliminate over-segmentation, e.g., lines and anatomic structures. After that, a region based level set method is used to extract text from the detected text regions. A GUI for the prototype application of the text detection and extraction system is implemented, which shows that our method can detect most of the text in the images. Experimental results validate that our method can detect and extract text in medical images with a 99% recall rate. Future research of this system includes algorithm improvement, performance evaluation, and computation optimization.

The increasing influence of medical image processing in clinical neuroimaging.

PubMed

Barillot, Christian

2007-01-20

This paper review the evolution of clinical neuroinformatics domain in the passed and gives an outlook how this research field will evolve in clinical neurology (e.g. Epilepsy, Multiple Sclerosis, Dementia) and neurosurgery (e.g. image guided surgery, intra-operative imaging, the definition of the Operation Room of the future). These different issues, as addressed by the VisAGeS research team, are discussed in more details and the benefits of a close collaboration between clinical scientists (radiologist, neurologist and neurosurgeon) and computer scientists are shown to give adequate answers to the series of problems which needs to be solved for a more effective use of medical images in clinical neurosciences.

Fully Convolutional Architecture for Low-Dose CT Image Noise Reduction

NASA Astrophysics Data System (ADS)

Badretale, S.; Shaker, F.; Babyn, P.; Alirezaie, J.

2017-10-01

One of the critical topics in medical low-dose Computed Tomography (CT) imaging is how best to maintain image quality. As the quality of images decreases with lowering the X-ray radiation dose, improving image quality is extremely important and challenging. We have proposed a novel approach to denoise low-dose CT images. Our algorithm learns directly from an end-to-end mapping from the low-dose Computed Tomography images for denoising the normal-dose CT images. Our method is based on a deep convolutional neural network with rectified linear units. By learning various low-level to high-level features from a low-dose image the proposed algorithm is capable of creating a high-quality denoised image. We demonstrate the superiority of our technique by comparing the results with two other state-of-the-art methods in terms of the peak signal to noise ratio, root mean square error, and a structural similarity index.

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

Benefit from NASA

NASA Image and Video Library

2001-09-01

The high-tech art of digital signal processing (DSP) was pioneered at NASA's Jet Propulsion Laboratory (JPL) in the mid-1960s for use in the Apollo Lunar Landing Program. Designed to computer enhance pictures of the Moon, this technology became the basis for the Landsat Earth resources satellites and subsequently has been incorporated into a broad range of Earthbound medical and diagnostic tools. DSP is employed in advanced body imaging techniques including Computer-Aided Tomography, also known as CT and CATScan, and Magnetic Resonance Imaging (MRI). CT images are collected by irradiating a thin slice of the body with a fan-shaped x-ray beam from a number of directions around the body's perimeter. A tomographic (slice-like) picture is reconstructed from these multiple views by a computer. MRI employs a magnetic field and radio waves, rather than x-rays, to create images.

Interrogation of patient data delivered to the operating theatre during hepato-pancreatic surgery using high-performance computing.

PubMed

John, Nigel W; McCloy, Rory F; Herrman, Simone

2004-01-01

The Op3D visualization system allows, for the first time, a surgeon in the operating theatre to interrogate patient-specific medical data sets rendered in three dimensions using high-performance computing. The hypothesis of this research is that the success rate of hepato-pancreatic surgical resections can be improved by replacing the light box with an interactive 3D representation of the medical data in the operating theatre. A laptop serves as the client computer and an easy-to-use interface has been developed for the surgeon to interact with and interrogate the patient data. To date, 16 patients have had 3D reconstructions of their DICOM data sets, including preoperative interrogation and planning of surgery. Interrogation of the 3D images live in theatre and comparison with the surgeons' operative findings (including intraoperative ultrasound) led to the operation being abandoned in 25% of cases, adoption of an alternative surgical approach in 25% of cases, and helpful image guidance for successful resection in 50% of cases. The clinical value of the latest generation of scanners and digital imaging techniques cannot be realized unless appropriate dissemination of the images takes place. This project has succeeded in translating the image technology into a user-friendly form and delivers 3D reconstructions of patient-specific data to the "sharp end"-the surgeon undertaking the tumor resection in theatre, in a manner that allows interaction and interpretation. More time interrogating the 3D data sets preoperatively would help reduce the incidence of abandoned operations-this is part of the surgeons' learning curve. We have developed one of the first practical applications to benefit from remote visualization, and certainly the first medical visualization application of this kind.

[The dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modeling language (VRML)].

PubMed

Yu, Zhengyang; Zheng, Shusen; Chen, Huaiqing; Wang, Jianjun; Xiong, Qingwen; Jing, Wanjun; Zeng, Yu

2006-10-01

This research studies the process of dynamic concision and 3D reconstruction from medical body data using VRML and JavaScript language, focuses on how to realize the dynamic concision of 3D medical model built with VRML. The 2D medical digital images firstly are modified and manipulated by 2D image software. Then, based on these images, 3D mould is built with VRML and JavaScript language. After programming in JavaScript to control 3D model, the function of dynamic concision realized by Script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be formed in high quality near to those got in traditional methods. By this way, with the function of dynamic concision, VRML browser can offer better windows of man-computer interaction in real time environment than before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and has a promising prospect in the fields of medical image.

A multi-resolution approach for optimal mass transport

NASA Astrophysics Data System (ADS)

Dominitz, Ayelet; Angenent, Sigurd; Tannenbaum, Allen

2007-09-01

Optimal mass transport is an important technique with numerous applications in econometrics, fluid dynamics, automatic control, statistical physics, shape optimization, expert systems, and meteorology. Motivated by certain problems in image registration and medical image visualization, in this note, we describe a simple gradient descent methodology for computing the optimal L2 transport mapping which may be easily implemented using a multiresolution scheme. We also indicate how the optimal transport map may be computed on the sphere. A numerical example is presented illustrating our ideas.

Flash X-Ray Apparatus With Spectrum Control Functions For Medical Use And Fuji Computed Radiography

NASA Astrophysics Data System (ADS)

Isobe, H.; Sato, E.; Hayasi, Y.; Suzuki, M.; Arima, H.; Hoshino, F.

1985-02-01

Flash radiographic bio-medical studies at sub-microsecond intervals were performed by using both a new type of flash X-ray(FX) apparatus with spectrum control functions and Fuji Computed Radiography(FCR). This single flasher tends to have a comparatively long exposure time and the electric pulse width of the FX wave form is about 0.3,usec. The maximum FX dose is about 50mR at 1m per pulse, and the effective focal spot varies according to condenser charging voltage, A-C distance, etc., ranging from 1.0 to 3.0mm in diameter, but in the low dose rate region it can be reduced to less than 1.0mm in diameter. The FX dose is determined by the condenser charging voltage and the A-C distance, while the FX spectrum is determined by the average voltage of the FX tube and filters. Various clear FX images were obtained by controlling the spectrum and dose. FCR is a new storage medium for medical radiography developed by the Fuji Photo Film Co., Ltd. and this apparatus has various image forming functions: low dose radiography, film density control, image contrast control, subtraction management and others. We have used this new apparatus in conjunction with our FX radiography and have obtained some new and interesting biomedical radiograms: the edge enhancement image, the instantaneous enlarged image, and the single exposure energy subtraction image using the FX spectrum distribution.

Bayesian X-ray computed tomography using a three-level hierarchical prior model

NASA Astrophysics Data System (ADS)

Wang, Li; Mohammad-Djafari, Ali; Gac, Nicolas

2017-06-01

In recent decades X-ray Computed Tomography (CT) image reconstruction has been largely developed in both medical and industrial domain. In this paper, we propose using the Bayesian inference approach with a new hierarchical prior model. In the proposed model, a generalised Student-t distribution is used to enforce the Haar transformation of images to be sparse. Comparisons with some state of the art methods are presented. It is shown that by using the proposed model, the sparsity of sparse representation of images is enforced, so that edges of images are preserved. Simulation results are also provided to demonstrate the effectiveness of the new hierarchical model for reconstruction with fewer projections.

Integrating medical imaging analyses through a high-throughput bundled resource imaging system

NASA Astrophysics Data System (ADS)

Covington, Kelsie; Welch, E. Brian; Jeong, Ha-Kyu; Landman, Bennett A.

2011-03-01

Exploitation of advanced, PACS-centric image analysis and interpretation pipelines provides well-developed storage, retrieval, and archival capabilities along with state-of-the-art data providence, visualization, and clinical collaboration technologies. However, pursuit of integrated medical imaging analysis through a PACS environment can be limiting in terms of the overhead required to validate, evaluate and integrate emerging research technologies. Herein, we address this challenge through presentation of a high-throughput bundled resource imaging system (HUBRIS) as an extension to the Philips Research Imaging Development Environment (PRIDE). HUBRIS enables PACS-connected medical imaging equipment to invoke tools provided by the Java Imaging Science Toolkit (JIST) so that a medical imaging platform (e.g., a magnetic resonance imaging scanner) can pass images and parameters to a server, which communicates with a grid computing facility to invoke the selected algorithms. Generated images are passed back to the server and subsequently to the imaging platform from which the images can be sent to a PACS. JIST makes use of an open application program interface layer so that research technologies can be implemented in any language capable of communicating through a system shell environment (e.g., Matlab, Java, C/C++, Perl, LISP, etc.). As demonstrated in this proof-of-concept approach, HUBRIS enables evaluation and analysis of emerging technologies within well-developed PACS systems with minimal adaptation of research software, which simplifies evaluation of new technologies in clinical research and provides a more convenient use of PACS technology by imaging scientists.

Present status and trends of image fusion

NASA Astrophysics Data System (ADS)

Xiang, Dachao; Fu, Sheng; Cai, Yiheng

2009-10-01

Image fusion information extracted from multiple images which is more accurate and reliable than that from just a single image. Since various images contain different information aspects of the measured parts, and comprehensive information can be obtained by integrating them together. Image fusion is a main branch of the application of data fusion technology. At present, it was widely used in computer vision technology, remote sensing, robot vision, medical image processing and military field. This paper mainly presents image fusion's contents, research methods, and the status quo at home and abroad, and analyzes the development trend.

Framework for 2D-3D image fusion of infrared thermography with preoperative MRI.

PubMed

Hoffmann, Nico; Weidner, Florian; Urban, Peter; Meyer, Tobias; Schnabel, Christian; Radev, Yordan; Schackert, Gabriele; Petersohn, Uwe; Koch, Edmund; Gumhold, Stefan; Steiner, Gerald; Kirsch, Matthias

2017-11-27

Multimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and single-photon emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.

Computer aided lung cancer diagnosis with deep learning algorithms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2016-03-01

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

Prevalence and correlates of patient-centred preparatory information provision to computed tomography and magnetic resonance imaging outpatients: A cross-sectional study.

PubMed

Hyde, Lisa; Mackenzie, Lisa; Boyes, Allison W; Evans, Tiffany-Jane; Symonds, Michael; Sanson-Fisher, Rob

2018-06-02

Responsiveness to information preferences is key to high-quality, patient-centred care. This study examined the top ten preparatory information items not delivered in accordance with medical imaging outpatients' preferences, and patient characteristics associated with reporting a greater number of unmet information preferences. Magnetic resonance imaging and computed tomography outpatients were recruited consecutively in one major public hospital waiting room. Participants self-administered a touchscreen computer questionnaire assessing their sociodemographic and scan characteristics, and unmet preferences for 33 guideline-endorsed preparatory information items. Of 317 eligible patients, 280 (88%) consented to participate. Given equal rankings, the top ten unmet information preferences included 13 items which were endorsed by at least 25% of participants, and commonly related to receiving 'too little' information. One item related to the pre-scan period, seven items to the scan period and five items to the post-scan period. None of the patient characteristics examined were significantly associated with reporting a greater number of unmet information preferences. There is room to improve responsiveness to medical imaging outpatients' preparatory information preferences. Improvements should be targeted at individuals, rather than groups defined by sociodemographic or scan characteristics. A standardised approach to addressing individual patient's information preferences is needed. Copyright © 2018 Elsevier B.V. All rights reserved.

Highlighting the medical applications of 3D printing in Egypt

PubMed Central

Abdelghany, Khaled; Hamza, Hosamuddin

2015-01-01

Computer-assisted designing/computer-assisted manufacturing (CAD/CAM) technology has enabled medical practitioners to tailor physical models in a patient and purpose-specific fashion. It allows the designing and manufacturing of templates, appliances and devices with a high range of accuracy using biocompatible materials. The technique, nevertheless, relies on digital scanning (e.g., using intraoral scanners) and/or digital imaging (e.g., CT and MRI). In developing countries, there are some technical and financial limitations of implementing such advanced tools as an essential portion of medical applications. This paper focuses on the surgical and dental use of 3D printing technology in Egypt as a developing country. PMID:26807414

Learning clinically useful information from images: Past, present and future.

PubMed

Rueckert, Daniel; Glocker, Ben; Kainz, Bernhard

2016-10-01

Over the last decade, research in medical imaging has made significant progress in addressing challenging tasks such as image registration and image segmentation. In particular, the use of model-based approaches has been key in numerous, successful advances in methodology. The advantage of model-based approaches is that they allow the incorporation of prior knowledge acting as a regularisation that favours plausible solutions over implausible ones. More recently, medical imaging has moved away from hand-crafted, and often explicitly designed models towards data-driven, implicit models that are constructed using machine learning techniques. This has led to major improvements in all stages of the medical imaging pipeline, from acquisition and reconstruction to analysis and interpretation. As more and more imaging data is becoming available, e.g., from large population studies, this trend is likely to continue and accelerate. At the same time new developments in machine learning, e.g., deep learning, as well as significant improvements in computing power, e.g., parallelisation on graphics hardware, offer new potential for data-driven, semantic and intelligent medical imaging. This article outlines the work of the BioMedIA group in this area and highlights some of the challenges and opportunities for future work. Copyright © 2016 Elsevier B.V. All rights reserved.

Deep Learning in Gastrointestinal Endoscopy.

PubMed

Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

2016-01-01

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

Dual-modality imaging

NASA Astrophysics Data System (ADS)

Hasegawa, Bruce; Tang, H. Roger; Da Silva, Angela J.; Wong, Kenneth H.; Iwata, Koji; Wu, Max C.

2001-09-01

In comparison to conventional medical imaging techniques, dual-modality imaging offers the advantage of correlating anatomical information from X-ray computed tomography (CT) with functional measurements from single-photon emission computed tomography (SPECT) or with positron emission tomography (PET). The combined X-ray/radionuclide images from dual-modality imaging can help the clinician to differentiate disease from normal uptake of radiopharmaceuticals, and to improve diagnosis and staging of disease. In addition, phantom and animal studies have demonstrated that a priori structural information from CT can be used to improve quantification of tissue uptake and organ function by correcting the radionuclide data for errors due to photon attenuation, partial volume effects, scatter radiation, and other physical effects. Dual-modality imaging therefore is emerging as a method of improving the visual quality and the quantitative accuracy of radionuclide imaging for diagnosis of patients with cancer and heart disease.

A Study of NetCDF as an Approach for High Performance Medical Image Storage

NASA Astrophysics Data System (ADS)

Magnus, Marcone; Coelho Prado, Thiago; von Wangenhein, Aldo; de Macedo, Douglas D. J.; Dantas, M. A. R.

2012-02-01

The spread of telemedicine systems increases every day. The systems and PACS based on DICOM images has become common. This rise reflects the need to develop new storage systems, more efficient and with lower computational costs. With this in mind, this article discusses a study for application in NetCDF data format as the basic platform for storage of DICOM images. The study case comparison adopts an ordinary database, the HDF5 and the NetCDF to storage the medical images. Empirical results, using a real set of images, indicate that the time to retrieve images from the NetCDF for large scale images has a higher latency compared to the other two methods. In addition, the latency is proportional to the file size, which represents a drawback to a telemedicine system that is characterized by a large amount of large image files.

SU-E-I-23: Design and Clinical Application of External Marking Body in Multi- Mode Medical Images Registration and Fusion

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

Chen, Z; Gong, G

2014-06-01

Purpose: To design an external marking body (EMB) that could be visible on computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET) and single-photon emission computed tomography (SPECT) images and to investigate the use of the EMB for multiple medical images registration and fusion in the clinic. Methods: We generated a solution containing paramagnetic metal ions and iodide ions (CT'MR dual-visible solution) that could be viewed on CT and MR images and multi-mode image visible solution (MIVS) that could be obtained by mixing radioactive nuclear material. A globular plastic theca (diameter: 3–6 mm) that mothball the MIVS and themore » EMB was brought by filling MIVS. The EMBs were fixed on the patient surface and CT, MR, PET and SPECT scans were obtained. The feasibility of clinical application and the display and registration error of EMB among different image modalities were investigated. Results: The dual-visible solution was highly dense on CT images (HU>700). A high signal was also found in all MR scanning (T1, T2, STIR and FLAIR) images, and the signal was higher than subcutaneous fat. EMB with radioactive nuclear material caused a radionuclide concentration area on PET and SPECT images, and the signal of EMB was similar to or higher than tumor signals. The theca with MIVS was clearly visible on all the images without artifact, and the shape was round or oval with a sharp edge. The maximum diameter display error was 0.3 ± 0.2mm on CT and MRI images, and 1.0 ± 0.3mm on PET and SPECT images. In addition, the registration accuracy of the theca center among multi-mode images was less than 1mm. Conclusion: The application of EMB with MIVS improves the registration and fusion accuracy of multi-mode medical images. Furthermore, it has the potential to ameliorate disease diagnosis and treatment outcome.« less

Medical Images Remote Consultation

NASA Astrophysics Data System (ADS)

Ferraris, Maurizio; Frixione, Paolo; Squarcia, Sandro

Teleconsultation of digital images among different medical centers is now a reality. The problem to be solved is how to interconnect all the clinical diagnostic devices in a hospital in order to allow physicians and health physicists, working in different places, to discuss on interesting clinical cases visualizing the same diagnostic images at the same time. Applying World Wide Web technologies, the proposed system can be easily used by people with no specific computer knowledge providing a verbose help to guide the user through the right steps of execution. Diagnostic images are retrieved from a relational database or from a standard DICOM-PACS through the DICOM-WWW gateway allowing connection of the usual Web browsers to DICOM applications via the HTTP protocol. The system, which is proposed for radiotherapy implementation, where radiographies play a fundamental role, can be easily converted to different field of medical applications where a remote access to secure data are compulsory.

The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

PubMed Central

Yang, Guocheng; Li, Meiling; Chen, Leiting; Yu, Jie

2015-01-01

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices. PMID:26557871

Augmented reality to the rescue of the minimally invasive surgeon. The usefulness of the interposition of stereoscopic images in the Da Vinci™ robotic console.

PubMed

Volonté, Francesco; Buchs, Nicolas C; Pugin, François; Spaltenstein, Joël; Schiltz, Boris; Jung, Minoa; Hagen, Monika; Ratib, Osman; Morel, Philippe

2013-09-01

Computerized management of medical information and 3D imaging has become the norm in everyday medical practice. Surgeons exploit these emerging technologies and bring information previously confined to the radiology rooms into the operating theatre. The paper reports the authors' experience with integrated stereoscopic 3D-rendered images in the da Vinci surgeon console. Volume-rendered images were obtained from a standard computed tomography dataset using the OsiriX DICOM workstation. A custom OsiriX plugin was created that permitted the 3D-rendered images to be displayed in the da Vinci surgeon console and to appear stereoscopic. These rendered images were displayed in the robotic console using the TilePro multi-input display. The upper part of the screen shows the real endoscopic surgical field and the bottom shows the stereoscopic 3D-rendered images. These are controlled by a 3D joystick installed on the console, and are updated in real time. Five patients underwent a robotic augmented reality-enhanced procedure. The surgeon was able to switch between the classical endoscopic view and a combined virtual view during the procedure. Subjectively, the addition of the rendered images was considered to be an undeniable help during the dissection phase. With the rapid evolution of robotics, computer-aided surgery is receiving increasing interest. This paper details the authors' experience with 3D-rendered images projected inside the surgical console. The use of this intra-operative mixed reality technology is considered very useful by the surgeon. It has been shown that the usefulness of this technique is a step toward computer-aided surgery that will progress very quickly over the next few years. Copyright © 2012 John Wiley & Sons, Ltd.

Visidep (TM): A Three-Dimensional Imaging System For The Unaided Eye

NASA Astrophysics Data System (ADS)

McLaurin, A. Porter; Jones, Edwin R.; Cathey, LeConte

1984-05-01

The VISIDEP process for creating images in three dimensions on flat screens is suitable for photographic, electrographic and computer generated imaging systems. Procedures for generating these images vary from medium to medium due to the specific requirements of each technology. Imaging requirements for photographic and electrographic media are more directly tied to the hardware than are computer based systems. Applications of these technologies are not limited to entertainment, but have implications for training, interactive computer/video systems, medical imaging, and inspection equipment. Through minor modification the system can provide three-dimensional images with accurately measureable relationships for robotics and adds this factor for future developments in artificial intelligence. In almost any area requiring image analysis or critical review, VISIDEP provides the added advantage of three-dimensionality. All of this is readily accomplished without aids to the human eye. The system can be viewed in full color, false-color infra-red, and monochromatic modalities from any angle and is also viewable with a single eye. Thus, the potential of application for this developing system is extensive and covers the broad spectrum of human endeavor from entertainment to scientific study.

MO-G-18A-01: Radiation Dose Reducing Strategies in CT, Fluoroscopy and Radiography

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

Mahesh, M; Gingold, E; Jones, A

2014-06-15

Advances in medical x-ray imaging have provided significant benefits to patient care. According to NCRP 160, there are more than 400 million x-ray procedures performed annually in the United States alone that contributes to nearly half of all the radiation exposure to the US population. Similar growth trends in medical x-ray imaging are observed worldwide. Apparent increase in number of medical x-ray imaging procedures, new protocols and the associated radiation dose and risk has drawn considerable attention. This has led to a number of technological innovations such as tube current modulation, iterative reconstruction algorithms, dose alerts, dose displays, flat panelmore » digital detectors, high efficient digital detectors, storage phosphor radiography, variable filters, etc. that are enabling users to acquire medical x-ray images at a much lower radiation dose. Along with these, there are number of radiation dose optimization strategies that users can adapt to effectively lower radiation dose in medical x-ray procedures. The main objectives of this SAM course are to provide information and how to implement the various radiation dose optimization strategies in CT, Fluoroscopy and Radiography. Learning Objectives: To update impact of technological advances on dose optimization in medical imaging. To identify radiation optimization strategies in computed tomography. To describe strategies for configuring fluoroscopic equipment that yields optimal images at reasonable radiation dose. To assess ways to configure digital radiography systems and recommend ways to improve image quality at optimal dose.« less

Education and research in medical optronics in France

NASA Astrophysics Data System (ADS)

Demongeot, Jacques; Fleute, M.; Herve, T.; Lavallee, Stephane

2000-06-01

First we present here the main post-graduate courses proposed in France both for physicians and engineers in medical optronics. After we explain which medical domains are concerned by this teaching, essentially computer assisted surgery, telemedicine and functional exploration. Then we show the main research axes in these fields, in which new jobs have to be invented and new educational approaches have to be prepared in order to satisfy the demand coming both from hospitals (mainly referent hospitals) and from industry (essentially medical imaging and instrumentation companies). Finally we will conclude that medical optronics is an important step in an entire chain of acquisition and processing of medical data, capable to create the medical knowledge a surgeon or a physician needs for diagnosis or therapy purposes. Optimizing the teaching of medical optronics needs a complete integration from acquiring to modeling the medical reality. This tendency to give a holistic education in medical imaging and instrumentation is called `Model driven Acquisition' learning.

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval

PubMed Central

Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G.; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

2016-01-01

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency. PMID:27688597

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.

PubMed

Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

2016-02-12

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

Medical image integrity control and forensics based on watermarking--approximating local modifications and identifying global image alterations.

PubMed

Huang, H; Coatrieux, G; Shu, H Z; Luo, L M; Roux, Ch

2011-01-01

In this paper we present a medical image integrity verification system that not only allows detecting and approximating malevolent local image alterations (e.g. removal or addition of findings) but is also capable to identify the nature of global image processing applied to the image (e.g. lossy compression, filtering …). For that purpose, we propose an image signature derived from the geometric moments of pixel blocks. Such a signature is computed over regions of interest of the image and then watermarked in regions of non interest. Image integrity analysis is conducted by comparing embedded and recomputed signatures. If any, local modifications are approximated through the determination of the parameters of the nearest generalized 2D Gaussian. Image moments are taken as image features and serve as inputs to one classifier we learned to discriminate the type of global image processing. Experimental results with both local and global modifications illustrate the overall performances of our approach.

Creating a Vision Channel for Observing Deep-Seated Anatomy in Medical Augmented Reality

NASA Astrophysics Data System (ADS)

Wimmer, Felix; Bichlmeier, Christoph; Heining, Sandro M.; Navab, Nassir

The intent of medical Augmented Reality (AR) is to augment the surgeon's real view on the patient with the patient's interior anatomy resulting from a suitable visualization of medical imaging data. This paper presents a fast and user-defined clipping technique for medical AR allowing for cutting away any parts of the virtual anatomy and images of the real part of the AR scene hindering the surgeon's view onto the deepseated region of interest. Modeled on cut-away techniques from scientific illustrations and computer graphics, the method creates a fixed vision channel to the inside of the patient. It enables a clear view on the focussed virtual anatomy and moreover improves the perception of spatial depth.

Development and Evaluation of an Interactive Computer-Assisted Learning Program--A Novel Approach to Teaching Gynaecological Surgery.

ERIC Educational Resources Information Center

Jha, Vikram; Widdowson, Shelley; Duffy, Sean

2002-01-01

Discusses computer-assisted learning (CAL) in medical education and describes the development of an interactive CAL program on CD-ROM, combining video, illustrations, and three-dimensional images, to enhance understanding of vaginal hysterectomy in terms of the anatomy and steps of the surgical procedure. (Author/LRW)

MATHEMATICAL METHODS IN MEDICAL IMAGE PROCESSING

PubMed Central

ANGENENT, SIGURD; PICHON, ERIC; TANNENBAUM, ALLEN

2013-01-01

In this paper, we describe some central mathematical problems in medical imaging. The subject has been undergoing rapid changes driven by better hardware and software. Much of the software is based on novel methods utilizing geometric partial differential equations in conjunction with standard signal/image processing techniques as well as computer graphics facilitating man/machine interactions. As part of this enterprise, researchers have been trying to base biomedical engineering principles on rigorous mathematical foundations for the development of software methods to be integrated into complete therapy delivery systems. These systems support the more effective delivery of many image-guided procedures such as radiation therapy, biopsy, and minimally invasive surgery. We will show how mathematics may impact some of the main problems in this area, including image enhancement, registration, and segmentation. PMID:23645963

Ultrasonic image analysis and image-guided interventions.

PubMed

Noble, J Alison; Navab, Nassir; Becher, H

2011-08-06

The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

20 CFR 30.114 - What kind of evidence is needed to establish a compensable medical condition and how will that...

Code of Federal Regulations, 2014 CFR

2014-04-01

...' Benefits OFFICE OF WORKERS' COMPENSATION PROGRAMS, DEPARTMENT OF LABOR ENERGY EMPLOYEES OCCUPATIONAL ILLNESS COMPENSATION PROGRAM ACT OF 2000 CLAIMS FOR COMPENSATION UNDER THE ENERGY EMPLOYEES OCCUPATIONAL... certificates, x-rays, magnetic resonance images or reports, computer axial tomography or other imaging reports...

20 CFR 30.114 - What kind of evidence is needed to establish a compensable medical condition and how will that...

Code of Federal Regulations, 2013 CFR

2013-04-01

...' Benefits OFFICE OF WORKERS' COMPENSATION PROGRAMS, DEPARTMENT OF LABOR ENERGY EMPLOYEES OCCUPATIONAL ILLNESS COMPENSATION PROGRAM ACT OF 2000 CLAIMS FOR COMPENSATION UNDER THE ENERGY EMPLOYEES OCCUPATIONAL... certificates, x-rays, magnetic resonance images or reports, computer axial tomography or other imaging reports...

Providing image management and communication functionality as an integral part of an existing hospital information system

NASA Astrophysics Data System (ADS)

Dayhoff, Ruth E.; Maloney, Daniel L.

1990-08-01

The effective delivery of health care has become increasingly dependent on a wide range of medical data which includes a variety of images. Manual and computer-based medical records ordinarily do not contain image data, leaving the physician to deal with a fragmented patient record widely scattered throughout the hospital. The Department of Veterans Affairs (VA) is currently installing a prototype hospital information system (HIS) workstation network to demonstrate the feasibility of providing image management and communications (IMAC) functionality as an integral part of an existing hospital information system. The core of this system is a database management system adapted to handle images as a new data type. A general model for this integration is discussed and specifics of the hospital-wide network of image display workstations are given.

Cancer Imaging Phenomics Toolkit (CaPTk) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

CaPTk is a software toolkit to facilitate translation of quantitative image analysis methods that help us obtain rich imaging phenotypic signatures of oncologic images and relate them to precision diagnostics and prediction of clinical outcomes, as well as to underlying molecular characteristics of cancer. The stand-alone graphical user interface of CaPTk brings analysis methods from the realm of medical imaging research to the clinic, and will be extended to use web-based services for computationally-demanding pipelines.

MO-A-9A-01: Innovation in Medical Physics Practice: 3D Printing Applications

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

Ehler, E; Perks, J; Rasmussen, K

2014-06-15

3D printing, also called additive manufacturing, has great potential to advance the field of medicine. Many medical uses have been exhibited from facial reconstruction to the repair of pulmonary obstructions. The strength of 3D printing is to quickly convert a 3D computer model into a physical object. Medical use of 3D models is already ubiquitous with technologies such as computed tomography and magnetic resonance imaging. Thus tailoring 3D printing technology to medical functions has the potential to impact patient care. This session will discuss applications to the field of Medical Physics. Topics discussed will include introduction to 3D printing methodsmore » as well as examples of real-world uses of 3D printing spanning clinical and research practice in diagnostic imaging and radiation therapy. The session will also compare 3D printing to other manufacturing processes and discuss a variety of uses of 3D printing technology outside the field of Medical Physics. Learning Objectives: Understand the technologies available for 3D Printing Understand methods to generate 3D models Identify the benefits and drawbacks to rapid prototyping / 3D Printing Understand the potential issues related to clinical use of 3D Printing.« less

SU-D-BRD-02: A Web-Based Image Processing and Plan Evaluation Platform (WIPPEP) for Future Cloud-Based Radiotherapy

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

Chai, X; Liu, L; Xing, L

Purpose: Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiotherapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers and relay informationmore » to the backend to process user requests. The image server serves as a PACS system. The computation server performs the actual image processing and dose calculation. The web server backend is developed using Java Servlets and the frontend is developed using HTML5, Javascript, and jQuery. The image server is based on open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in Delphi, Python and PHP, which can process data directly or via a C++ program DLL. Results: This software platform is running on a 32-core CPU server virtually hosting the web server, image server, and computation servers separately. Users can visit our internal website with Chrome browser, select a specific patient, visualize image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python or PHP computation server. Conclusion: We have developed a webbased image processing and plan evaluation platform prototype for radiotherapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiotherapy.« less

Gaps in content-based image retrieval

NASA Astrophysics Data System (ADS)

Deserno, Thomas M.; Antani, Sameer; Long, Rodney

2007-03-01

Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potentially strong impact in diagnostics, research, and education. Research successes that are increasingly reported in the scientific literature, however, have not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed without sufficient analytical reasoning to the inability of these applications in overcoming the "semantic gap". The semantic gap divides the high-level scene analysis of humans from the low-level pixel analysis of computers. In this paper, we suggest a more systematic and comprehensive view on the concept of gaps in medical CBIR research. In particular, we define a total of 13 gaps that address the image content and features, as well as the system performance and usability. In addition to these gaps, we identify 6 system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application. To illustrate the a posteriori use of our conceptual system, we apply it, initially, to the classification of three medical CBIR implementations: the content-based PACS approach (cbPACS), the medical GNU image finding tool (medGIFT), and the image retrieval in medical applications (IRMA) project. We show that systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.

[Progress in imaging techniques].

PubMed

Mishima, Kazuaki; Otsuka, Tsukasa

2013-05-01

Today it is common to perform real-time diagnosis and treatment via live broadcast as a method of education and to spread new technology for diagnosis and therapy in medical fields. Live medical broadcasts have developed along with broadcast technology. In the early days, live video feeds were sent from operating rooms to classrooms and lecture halls in universities and hospitals. However, the development of imaging techniques and communication networks enabled live broadcasts that bi-directionally link operating rooms and meeting halls during scientific meetings and live demonstration courses. Live broadcasts therefore became an important method for education and the dissemination of new medical technologies. The development of imaging techniques has contributed to more realistic live broadcasts through such innovative techniques as three-dimensional viewing and higher-definition 4K technology. In the future, live broadcasts will be transmitted on personal computers using regular Internet connections. In addition to the enhancement of image delivery technology, it will also be necessary to examine the entire image delivery environment carefully, including issues of security and privacy of personal information.

Novel medical imaging technologies for disease diagnosis and treatment

NASA Astrophysics Data System (ADS)

Olego, Diego

2009-03-01

New clinical approaches for disease diagnosis, treatment and monitoring will rely on the ability of simultaneously obtaining anatomical, functional and biological information. Medical imaging technologies in combination with targeted contrast agents play a key role in delivering with ever increasing temporal and spatial resolution structural and functional information about conditions and pathologies in cardiology, oncology and neurology fields among others. This presentation will review the clinical motivations and physics challenges in on-going developments of new medical imaging techniques and the associated contrast agents. Examples to be discussed are: *The enrichment of computer tomography with spectral sensitivity for the diagnosis of vulnerable sclerotic plaque. *Time of flight positron emission tomography for improved resolution in metabolic characterization of pathologies. *Magnetic particle imaging -a novel imaging modality based on in-vivo measurement of the local concentration of iron oxide nano-particles - for blood perfusion measurement with better sensitivity, spatial resolution and 3D real time acquisition. *Focused ultrasound for therapy delivery.

Optical 3D watermark based digital image watermarking for telemedicine

NASA Astrophysics Data System (ADS)

Li, Xiao Wei; Kim, Seok Tae

2013-12-01

Region of interest (ROI) of a medical image is an area including important diagnostic information and must be stored without any distortion. This algorithm for application of watermarking technique for non-ROI of the medical image preserving ROI. The paper presents a 3D watermark based medical image watermarking scheme. In this paper, a 3D watermark object is first decomposed into 2D elemental image array (EIA) by a lenslet array, and then the 2D elemental image array data is embedded into the host image. The watermark extraction process is an inverse process of embedding. The extracted EIA through the computational integral imaging reconstruction (CIIR) technique, the 3D watermark can be reconstructed. Because the EIA is composed of a number of elemental images possesses their own perspectives of a 3D watermark object. Even though the embedded watermark data badly damaged, the 3D virtual watermark can be successfully reconstructed. Furthermore, using CAT with various rule number parameters, it is possible to get many channels for embedding. So our method can recover the weak point having only one transform plane in traditional watermarking methods. The effectiveness of the proposed watermarking scheme is demonstrated with the aid of experimental results.

Strategies for the promotion of computer applications in radiology in healthcare delivery.

PubMed

Reiner, B; Siegel, E; Allman, R

1998-08-01

The objective of this paper is to identify current trends in the development and implementation of computer applications in today's ever-changing healthcare environment. Marketing strategies are discussed with the goal of promoting computer applications in radiology as a means to advance future healthcare acceptance of technologic developments from the medical imaging field. With the rapid evolution of imaging and and information technologies along with the transition to filmless imaging, radiologists must assume a proactive role in the development and application of these advancements. This expansion can be accomplished in a number of ways including internet based educational programs, research partnerships, and professional membership in societies such as the Society of Computer Applications in Radiology (SCAR). Professional societies such as SCAR, in turn, should reach out to include other professionals from the healthcare community. These would include financial, administrative, and information systems disciplines to promote these technologies in a cost conscious and value added manner.

Human-machine interface for a VR-based medical imaging environment

NASA Astrophysics Data System (ADS)

Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

1997-05-01

Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

Creating vascular models by postprocessing computed tomography angiography images: a guide for anatomical education.

PubMed

Govsa, Figen; Ozer, Mehmet Asim; Sirinturk, Suzan; Eraslan, Cenk; Alagoz, Ahmet Kemal

2017-08-01

A new application of teaching anatomy includes the use of computed tomography angiography (CTA) images to create clinically relevant three-dimensional (3D) printed models. The purpose of this article is to review recent innovations on the process and the application of 3D printed models as a tool for using under and post-graduate medical education. Images of aortic arch pattern received by CTA were converted into 3D images using the Google SketchUp free software and were saved in stereolithography format. Using a 3D printer (Makerbot), a model mode polylactic acid material was printed. A two-vessel left aortic arch was identified consisting of the brachiocephalic trunk and left subclavian artery. The life-like 3D models were rotated 360° in all axes in hand. The early adopters in education and clinical practices have embraced the medical imaging-guided 3D printed anatomical models for their ability to provide tactile feedback and a superior appreciation of visuospatial relationship between the anatomical structures. Printed vascular models are used to assist in preoperative planning, develop intraoperative guidance tools, and to teach patients surgical trainees in surgical practice.

Benefit from NASA

NASA Image and Video Library

2001-01-01

The high-tech art of digital signal processing (DSP) was pioneered at NASA's Jet Propulsion Laboratory (JPL) in the mid-1960s for use in the Apollo Lunar Landing Program. Designed to computer enhance pictures of the Moon, this technology became the basis for the Landsat Earth resources satellites and subsequently has been incorporated into a broad range of Earthbound medical and diagnostic tools. DSP is employed in advanced body imaging techniques including Computer-Aided Tomography, also known as CT and CATScan, and Magnetic Resonance Imaging (MRI). CT images are collected by irradiating a thin slice of the body with a fan-shaped x-ray beam from a number of directions around the body's perimeter. A tomographic (slice-like) picture is reconstructed from these multiple views by a computer. MRI employs a magnetic field and radio waves, rather than x-rays, to create images. In this photograph, a patient undergoes an open MRI.

Bits and bytes: the future of radiology lies in informatics and information technology.

PubMed

Brink, James A; Arenson, Ronald L; Grist, Thomas M; Lewin, Jonathan S; Enzmann, Dieter

2017-09-01

Advances in informatics and information technology are sure to alter the practice of medical imaging and image-guided therapies substantially over the next decade. Each element of the imaging continuum will be affected by substantial increases in computing capacity coincident with the seamless integration of digital technology into our society at large. This article focuses primarily on areas where this IT transformation is likely to have a profound effect on the practice of radiology. • Clinical decision support ensures consistent and appropriate resource utilization. • Big data enables correlation of health information across multiple domains. • Data mining advances the quality of medical decision-making. • Business analytics allow radiologists to maximize the benefits of imaging resources.

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

PubMed

Lückehe, Daniel; von Voigt, Gabriele

2018-05-29

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

[The radiologist physician in major trauma evaluation].

PubMed

Motta-Ramírez, Gaspar Alberto

2016-01-01

Trauma is the most common cause of death in young adults. A multidisciplinary trauma team consists of at least a surgical team, an anesthesiology team, radiologic team, and an emergency department team. Recognize the integration of multidisciplinary medical team in managing the trauma patient and which must include the radiologist physician responsible for the institutional approach to the systematization of the trauma patient regarding any radiological and imaging study with emphasis on the FAST (del inglés, Focused Assessment with Sonography in Trauma)/USTA, Whole body computed tomography. Ultrasound is a cross-sectional method available for use in patients with major trauma. Whole-body multidetector computed tomography became the imaging modality of choice in the late 1990s. In patients with major trauma, examination FAST often is the initial imaging examination, extended to extraabdominal regions. Patients who have multitrauma from blunt mechanisms often require multiple diagnostic examinations, including Computed Tomography imaging of the torso as well as abdominopelvic Computed Tomography angiography. Multiphasic Whole-body trauma imaging is feasible, helps detect clinically relevant vascular injuries, and results in diagnostic image quality in the majority of patients. Computed Tomography has gained importance in the early diagnostic phase of trauma care in the emergency room. With a single continuous acquisition, whole-body computed tomography angiography is able to demonstrate all potentially injured organs, as well as vascular and bone structures, from the circle of Willis to the symphysis pubis.

An implementation of wireless medical image transmission system on mobile devices.

PubMed

Lee, SangBock; Lee, Taesoo; Jin, Gyehwan; Hong, Juhyun

2008-12-01

The advanced technology of computing system was followed by the rapid improvement of medical instrumentation and patient record management system. The typical examples are hospital information system (HIS) and picture archiving and communication system (PACS), which computerized the management procedure of medical records and images in hospital. Because these systems were built and used in hospitals, doctors out of hospital have problems to access them immediately on emergent cases. To solve these problems, this paper addressed the realization of system that could transmit the images acquired by medical imaging systems in hospital to the remote doctors' handheld PDA's using CDMA cellular phone network. The system consists of server and PDA. The server was developed to manage the accounts of doctors and patients and allocate the patient images to each doctor. The PDA was developed to display patient images through remote server connection. To authenticate the personal user, remote data access (RDA) method was used in PDA accessing the server database and file transfer protocol (FTP) was used to download patient images from the remove server. In laboratory experiments, it was calculated to take ninety seconds to transmit thirty images with 832 x 488 resolution and 24 bit depth and 0.37 Mb size. This result showed that the developed system has no problems for remote doctors to receive and review the patient images immediately on emergent cases.

Management of intra-abdominal abscesses in children with Crohn's disease: a 12-year, retrospective single-center review.

PubMed

Dotson, Jennifer L; Bashaw, Hillary; Nwomeh, Benedict; Crandall, Wallace V

2015-05-01

Intra-abdominal abscesses (IAA) are complications of Crohn's disease, which often result in hospitalization, surgery, and increased cost. Initial management may include medical therapy, percutaneous drainage (PD), or surgery, although the optimal management of IAA in children is unclear. Retrospective review of all pediatric patients with Crohn's disease who developed an IAA from January 1, 2000 to April 30, 2012. Three groups, based on initial IAA treatment modality (medical, PD, and surgery), were compared. Thirty cases of IAA were identified (mean age at IAA diagnosis, 15.4 ± 2.6 yr, 67% female, median Crohn's disease duration, 2.6 mo). Computed tomography was the most common initial (93%) and follow-up (47%) imaging. The average time to follow-up imaging was 8.5 days. For initial management, 18 received medical therapy, 10 PD, and 2 had surgery. The medical therapy group received more computed tomography scans for follow-up imaging than the PD group (12 [67%] versus 2 [20%], P = 0.046). There were no significant differences in abscess characteristics or management of posttreatment course between these 2 groups. Surgical resection occurred in 3 patients (17%) in the medical group and 2 (20%) in the PD group during index hospitalization. No significant differences were identified among treatment groups for readmissions, complications, or abscess recurrence. By 1 year, 12 of the 18 medically managed patients (67%) had surgery, and 6 of the 10 patients (60%) treated with initial PD ultimately had surgery. The majority of patients with IAA require definitive surgical treatment, and there were no clear predictors of those who did not.

A comparative approach to computer aided design model of a dog femur.

PubMed

Turamanlar, O; Verim, O; Karabulut, A

2016-01-01

Computer assisted technologies offer new opportunities in medical imaging and rapid prototyping in biomechanical engineering. Three dimensional (3D) modelling of soft tissues and bones are becoming more important. The accuracy of the analysis in modelling processes depends on the outline of the tissues derived from medical images. The aim of this study is the evaluation of the accuracy of 3D models of a dog femur derived from computed tomography data by using point cloud method and boundary line method on several modelling software. Solidworks, Rapidform and 3DSMax software were used to create 3D models and outcomes were evaluated statistically. The most accurate 3D prototype of the dog femur was created with stereolithography method using rapid prototype device. Furthermore, the linearity of the volumes of models was investigated between software and the constructed models. The difference between the software and real models manifests the sensitivity of the software and the devices used in this manner.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Constructing a simple parametric model of shoulder from medical images

NASA Astrophysics Data System (ADS)

Atmani, H.; Fofi, D.; Merienne, F.; Trouilloud, P.

2006-02-01

The modelling of the shoulder joint is an important step to set a Computer-Aided Surgery System for shoulder prosthesis placement. Our approach mainly concerns the bones structures of the scapulo-humeral joint. Our goal is to develop a tool that allows the surgeon to extract morphological data from medical images in order to interpret the biomechanical behaviour of a prosthesised shoulder for preoperative and peroperative virtual surgery. To provide a light and easy-handling representation of the shoulder, a geometrical model composed of quadrics, planes and other simple forms is proposed.

Cloud solution for histopathological image analysis using region of interest based compression.

PubMed

Kanakatte, Aparna; Subramanya, Rakshith; Delampady, Ashik; Nayak, Rajarama; Purushothaman, Balamuralidhar; Gubbi, Jayavardhana

2017-07-01

Recent technological gains have led to the adoption of innovative cloud based solutions in medical imaging field. Once the medical image is acquired, it can be viewed, modified, annotated and shared on many devices. This advancement is mainly due to the introduction of Cloud computing in medical domain. Tissue pathology images are complex and are normally collected at different focal lengths using a microscope. The single whole slide image contains many multi resolution images stored in a pyramidal structure with the highest resolution image at the base and the smallest thumbnail image at the top of the pyramid. Highest resolution image will be used for tissue pathology diagnosis and analysis. Transferring and storing such huge images is a big challenge. Compression is a very useful and effective technique to reduce the size of these images. As pathology images are used for diagnosis, no information can be lost during compression (lossless compression). A novel method of extracting the tissue region and applying lossless compression on this region and lossy compression on the empty regions has been proposed in this paper. The resulting compression ratio along with lossless compression on tissue region is in acceptable range allowing efficient storage and transmission to and from the Cloud.

Imaging of Traumatic Brain Injury.

PubMed

Bodanapally, Uttam K; Sours, Chandler; Zhuo, Jiachen; Shanmuganathan, Kathirkamanathan

2015-07-01

Imaging plays an important role in the management of patients with traumatic brain injury (TBI). Computed tomography (CT) is the first-line imaging technique allowing rapid detection of primary structural brain lesions that require surgical intervention. CT also detects various deleterious secondary insults allowing early medical and surgical management. Serial imaging is critical to identifying secondary injuries. MR imaging is indicated in patients with acute TBI when CT fails to explain neurologic findings. However, MR imaging is superior in patients with subacute and chronic TBI and also predicts neurocognitive outcome. Copyright © 2015 Elsevier Inc. All rights reserved.

MITK-OpenIGTLink for combining open-source toolkits in real-time computer-assisted interventions.

PubMed

Klemm, Martin; Kirchner, Thomas; Gröhl, Janek; Cheray, Dominique; Nolden, Marco; Seitel, Alexander; Hoppe, Harald; Maier-Hein, Lena; Franz, Alfred M

2017-03-01

Due to rapid developments in the research areas of medical imaging, medical image processing and robotics, computer-assisted interventions (CAI) are becoming an integral part of modern patient care. From a software engineering point of view, these systems are highly complex and research can benefit greatly from reusing software components. This is supported by a number of open-source toolkits for medical imaging and CAI such as the medical imaging interaction toolkit (MITK), the public software library for ultrasound imaging research (PLUS) and 3D Slicer. An independent inter-toolkit communication such as the open image-guided therapy link (OpenIGTLink) can be used to combine the advantages of these toolkits and enable an easier realization of a clinical CAI workflow. MITK-OpenIGTLink is presented as a network interface within MITK that allows easy to use, asynchronous two-way messaging between MITK and clinical devices or other toolkits. Performance and interoperability tests with MITK-OpenIGTLink were carried out considering the whole CAI workflow from data acquisition over processing to visualization. We present how MITK-OpenIGTLink can be applied in different usage scenarios. In performance tests, tracking data were transmitted with a frame rate of up to 1000 Hz and a latency of 2.81 ms. Transmission of images with typical ultrasound (US) and greyscale high-definition (HD) resolutions of [Formula: see text] and [Formula: see text] is possible at up to 512 and 128 Hz, respectively. With the integration of OpenIGTLink into MITK, this protocol is now supported by all established open-source toolkits in the field. This eases interoperability between MITK and toolkits such as PLUS or 3D Slicer and facilitates cross-toolkit research collaborations. MITK and its submodule MITK-OpenIGTLink are provided open source under a BSD-style licence ( http://mitk.org ).

Supporting medical communication for older patients with a shared touch-screen computer.

PubMed

Piper, Anne Marie; Hollan, James D

2013-11-01

Increasingly health care facilities are adopting electronic medical record systems and installing computer workstations in patient exam rooms. The introduction of computer workstations into the medical interview process makes it important to consider the impact of such technology on older patients as well as new types of interfaces that may better suit the needs of older adults. While many older adults are comfortable with a traditional computer workstation with a keyboard and mouse, this article explores how a large horizontal touch-screen (i.e., a surface computer) may suit the needs of older patients and facilitates the doctor-patient interview process. Twenty older adults (age 60 to 88) used a prototype multiuser, multitouch system in our research laboratory to examine seven health care scenarios. Behavioral observations as well as results from questionnaires and a structured interview were analyzed. The older adults quickly adapted to the prototype system and reported that it was easy to use. Participants also suggested that having a shared view of one's medical records, especially charts and images, would enhance communication with their doctor and aid understanding. While this study is exploratory and some areas of interaction with a surface computer need to be refined, the technology is promising for sharing electronic patient information during medical interviews involving older adults. Future work must examine doctors' and nurses' interaction with the technology as well as logistical issues of installing such a system in a real world medical setting. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

SU-E-J-91: FFT Based Medical Image Registration Using a Graphics Processing Unit (GPU).

PubMed

Luce, J; Hoggarth, M; Lin, J; Block, A; Roeske, J

2012-06-01

To evaluate the efficiency gains obtained from using a Graphics Processing Unit (GPU) to perform a Fourier Transform (FT) based image registration. Fourier-based image registration involves obtaining the FT of the component images, and analyzing them in Fourier space to determine the translations and rotations of one image set relative to another. An important property of FT registration is that by enlarging the images (adding additional pixels), one can obtain translations and rotations with sub-pixel resolution. The expense, however, is an increased computational time. GPUs may decrease the computational time associated with FT image registration by taking advantage of their parallel architecture to perform matrix computations much more efficiently than a Central Processor Unit (CPU). In order to evaluate the computational gains produced by a GPU, images with known translational shifts were utilized. A program was written in the Interactive Data Language (IDL; Exelis, Boulder, CO) to performCPU-based calculations. Subsequently, the program was modified using GPU bindings (Tech-X, Boulder, CO) to perform GPU-based computation on the same system. Multiple image sizes were used, ranging from 256×256 to 2304×2304. The time required to complete the full algorithm by the CPU and GPU were benchmarked and the speed increase was defined as the ratio of the CPU-to-GPU computational time. The ratio of the CPU-to- GPU time was greater than 1.0 for all images, which indicates the GPU is performing the algorithm faster than the CPU. The smallest improvement, a 1.21 ratio, was found with the smallest image size of 256×256, and the largest speedup, a 4.25 ratio, was observed with the largest image size of 2304×2304. GPU programming resulted in a significant decrease in computational time associated with a FT image registration algorithm. The inclusion of the GPU may provide near real-time, sub-pixel registration capability. © 2012 American Association of Physicists in Medicine.

Children's (Pediatric) Magnetic Resonance Imaging

MedlinePlus Videos and Cool Tools

... ultrasound), computed tomography and catheter angiography to provide information both before and after treatment. Cardiac MRI may ... use it after appropriate pre-medication. For more information on adverse reactions to gadolinium-based contrast agents, ...

Development of an organ-specific insert phantom generated using a 3D printer for investigations of cardiac computed tomography protocols.

PubMed

Abdullah, Kamarul A; McEntee, Mark F; Reed, Warren; Kench, Peter L

2018-04-30

An ideal organ-specific insert phantom should be able to simulate the anatomical features with appropriate appearances in the resultant computed tomography (CT) images. This study investigated a 3D printing technology to develop a novel and cost-effective cardiac insert phantom derived from volumetric CT image datasets of anthropomorphic chest phantom. Cardiac insert volumes were segmented from CT image datasets, derived from an anthropomorphic chest phantom of Lungman N-01 (Kyoto Kagaku, Japan). These segmented datasets were converted to a virtual 3D-isosurface of heart-shaped shell, while two other removable inserts were included using computer-aided design (CAD) software program. This newly designed cardiac insert phantom was later printed by using a fused deposition modelling (FDM) process via a Creatbot DM Plus 3D printer. Then, several selected filling materials, such as contrast media, oil, water and jelly, were loaded into designated spaces in the 3D-printed phantom. The 3D-printed cardiac insert phantom was positioned within the anthropomorphic chest phantom and 30 repeated CT acquisitions performed using a multi-detector scanner at 120-kVp tube potential. Attenuation (Hounsfield Unit, HU) values were measured and compared to the image datasets of real-patient and Catphan ® 500 phantom. The output of the 3D-printed cardiac insert phantom was a solid acrylic plastic material, which was strong, light in weight and cost-effective. HU values of the filling materials were comparable to the image datasets of real-patient and Catphan ® 500 phantom. A novel and cost-effective cardiac insert phantom for anthropomorphic chest phantom was developed using volumetric CT image datasets with a 3D printer. Hence, this suggested the printing methodology could be applied to generate other phantoms for CT imaging studies. © 2018 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.

Constructing and Classifying Email Networks from Raw Forensic Images

DTIC Science & Technology

2016-09-01

data mining for sequence and pattern mining ; in medical imaging for image segmentation; and in computer vision for object recognition” [28]. 2.3.1...machine learning and data mining suite that is written in Python. It provides a platform for experiment selection, recommendation systems, and...predictivemod- eling. The Orange library is a hierarchically-organized toolbox of data mining components. Data filtering and probability assessment are at the

Dental computed tomographic imaging as age estimation: morphological analysis of the third molar of a group of Turkish population.

PubMed

Cantekin, Kenan; Sekerci, Ahmet Ercan; Buyuk, Suleyman Kutalmis

2013-12-01

Computed tomography (CT) is capable of providing accurate and measurable 3-dimensional images of the third molar. The aims of this study were to analyze the development of the mandibular third molar and its relation to chronological age and to create new reference data for a group of Turkish participants aged 9 to 25 years on the basis of cone-beam CT images. All data were obtained from the patients' records including medical, social, and dental anamnesis and cone-beam CT images of 752 patients. Linear regression analysis was performed to obtain regression formulas for dental age calculation with chronological age and to determine the coefficient of determination (r) for each sex. Statistical analysis showed a strong correlation between age and third-molar development for the males (r2 = 0.80) and the females (r2 = 0.78). Computed tomographic images are clinically useful for accurate and reliable estimation of dental ages of children and youth.

D Reconstruction from Multi-View Medical X-Ray Images - Review and Evaluation of Existing Methods

NASA Astrophysics Data System (ADS)

Hosseinian, S.; Arefi, H.

2015-12-01

The 3D concept is extremely important in clinical studies of human body. Accurate 3D models of bony structures are currently required in clinical routine for diagnosis, patient follow-up, surgical planning, computer assisted surgery and biomechanical applications. However, 3D conventional medical imaging techniques such as computed tomography (CT) scan and magnetic resonance imaging (MRI) have serious limitations such as using in non-weight-bearing positions, costs and high radiation dose(for CT). Therefore, 3D reconstruction methods from biplanar X-ray images have been taken into consideration as reliable alternative methods in order to achieve accurate 3D models with low dose radiation in weight-bearing positions. Different methods have been offered for 3D reconstruction from X-ray images using photogrammetry which should be assessed. In this paper, after demonstrating the principles of 3D reconstruction from X-ray images, different existing methods of 3D reconstruction of bony structures from radiographs are classified and evaluated with various metrics and their advantages and disadvantages are mentioned. Finally, a comparison has been done on the presented methods with respect to several metrics such as accuracy, reconstruction time and their applications. With regards to the research, each method has several advantages and disadvantages which should be considered for a specific application.

A novel strategy for load balancing of distributed medical applications.

PubMed

Logeswaran, Rajasvaran; Chen, Li-Choo

2012-04-01

Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.

Exploitation of realistic computational anthropomorphic phantoms for the optimization of nuclear imaging acquisition and processing protocols.

PubMed

Loudos, George K; Papadimitroulas, Panagiotis G; Kagadis, George C

2014-01-01

Monte Carlo (MC) simulations play a crucial role in nuclear medical imaging since they can provide the ground truth for clinical acquisitions, by integrating and quantifing all physical parameters that affect image quality. The last decade a number of realistic computational anthropomorphic models have been developed to serve imaging, as well as other biomedical engineering applications. The combination of MC techniques with realistic computational phantoms can provide a powerful tool for pre and post processing in imaging, data analysis and dosimetry. This work aims to create a global database for simulated Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) exams and the methodology, as well as the first elements are presented. Simulations are performed using the well validated GATE opensource toolkit, standard anthropomorphic phantoms and activity distribution of various radiopharmaceuticals, derived from literature. The resulting images, projections and sinograms of each study are provided in the database and can be further exploited to evaluate processing and reconstruction algorithms. Patient studies using different characteristics are included in the database and different computational phantoms were tested for the same acquisitions. These include the XCAT, Zubal and the Virtual Family, which some of which are used for the first time in nuclear imaging. The created database will be freely available and our current work is towards its extension by simulating additional clinical pathologies.

The method for detecting small lesions in medical image based on sliding window

NASA Astrophysics Data System (ADS)

Han, Guilai; Jiao, Yuan

2016-10-01

At present, the research on computer-aided diagnosis includes the sample image segmentation, extracting visual features, generating the classification model by learning, and according to the model generated to classify and judge the inspected images. However, this method has a large scale of calculation and speed is slow. And because medical images are usually low contrast, when the traditional image segmentation method is applied to the medical image, there is a complete failure. As soon as possible to find the region of interest, improve detection speed, this topic attempts to introduce the current popular visual attention model into small lesions detection. However, Itti model is mainly for natural images. But the effect is not ideal when it is used to medical images which usually are gray images. Especially in the early stages of some cancers, the focus of a disease in the whole image is not the most significant region and sometimes is very difficult to be found. But these lesions are prominent in the local areas. This paper proposes a visual attention mechanism based on sliding window, and use sliding window to calculate the significance of a local area. Combined with the characteristics of the lesion, select the features of gray, entropy, corner and edge to generate a saliency map. Then the significant region is segmented and distinguished. This method reduces the difficulty of image segmentation, and improves the detection accuracy of small lesions, and it has great significance to early discovery, early diagnosis and treatment of cancers.

Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.

PubMed

Prescott, Jeffrey William

2013-02-01

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.

Extraction of features from medical images using a modular neural network approach that relies on learning by sample

NASA Astrophysics Data System (ADS)

Brahmi, Djamel; Serruys, Camille; Cassoux, Nathalie; Giron, Alain; Triller, Raoul; Lehoang, Phuc; Fertil, Bernard

2000-06-01

Medical images provide experienced physicians with meaningful visual stimuli but their features are frequently hard to decipher. The development of a computational model to mimic physicians' expertise is a demanding task, especially if a significant and sophisticated preprocessing of images is required. Learning from well-expertised images may be a more convenient approach, inasmuch a large and representative bunch of samples is available. A four-stage approach has been designed, which combines image sub-sampling with unsupervised image coding, supervised classification and image reconstruction in order to directly extract medical expertise from raw images. The system has been applied (1) to the detection of some features related to the diagnosis of black tumors of skin (a classification issue) and (2) to the detection of virus-infected and healthy areas in retina angiography in order to locate precisely the border between them and characterize the evolution of infection. For reasonably balanced training sets, we are able to obtained about 90% correct classification of features (black tumors). Boundaries generated by our system mimic reproducibility of hand-outlines drawn by experts (segmentation of virus-infected area).

Web-based continuing medical education. (II): Evaluation study of computer-mediated continuing medical education.

PubMed

Curran, V R; Hoekman, T; Gulliver, W; Landells, I; Hatcher, L

2000-01-01

Over the years, various distance learning technologies and methods have been applied to the continuing medical education needs of rural and remote physicians. They have included audio teleconferencing, slow scan imaging, correspondence study, and compressed videoconferencing. The recent emergence and growth of Internet, World Wide Web (Web), and compact disk read-only-memory (CD-ROM) technologies have introduced new opportunities for providing continuing education to the rural medical practitioner. This evaluation study assessed the instructional effectiveness of a hybrid computer-mediated courseware delivery system on dermatologic office procedures. A hybrid delivery system merges Web documents, multimedia, computer-mediated communications, and CD-ROMs to enable self-paced instruction and collaborative learning. Using a modified pretest to post-test control group study design, several evaluative criteria (participant reaction, learning achievement, self-reported performance change, and instructional transactions) were assessed by various qualitative and quantitative data collection methods. This evaluation revealed that a hybrid computer-mediated courseware system was an effective means for increasing knowledge (p < .05) and improving self-reported competency (p < .05) in dermatologic office procedures, and that participants were very satisfied with the self-paced instruction and use of asynchronous computer conferencing for collaborative information sharing among colleagues.

Analyzing Dental Implant Sites From Cone Beam Computed Tomography Scans on a Tablet Computer: A Comparative Study Between iPad and 3 Display Systems.

PubMed

Carrasco, Alejandro; Jalali, Elnaz; Dhingra, Ajay; Lurie, Alan; Yadav, Sumit; Tadinada, Aditya

2017-06-01

The aim of this study was to compare a medical-grade PACS (picture archiving and communication system) monitor, a consumer-grade monitor, a laptop computer, and a tablet computer for linear measurements of height and width for specific implant sites in the posterior maxilla and mandible, along with visualization of the associated anatomical structures. Cone beam computed tomography (CBCT) scans were evaluated. The images were reviewed using PACS-LCD monitor, consumer-grade LCD monitor using CB-Works software, a 13″ MacBook Pro, and an iPad 4 using OsiriX DICOM reader software. The operators had to identify anatomical structures in each display using a 2-point scale. User experience between PACS and iPad was also evaluated by means of a questionnaire. The measurements were very similar for each device. P-values were all greater than 0.05, indicating no significant difference between the monitors for each measurement. The intraoperator reliability was very high. The user experience was similar in each category with the most significant difference regarding the portability where the PACS display received the lowest score and the iPad received the highest score. The iPad with retina display was comparable with the medical-grade monitor, producing similar measurements and image visualization, and thus providing an inexpensive, portable, and reliable screen to analyze CBCT images in the operating room during the implant surgery.

An Integrated Teaching Method of Gross Anatomy and Computed Tomography Radiology

ERIC Educational Resources Information Center

Murakami, Tohru; Tajika, Yuki; Ueno, Hitoshi; Awata, Sachiko; Hirasawa, Satoshi; Sugimoto, Maki; Kominato, Yoshihiko; Tsushima, Yoshito; Endo, Keigo; Yorifuji, Hiroshi

2014-01-01

It is essential for medical students to learn and comprehend human anatomy in three dimensions (3D). With this in mind, a new system was designed in order to integrate anatomical dissections with diagnostic computed tomography (CT) radiology. Cadavers were scanned by CT scanners, and students then consulted the postmortem CT images during cadaver…

High-performance web viewer for cardiac images

NASA Astrophysics Data System (ADS)

dos Santos, Marcelo; Furuie, Sergio S.

2004-04-01

With the advent of the digital devices for medical diagnosis the use of the regular films in radiology has decreased. Thus, the management and handling of medical images in digital format has become an important and critical task. In Cardiology, for example, the main difficulty is to display dynamic images with the appropriated color palette and frame rate used on acquisition process by Cath, Angio and Echo systems. In addition, other difficulty is handling large images in memory by any existing personal computer, including thin clients. In this work we present a web-based application that carries out these tasks with robustness and excellent performance, without burdening the server and network. This application provides near-diagnostic quality display of cardiac images stored as DICOM 3.0 files via a web browser and provides a set of resources that allows the viewing of still and dynamic images. It can access image files from the local disks, or network connection. Its features include: allows real-time playback, dynamic thumbnails image viewing during loading, access to patient database information, image processing tools, linear and angular measurements, on-screen annotations, image printing and exporting DICOM images to other image formats, and many others, all characterized by a pleasant user-friendly interface, inside a Web browser by means of a Java application. This approach offers some advantages over the most of medical images viewers, such as: facility of installation, integration with other systems by means of public and standardized interfaces, platform independence, efficient manipulation and display of medical images, all with high performance.

NASA Tech Briefs, April 2000. Volume 24, No. 4

NASA Technical Reports Server (NTRS)

2000-01-01

Topics covered include: Imaging/Video/Display Technology; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Bio-Medical; Test and Measurement; Mathematics and Information Sciences; Books and Reports.

Skin Texture Recognition using Medical Diagnosis

NASA Astrophysics Data System (ADS)

Munshi, Anindita; Parekh, Ranjan

2010-10-01

This paper proposes an automated system for recognizing disease conditions of human skin in context to medical diagnosis. The disease conditions are recognized by analyzing skin texture images using a set of normalized symmetrical Grey Level Co occurrence Matrices (GLCM). GLCM defines the probability of grey level i occurring in the neighborhood of another grey level j at a distance d in directionθ. Directional GLCMs are computed along four directions: horizontal (θ = 0°), vertical (θ = 90°), right diagonal (θ = 45°) and left diagonal (θ = 135°), and a set of features viz. Contrast, Homogeneity and Energy computed from each, are averaged to provide an estimation of the texture class. The system is tested using 225 images pertaining to three dermatological skin conditions viz. dermatitis, eczema, urticaria. An accuracy of 94.81% is obtained using a multilayer perceptron (MLP) as a classifier.

Craniofacial imaging informatics and technology development.

PubMed

Vannier, M W

2003-01-01

'Craniofacial imaging informatics' refers to image and related scientific data from the dentomaxillofacial complex, and application of 'informatics techniques' (derived from disciplines such as applied mathematics, computer science and statistics) to understand and organize the information associated with the data. Major trends in information technology determine the progress made in craniofacial imaging and informatics. These trends include industry consolidation, disruptive technologies, Moore's law, electronic atlases and on-line databases. Each of these trends is explained and documented, relative to their influence on craniofacial imaging. Craniofacial imaging is influenced by major trends that affect all medical imaging and related informatics applications. The introduction of cone beam craniofacial computed tomography scanners is an example of a disruptive technology entering the field. An important opportunity lies in the integration of biologic knowledge repositories with craniofacial images. The progress of craniofacial imaging will continue subject to limitations imposed by the underlying technologies, especially imaging informatics. Disruptive technologies will play a major role in the evolution of this field.

Imaging biomarkers in multiple Sclerosis: From image analysis to population imaging.

PubMed

Barillot, Christian; Edan, Gilles; Commowick, Olivier

2016-10-01

The production of imaging data in medicine increases more rapidly than the capacity of computing models to extract information from it. The grand challenges of better understanding the brain, offering better care for neurological disorders, and stimulating new drug design will not be achieved without significant advances in computational neuroscience. The road to success is to develop a new, generic, computational methodology and to confront and validate this methodology on relevant diseases with adapted computational infrastructures. This new concept sustains the need to build new research paradigms to better understand the natural history of the pathology at the early phase; to better aggregate data that will provide the most complete representation of the pathology in order to better correlate imaging with other relevant features such as clinical, biological or genetic data. In this context, one of the major challenges of neuroimaging in clinical neurosciences is to detect quantitative signs of pathological evolution as early as possible to prevent disease progression, evaluate therapeutic protocols or even better understand and model the natural history of a given neurological pathology. Many diseases encompass brain alterations often not visible on conventional MRI sequences, especially in normal appearing brain tissues (NABT). MRI has often a low specificity for differentiating between possible pathological changes which could help in discriminating between the different pathological stages or grades. The objective of medical image analysis procedures is to define new quantitative neuroimaging biomarkers to track the evolution of the pathology at different levels. This paper illustrates this issue in one acute neuro-inflammatory pathology: Multiple Sclerosis (MS). It exhibits the current medical image analysis approaches and explains how this field of research will evolve in the next decade to integrate larger scale of information at the temporal, cellular, structural and morphological levels. Copyright © 2016 Elsevier B.V. All rights reserved.

High resolution bone mineral densitometry with a gamma camera

NASA Technical Reports Server (NTRS)

Leblanc, A.; Evans, H.; Jhingran, S.; Johnson, P.

1983-01-01

A technique by which the regional distribution of bone mineral can be determined in bone samples from small animals is described. The technique employs an Anger camera interfaced to a medical computer. High resolution imaging is possible by producing magnified images of the bone samples. Regional densitometry of femurs from oophorectomised and bone mineral loss.

Complementary concept for an image archive and communication system in a cardiological department based on CD-medical, an online archive, and networking facilities

NASA Astrophysics Data System (ADS)

Oswald, Helmut; Mueller-Jones, Kay; Builtjes, Jan; Fleck, Eckart

1998-07-01

The developments in information technologies -- computer hardware, networking and storage media -- has led to expectations that these advances make it possible to replace 35 mm film completely by digital techniques in the catheter laboratory. Besides the role of an archival medium, cine film is used as the major image review and exchange medium in cardiology. None of the today technologies can fulfill completely the requirements to replace cine film. One of the major drawbacks of cine film is the single access in time and location. For the four catheter laboratories in our institutions we have designed a complementary concept combining the CD-R, also called CD-medical, as a single patient storage and exchange medium, and a digital archive for on-line access and image review of selected frames or short sequences on adequate medical workstations. The image data from various modalities as well as all digital documents regarding to a patient are part of an electronic patient record. The access, the processing and the display of documents is supported by an integrated medical application.

Anniversary paper. Development of x-ray computed tomography: the role of medical physics and AAPM from the 1970s to present.

PubMed

Pan, Xiaochuan; Siewerdsen, Jeffrey; La Riviere, Patrick J; Kalender, Willi A

2008-08-01

The AAPM, through its members, meetings, and its flagship journal Medical Physics, has played an important role in the development and growth of x-ray tomography in the last 50 years. From a spate of early articles in the 1970s characterizing the first commercial computed tomography (CT) scanners through the "slice wars" of the 1990s and 2000s, the history of CT and related techniques such as tomosynthesis can readily be traced through the pages of Medical Physics and the annals of the AAPM and RSNA/AAPM Annual Meetings. In this article, the authors intend to give a brief review of the role of Medical Physics and the AAPM in CT and tomosynthesis imaging over the last few decades.

An automatic segmentation method of a parameter-adaptive PCNN for medical images.

PubMed

Lian, Jing; Shi, Bin; Li, Mingcong; Nan, Ziwei; Ma, Yide

2017-09-01

Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision. The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter [Formula: see text] for different kinds of images. Secondly, we acquire the parameter [Formula: see text] according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter V of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset [Formula: see text] to improve initial segmentation precision. Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726. The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.

Vision 20/20: Single photon counting x-ray detectors in medical imaging

PubMed Central

Taguchi, Katsuyuki; Iwanczyk, Jan S.

2013-01-01

Photon counting detectors (PCDs) with energy discrimination capabilities have been developed for medical x-ray computed tomography (CT) and x-ray (XR) imaging. Using detection mechanisms that are completely different from the current energy integrating detectors and measuring the material information of the object to be imaged, these PCDs have the potential not only to improve the current CT and XR images, such as dose reduction, but also to open revolutionary novel applications such as molecular CT and XR imaging. The performance of PCDs is not flawless, however, and it seems extremely challenging to develop PCDs with close to ideal characteristics. In this paper, the authors offer our vision for the future of PCD-CT and PCD-XR with the review of the current status and the prediction of (1) detector technologies, (2) imaging technologies, (3) system technologies, and (4) potential clinical benefits with PCDs. PMID:24089889

Classification of large-scale fundus image data sets: a cloud-computing framework.

PubMed

Roychowdhury, Sohini

2016-08-01

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.

Use of medical information by computer networks raises major concerns about privacy.

PubMed Central

OReilly, M

1995-01-01

The development of computer data-bases and long-distance computer networks is leading to improvements in Canada's health care system. However, these developments come at a cost and require a balancing act between access and confidentiality. Columnist Michael OReilly, who in this article explores the security of computer networks, notes that respect for patients' privacy must be given as high a priority as the ability to see their records in the first place. Images p213-a PMID:7600474

HoDOr: histogram of differential orientations for rigid landmark tracking in medical images

NASA Astrophysics Data System (ADS)

Tiwari, Abhishek; Patwardhan, Kedar Anil

2018-03-01

Feature extraction plays a pivotal role in pattern recognition and matching. An ideal feature should be invariant to image transformations such as translation, rotation, scaling, etc. In this work, we present a novel rotation-invariant feature, which is based on Histogram of Oriented Gradients (HOG). We compare performance of the proposed approach with the HOG feature on 2D phantom data, as well as 3D medical imaging data. We have used traditional histogram comparison measures such as Bhattacharyya distance and Normalized Correlation Coefficient (NCC) to assess efficacy of the proposed approach under effects of image rotation. In our experiments, the proposed feature performs 40%, 20%, and 28% better than the HOG feature on phantom (2D), Computed Tomography (CT-3D), and Ultrasound (US-3D) data for image matching, and landmark tracking tasks respectively.

Home teleradiology system

NASA Astrophysics Data System (ADS)

Komo, Darmadi; Garra, Brian S.; Freedman, Matthew T.; Mun, Seong K.

1997-05-01

The Home Teleradiology Server system has been developed and installed at the Department of Radiology, Georgetown University Medical Center. The main purpose of the system is to provide a service for on-call physicians to view patients' medical images at home during off-hours. This service will reduce the overhead time required by on-call physicians to travel to the hospital, thereby increasing the efficiency of patient care and improving the total quality of the health care. Typically when a new case is conducted, the medical images generated from CT, US, and/or MRI modalities are transferred to a central server at the hospital via DICOM messages over an existing hospital network. The server has a DICOM network agent that listens to DICOM messages sent by CT, US, and MRI modalities and stores them into separate DICOM files for sending purposes. The server also has a general purpose, flexible scheduling software that can be configured to send image files to specific user(s) at certain times on any day(s) of the week. The server will then distribute the medical images to on- call physicians' homes via a high-speed modem. All file transmissions occur in the background without human interaction after the scheduling software is pre-configured accordingly. At the receiving end, the physicians' computers consist of high-end workstations that have high-speed modems to receive the medical images sent by the central server from the hospital, and DICOM compatible viewer software to view the transmitted medical images in DICOM format. A technician from the hospital, and DICOM compatible viewer software to view the transmitted medical images in DICOM format. A technician from the hospital will notify the physician(s) after all the image files have been completely sent. The physician(s) will then examine the medical images and decide if it is necessary to travel to the hospital for further examination on the patients. Overall, the Home Teleradiology system provides the on-call physicians with a cost-effective and convenient environment for viewing patients' medical images at home.

Browsing software of the Visible Korean data used for teaching sectional anatomy.

PubMed

Shin, Dong Sun; Chung, Min Suk; Park, Hyo Seok; Park, Jin Seo; Hwang, Sung Bae

2011-01-01

The interpretation of computed tomographs (CTs) and magnetic resonance images (MRIs) to diagnose clinical conditions requires basic knowledge of sectional anatomy. Sectional anatomy has traditionally been taught using sectioned cadavers, atlases, and/or computer software. The computer software commonly used for this subject is practical and efficient for students but could be more advanced. The objective of this research was to present browsing software developed from the Visible Korean images that can be used for teaching sectional anatomy. One thousand seven hundred and two sets of MRIs, CTs, and sectioned images (intervals, one millimeter) of a whole male cadaver were prepared. Over 900 structures in the sectioned images were outlined and then filled with different colors to elaborate each structure. Software was developed where four corresponding images could be displayed simultaneously; in addition, the structures in the image data could be readily recognized with the aid of the color-filled outlines. The software, distributed free of charge, could be a valuable tool to teach medical students. For example, sectional anatomy could be taught by showing the sectioned images with real color and high resolution. Students could then review the lecture by using the sectioned and color-filled images on their own computers. Students could also be evaluated using the same software. Furthermore, other investigators would be able to replace the images for more comprehensive sectional anatomy. Copyright © 2011 Wiley-Liss, Inc.

Dicoogle Mobile: a medical imaging platform for Android.

PubMed

Viana-Ferreira, Carlos; Ferreira, Daniel; Valente, Frederico; Monteiro, Eriksson; Costa, Carlos; Oliveira, José Luís

2012-01-01

Mobile computing technologies are increasingly becoming a valuable asset in healthcare information systems. The adoption of these technologies helps to assist in improving quality of care, increasing productivity and facilitating clinical decision support. They provide practitioners with ubiquitous access to patient records, being actually an important component in telemedicine and tele-work environments. We have developed Dicoogle Mobile, an Android application that provides remote access to distributed medical imaging data through a cloud relay service. Besides, this application has the capability to store and index local imaging data, so that they can also be searched and visualized. In this paper, we will describe Dicoogle Mobile concept as well the architecture of the whole system that makes it running.

Computational Approach for Securing Radiology-Diagnostic Data in Connected Health Network using High-Performance GPU-Accelerated AES.

PubMed

Adeshina, A M; Hashim, R

2017-03-01

Diagnostic radiology is a core and integral part of modern medicine, paving ways for the primary care physicians in the disease diagnoses, treatments and therapy managements. Obviously, all recent standard healthcare procedures have immensely benefitted from the contemporary information technology revolutions, apparently revolutionizing those approaches to acquiring, storing and sharing of diagnostic data for efficient and timely diagnosis of diseases. Connected health network was introduced as an alternative to the ageing traditional concept in healthcare system, improving hospital-physician connectivity and clinical collaborations. Undoubtedly, the modern medicinal approach has drastically improved healthcare but at the expense of high computational cost and possible breach of diagnosis privacy. Consequently, a number of cryptographical techniques are recently being applied to clinical applications, but the challenges of not being able to successfully encrypt both the image and the textual data persist. Furthermore, processing time of encryption-decryption of medical datasets, within a considerable lower computational cost without jeopardizing the required security strength of the encryption algorithm, still remains as an outstanding issue. This study proposes a secured radiology-diagnostic data framework for connected health network using high-performance GPU-accelerated Advanced Encryption Standard. The study was evaluated with radiology image datasets consisting of brain MR and CT datasets obtained from the department of Surgery, University of North Carolina, USA, and the Swedish National Infrastructure for Computing. Sample patients' notes from the University of North Carolina, School of medicine at Chapel Hill were also used to evaluate the framework for its strength in encrypting-decrypting textual data in the form of medical report. Significantly, the framework is not only able to accurately encrypt and decrypt medical image datasets, but it also successfully encrypts and decrypts textual data in Microsoft Word document, Microsoft Excel and Portable Document Formats which are the conventional format of documenting medical records. Interestingly, the entire encryption and decryption procedures were achieved at a lower computational cost using regular hardware and software resources without compromising neither the quality of the decrypted data nor the security level of the algorithms.

Efficient image acquisition design for a cancer detection system

NASA Astrophysics Data System (ADS)

Nguyen, Dung; Roehrig, Hans; Borders, Marisa H.; Fitzpatrick, Kimberly A.; Roveda, Janet

2013-09-01

Modern imaging modalities, such as Computed Tomography (CT), Digital Breast Tomosynthesis (DBT) or Magnetic Resonance Tomography (MRT) are able to acquire volumetric images with an isotropic resolution in micrometer (um) or millimeter (mm) range. When used in interactive telemedicine applications, these raw images need a huge storage unit, thereby necessitating the use of high bandwidth data communication link. To reduce the cost of transmission and enable archiving, especially for medical applications, image compression is performed. Recent advances in compression algorithms have resulted in a vast array of data compression techniques, but because of the characteristics of these images, there are challenges to overcome to transmit these images efficiently. In addition, the recent studies raise the low dose mammography risk on high risk patient. Our preliminary studies indicate that by bringing the compression before the analog-to-digital conversion (ADC) stage is more efficient than other compression techniques after the ADC. The linearity characteristic of the compressed sensing and ability to perform the digital signal processing (DSP) during data conversion open up a new area of research regarding the roles of sparsity in medical image registration, medical image analysis (for example, automatic image processing algorithm to efficiently extract the relevant information for the clinician), further Xray dose reduction for mammography, and contrast enhancement.

Time-Resolved Particle Image Velocimetry Measurements with Wall Shear Stress and Uncertainty Quantification for the FDA Nozzle Model.

PubMed

Raben, Jaime S; Hariharan, Prasanna; Robinson, Ronald; Malinauskas, Richard; Vlachos, Pavlos P

2016-03-01

We present advanced particle image velocimetry (PIV) processing, post-processing, and uncertainty estimation techniques to support the validation of computational fluid dynamics analyses of medical devices. This work is an extension of a previous FDA-sponsored multi-laboratory study, which used a medical device mimicking geometry referred to as the FDA benchmark nozzle model. Experimental measurements were performed using time-resolved PIV at five overlapping regions of the model for Reynolds numbers in the nozzle throat of 500, 2000, 5000, and 8000. Images included a twofold increase in spatial resolution in comparison to the previous study. Data was processed using ensemble correlation, dynamic range enhancement, and phase correlations to increase signal-to-noise ratios and measurement accuracy, and to resolve flow regions with large velocity ranges and gradients, which is typical of many blood-contacting medical devices. Parameters relevant to device safety, including shear stress at the wall and in bulk flow, were computed using radial basis functions. In addition, in-field spatially resolved pressure distributions, Reynolds stresses, and energy dissipation rates were computed from PIV measurements. Velocity measurement uncertainty was estimated directly from the PIV correlation plane, and uncertainty analysis for wall shear stress at each measurement location was performed using a Monte Carlo model. Local velocity uncertainty varied greatly and depended largely on local conditions such as particle seeding, velocity gradients, and particle displacements. Uncertainty in low velocity regions in the sudden expansion section of the nozzle was greatly reduced by over an order of magnitude when dynamic range enhancement was applied. Wall shear stress uncertainty was dominated by uncertainty contributions from velocity estimations, which were shown to account for 90-99% of the total uncertainty. This study provides advancements in the PIV processing methodologies over the previous work through increased PIV image resolution, use of robust image processing algorithms for near-wall velocity measurements and wall shear stress calculations, and uncertainty analyses for both velocity and wall shear stress measurements. The velocity and shear stress analysis, with spatially distributed uncertainty estimates, highlights the challenges of flow quantification in medical devices and provides potential methods to overcome such challenges.

Image-guided interventional procedures in the dog and cat.

PubMed

Vignoli, Massimo; Saunders, Jimmy H

2011-03-01

Medical imaging is essential for the diagnostic workup of many soft tissue and bone lesions in dogs and cats, but imaging modalities do not always allow the clinician to differentiate inflammatory or infectious conditions from neoplastic disorders. This review describes interventional procedures in dogs and cats for collection of samples for cytological or histopathological examinations under imaging guidance. It describes the indications and procedures for imaging-guided sampling, including ultrasound (US), computed tomography (CT), magnetic resonance imaging and fluoroscopy. US and CT are currently the modalities of choice in interventional imaging. Copyright © 2009 Elsevier Ltd. All rights reserved.

Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.

PubMed

Ahmad, Asif; Asif, Amina; Rajpoot, Nasir; Arif, Muhammad; Minhas, Fayyaz Ul Amir Afsar

2017-11-21

Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist .

Parallel Processing of Images in Mobile Devices using BOINC

NASA Astrophysics Data System (ADS)

Curiel, Mariela; Calle, David F.; Santamaría, Alfredo S.; Suarez, David F.; Flórez, Leonardo

2018-04-01

Medical image processing helps health professionals make decisions for the diagnosis and treatment of patients. Since some algorithms for processing images require substantial amounts of resources, one could take advantage of distributed or parallel computing. A mobile grid can be an adequate computing infrastructure for this problem. A mobile grid is a grid that includes mobile devices as resource providers. In a previous step of this research, we selected BOINC as the infrastructure to build our mobile grid. However, parallel processing of images in mobile devices poses at least two important challenges: the execution of standard libraries for processing images and obtaining adequate performance when compared to desktop computers grids. By the time we started our research, the use of BOINC in mobile devices also involved two issues: a) the execution of programs in mobile devices required to modify the code to insert calls to the BOINC API, and b) the division of the image among the mobile devices as well as its merging required additional code in some BOINC components. This article presents answers to these four challenges.

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation

PubMed Central

Lee, Jae H.; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T.; Seo, Youngho

2014-01-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting. PMID:27081299

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation.

PubMed

Lee, Jae H; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T; Seo, Youngho

2014-11-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting.

The Burn Medical Assistant: Developing Machine Learning Algorithms to Aid in the Estimation of Burn Wound Size

DTIC Science & Technology

2017-10-01

hypothesis that a computer machine learning algorithm can analyze and classify burn injures using multispectral imaging within 5% of an expert clinician...morbidity. In response to these challenges, the USAISR developed and obtained FDA 510(k) clearance of the Burn Navigator™, a computer decision support... computer decision support software (CDSS), can significantly change the CDSS algorithm’s recommendations and thus the total fluid administered to a

Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography

NASA Astrophysics Data System (ADS)

Ota, Junko; Umehara, Kensuke; Ishimaru, Naoki; Ohno, Shunsuke; Okamoto, Kentaro; Suzuki, Takanori; Shirai, Naoki; Ishida, Takayuki

2017-02-01

As the capability of high-resolution displays grows, high-resolution images are often required in Computed Tomography (CT). However, acquiring high-resolution images takes a higher radiation dose and a longer scanning time. In this study, we applied the Sparse-coding-based Super-Resolution (ScSR) method to generate high-resolution images without increasing the radiation dose. We prepared the over-complete dictionary learned the mapping between low- and highresolution patches and seek a sparse representation of each patch of the low-resolution input. These coefficients were used to generate the high-resolution output. For evaluation, 44 CT cases were used as the test dataset. We up-sampled images up to 2 or 4 times and compared the image quality of the ScSR scheme and bilinear and bicubic interpolations, which are the traditional interpolation schemes. We also compared the image quality of three learning datasets. A total of 45 CT images, 91 non-medical images, and 93 chest radiographs were used for dictionary preparation respectively. The image quality was evaluated by measuring peak signal-to-noise ratio (PSNR) and structure similarity (SSIM). The differences of PSNRs and SSIMs between the ScSR method and interpolation methods were statistically significant. Visual assessment confirmed that the ScSR method generated a high-resolution image with sharpness, whereas conventional interpolation methods generated over-smoothed images. To compare three different training datasets, there were no significance between the CT, the CXR and non-medical datasets. These results suggest that the ScSR provides a robust approach for application of up-sampling CT images and yields substantial high image quality of extended images in CT.

Implementation and applications of dual-modality imaging

NASA Astrophysics Data System (ADS)

Hasegawa, Bruce H.; Barber, William C.; Funk, Tobias; Hwang, Andrew B.; Taylor, Carmen; Sun, Mingshan; Seo, Youngho

2004-06-01

In medical diagnosis, functional or physiological data can be acquired using radionuclide imaging with positron emission tomography or with single-photon emission computed tomography. However, anatomical or structural data can be acquired using X-ray computed tomography. In dual-modality imaging, both radionuclide and X-ray detectors are incorporated in an imaging system to allow both functional and structural data to be acquired in a single procedure without removing the patient from the imaging system. In a clinical setting, dual-modality imaging systems commonly are used to localize radiopharmaceutical uptake with respect to the patient's anatomy. This helps the clinician to differentiate disease from regions of normal radiopharmaceutical accumulation, to improve diagnosis or cancer staging, or to facilitate planning for radiation therapy or surgery. While initial applications of dual-modality imaging were developed for clinical imaging on humans, it now is recognized that these systems have potentially important applications for imaging small animals involved in experimental studies including basic investigations of mammalian biology and development of new pharmaceuticals for diagnosis or treatment of disease.

Picture This... Developing Standards for Electronic Images at the National Library of Medicine

PubMed Central

Masys, Daniel R.

1990-01-01

New computer technologies have made it feasible to represent, store, and communicate high resolution biomedical images via electronic means. Traditional two dimensional medical images such as those on printed pages have been supplemented by three dimensional images which can be rendered, rotated, and “dissected” from any point of view. The library of the future will provide electronic access not only to words and numbers, but to pictures, sounds, and other nontextual information. There currently exist few widely-accepted standards for the representation and communication of complex images, yet such standards will be critical to the feasibility and usefulness of digital image collections in the life sciences. The National Library of Medicine is embarked on a project to develop a complete digital volumetric representation of an adult human male and female. This “Visible Human Project” will address the issue of standards for computer representation of biological structure.

Computer-Aided Evaluation of Blood Vessel Geometry From Acoustic Images.

PubMed

Lindström, Stefan B; Uhlin, Fredrik; Bjarnegård, Niclas; Gylling, Micael; Nilsson, Kamilla; Svensson, Christina; Yngman-Uhlin, Pia; Länne, Toste

2018-04-01

A method for computer-aided assessment of blood vessel geometries based on shape-fitting algorithms from metric vision was evaluated. Acoustic images of cross sections of the radial artery and cephalic vein were acquired, and medical practitioners used a computer application to measure the wall thickness and nominal diameter of these blood vessels with a caliper method and the shape-fitting method. The methods performed equally well for wall thickness measurements. The shape-fitting method was preferable for measuring the diameter, since it reduced systematic errors by up to 63% in the case of the cephalic vein because of its eccentricity. © 2017 by the American Institute of Ultrasound in Medicine.

Three-dimensional surface reconstruction for industrial computed tomography

NASA Technical Reports Server (NTRS)

Vannier, M. W.; Knapp, R. H.; Gayou, D. E.; Sammon, N. P.; Butterfield, R. L.; Larson, J. W.

1985-01-01

Modern high resolution medical computed tomography (CT) scanners can produce geometrically accurate sectional images of many types of industrial objects. Computer software has been developed to convert serial CT scans into a three-dimensional surface form, suitable for display on the scanner itself. This software, originally developed for imaging the skull, has been adapted for application to industrial CT scanning, where serial CT scans thrrough an object of interest may be reconstructed to demonstrate spatial relationships in three dimensions that cannot be easily understood using the original slices. The methods of three-dimensional reconstruction and solid modeling are reviewed, and reconstruction in three dimensions from CT scans through familiar objects is demonstrated.

Fusion Imaging: A Novel Staging Modality in Testis Cancer

PubMed Central

Sterbis, Joseph R.; Rice, Kevin R.; Javitt, Marcia C.; Schenkman, Noah S.; Brassell, Stephen A.

2010-01-01

Objective: Computed tomography and chest radiographs provide the standard imaging for staging, treatment, and surveillance of testicular germ cell neoplasms. Positron emission tomography has recently been utilized for staging, but is somewhat limited in its ability to provide anatomic localization. Fusion imaging combines the metabolic information provided by positron emission tomography with the anatomic precision of computed tomography. To the best of our knowledge, this represents the first study of the effectiveness using fusion imaging in evaluation of patients with testis cancer. Methods: A prospective study of 49 patients presenting to Walter Reed Army Medical Center with testicular cancer from 2003 to 2009 was performed. Fusion imaging was compared with conventional imaging, tumor markers, pathologic results, and clinical follow-up. Results: There were 14 true positives, 33 true negatives, 1 false positive, and 1 false negative. Sensitivity, specificity, positive predictive value, and negative predictive value were 93.3, 97.0, 93.3, and 97.0% respectively. In 11 patient scenarios, fusion imaging differed from conventional imaging. Utility was found in superior lesion detection compared to helical computed tomography due to anatomical/functional image co-registration, detection of micrometastasis in lymph nodes (pathologic nodes < 1cm), surveillance for recurrence post-chemotherapy, differentiating fibrosis from active disease in nodes < 2.5cm, and acting as a quality assurance measure to computed tomography alone. Conclusions: In addition to demonstrating a sensitivity and specificity comparable or superior to conventional imaging, fusion imaging shows promise in providing additive data that may assist in clinical decision-making. PMID:21103077

Fusion imaging: a novel staging modality in testis cancer.

PubMed

Sterbis, Joseph R; Rice, Kevin R; Javitt, Marcia C; Schenkman, Noah S; Brassell, Stephen A

2010-11-05

Computed tomography and chest radiographs provide the standard imaging for staging, treatment, and surveillance of testicular germ cell neoplasms. Positron emission tomography has recently been utilized for staging, but is somewhat limited in its ability to provide anatomic localization. Fusion imaging combines the metabolic information provided by positron emission tomography with the anatomic precision of computed tomography. To the best of our knowledge, this represents the first study of the effectiveness using fusion imaging in evaluation of patients with testis cancer. A prospective study of 49 patients presenting to Walter Reed Army Medical Center with testicular cancer from 2003 to 2009 was performed. Fusion imaging was compared with conventional imaging, tumor markers, pathologic results, and clinical follow-up. There were 14 true positives, 33 true negatives, 1 false positive, and 1 false negative. Sensitivity, specificity, positive predictive value, and negative predictive value were 93.3, 97.0, 93.3, and 97.0% respectively. In 11 patient scenarios, fusion imaging differed from conventional imaging. Utility was found in superior lesion detection compared to helical computed tomography due to anatomical/functional image co-registration, detection of micrometastasis in lymph nodes (pathologic nodes < 1cm), surveillance for recurrence post-chemotherapy, differentiating fibrosis from active disease in nodes < 2.5cm, and acting as a quality assurance measure to computed tomography alone. In addition to demonstrating a sensitivity and specificity comparable or superior to conventional imaging, fusion imaging shows promise in providing additive data that may assist in clinical decision-making.

Paradigms of perception in clinical practice.

PubMed

Jacobson, Francine L; Berlanstein, Bruce P; Andriole, Katherine P

2006-06-01

Display strategies for medical images in radiology have evolved in tandem with the technology by which images are made. The close of the 20th century, nearly coincident with the 100th anniversary of the discovery of x-rays, brought radiologists to a new crossroad in the evolution of image display. The increasing availability, speed, and flexibility of computer technology can now revolutionize how images are viewed and interpreted. Radiologists are not yet in agreement regarding the next paradigm for image display. The possibilities are being explored systematically through the Society for Computer Applications in Radiology's Transforming the Radiological Interpretation Process initiative. The varied input of radiologists who work in a large variety of settings will enable new display strategies to best serve radiologists in the detection and quantification of disease. Considerations and possibilities for the future are presented in this paper.

Fiber Optic Communication System For Medical Images

NASA Astrophysics Data System (ADS)

Arenson, Ronald L.; Morton, Dan E.; London, Jack W.

1982-01-01

This paper discusses a fiber optic communication system linking ultrasound devices, Computerized tomography scanners, Nuclear Medicine computer system, and a digital fluoro-graphic system to a central radiology research computer. These centrally archived images are available for near instantaneous recall at various display consoles. When a suitable laser optical disk is available for mass storage, more extensive image archiving will be added to the network including digitized images of standard radiographs for comparison purposes and for remote display in such areas as the intensive care units, the operating room, and selected outpatient departments. This fiber optic system allows for a transfer of high resolution images in less than a second over distances exceeding 2,000 feet. The advantages of using fiber optic cables instead of typical parallel or serial communication techniques will be described. The switching methodology and communication protocols will also be discussed.

Image Registration Workshop Proceedings

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline (Editor)

1997-01-01

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research.

Cadaveric and in vivo human joint imaging based on differential phase contrast by X-ray Talbot-Lau interferometry.

PubMed

Tanaka, Junji; Nagashima, Masabumi; Kido, Kazuhiro; Hoshino, Yoshihide; Kiyohara, Junko; Makifuchi, Chiho; Nishino, Satoshi; Nagatsuka, Sumiya; Momose, Atsushi

2013-09-01

We developed an X-ray phase imaging system based on Talbot-Lau interferometry and studied its feasibility for clinical diagnoses of joint diseases. The system consists of three X-ray gratings, a conventional X-ray tube, an object holder, an X-ray image sensor, and a computer for image processing. The joints of human cadavers and healthy volunteers were imaged, and the results indicated sufficient sensitivity to cartilage, suggesting medical significance. Copyright © 2012. Published by Elsevier GmbH.

Local reconstruction in computed tomography of diffraction enhanced imaging

NASA Astrophysics Data System (ADS)

Huang, Zhi-Feng; Zhang, Li; Kang, Ke-Jun; Chen, Zhi-Qiang; Zhu, Pei-Ping; Yuan, Qing-Xi; Huang, Wan-Xia

2007-07-01

Computed tomography of diffraction enhanced imaging (DEI-CT) based on synchrotron radiation source has extremely high sensitivity of weakly absorbing low-Z samples in medical and biological fields. The authors propose a modified backprojection filtration(BPF)-type algorithm based on PI-line segments to reconstruct region of interest from truncated refraction-angle projection data in DEI-CT. The distribution of refractive index decrement in the sample can be directly estimated from its reconstruction images, which has been proved by experiments at the Beijing Synchrotron Radiation Facility. The algorithm paves the way for local reconstruction of large-size samples by the use of DEI-CT with small field of view based on synchrotron radiation source.

Sensor-based architecture for medical imaging workflow analysis.

PubMed

Silva, Luís A Bastião; Campos, Samuel; Costa, Carlos; Oliveira, José Luis

2014-08-01

The growing use of computer systems in medical institutions has been generating a tremendous quantity of data. While these data have a critical role in assisting physicians in the clinical practice, the information that can be extracted goes far beyond this utilization. This article proposes a platform capable of assembling multiple data sources within a medical imaging laboratory, through a network of intelligent sensors. The proposed integration framework follows a SOA hybrid architecture based on an information sensor network, capable of collecting information from several sources in medical imaging laboratories. Currently, the system supports three types of sensors: DICOM repository meta-data, network workflows and examination reports. Each sensor is responsible for converting unstructured information from data sources into a common format that will then be semantically indexed in the framework engine. The platform was deployed in the Cardiology department of a central hospital, allowing identification of processes' characteristics and users' behaviours that were unknown before the utilization of this solution.

Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images.

PubMed

Lv, Shengqing; Chen, Yimin; Li, Zeyu; Lu, Jiahui; Gao, Mingke; Lu, Rongrong

2017-11-01

Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.

Rapid 3D bioprinting from medical images: an application to bone scaffolding

NASA Astrophysics Data System (ADS)

Lee, Daniel Z.; Peng, Matthew W.; Shinde, Rohit; Khalid, Arbab; Hong, Abigail; Pennacchi, Sara; Dawit, Abel; Sipzner, Daniel; Udupa, Jayaram K.; Rajapakse, Chamith S.

2018-03-01

Bioprinting of tissue has its applications throughout medicine. Recent advances in medical imaging allows the generation of 3-dimensional models that can then be 3D printed. However, the conventional method of converting medical images to 3D printable G-Code instructions has several limitations, namely significant processing time for large, high resolution images, and the loss of microstructural surface information from surface resolution and subsequent reslicing. We have overcome these issues by creating a JAVA program that skips the intermediate triangularization and reslicing steps and directly converts binary dicom images into G-Code. In this study, we tested the two methods of G-Code generation on the application of synthetic bone graft scaffold generation. We imaged human cadaveric proximal femurs at an isotropic resolution of 0.03mm using a high resolution peripheral quantitative computed tomography (HR-pQCT) scanner. These images, of the Digital Imaging and Communications in Medicine (DICOM) format, were then processed through two methods. In each method, slices and regions of print were selected, filtered to generate a smoothed image, and thresholded. In the conventional method, these processed images are converted to the STereoLithography (STL) format and then resliced to generate G-Code. In the new, direct method, these processed images are run through our JAVA program and directly converted to G-Code. File size, processing time, and print time were measured for each. We found that this new method produced a significant reduction in G-Code file size as well as processing time (92.23% reduction). This allows for more rapid 3D printing from medical images.

[Real-time detection and processing of medical signals under windows using Lcard analog interfaces].

PubMed

Kuz'min, A A; Belozerov, A E; Pronin, T V

2008-01-01

Multipurpose modular software for an analog interface based on Lcard 761 is considered. Algorithms for pipeline processing of medical signals under Windows with dynamic control of computational resources are suggested. The software consists of user-friendly completable modifiable modules. The module hierarchy is based on object-oriented heritage principles, which make it possible to construct various real-time systems for long-term detection, processing, and imaging of multichannel medical signals.

Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction.

PubMed

Blaiotta, Claudia; Freund, Patrick; Cardoso, M Jorge; Ashburner, John

2018-02-01

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

MRI-powered biomedical devices.

PubMed

Hovet, Sierra; Ren, Hongliang; Xu, Sheng; Wood, Bradford; Tokuda, Junichi; Tse, Zion Tsz Ho

2017-11-16

Magnetic resonance imaging (MRI) is beneficial for imaging-guided procedures because it provides higher resolution images and better soft tissue contrast than computed tomography (CT), ultrasound, and X-ray. MRI can be used to streamline diagnostics and treatment because it does not require patients to be repositioned between scans of different areas of the body. It is even possible to use MRI to visualize, power, and control medical devices inside the human body to access remote locations and perform minimally invasive procedures. Therefore, MR conditional medical devices have the potential to improve a wide variety of medical procedures; this potential is explored in terms of practical considerations pertaining to clinical applications and the MRI environment. Recent advancements in this field are introduced with a review of clinically relevant research in the areas of interventional tools, endovascular microbots, and closed-loop controlled MRI robots. Challenges related to technology and clinical feasibility are discussed, including MRI based propulsion and control, navigation of medical devices through the human body, clinical adoptability, and regulatory issues. The development of MRI-powered medical devices is an emerging field, but the potential clinical impact of these devices is promising.

Interpretive versus noninterpretive content in top-selling radiology textbooks: what are we teaching medical students?

PubMed

Webb, Emily M; Vella, Maya; Straus, Christopher M; Phelps, Andrew; Naeger, David M

2015-04-01

There are little data as to whether appropriate, cost effective, and safe ordering of imaging examinations are adequately taught in US medical school curricula. We sought to determine the proportion of noninterpretive content (such as appropriate ordering) versus interpretive content (such as reading a chest x-ray) in the top-selling medical student radiology textbooks. We performed an online search to identify a ranked list of the six top-selling general radiology textbooks for medical students. Each textbook was reviewed including content in the text, tables, images, figures, appendices, practice questions, question explanations, and glossaries. Individual pages of text and individual images were semiquantitatively scored on a six-level scale as to the percentage of material that was interpretive versus noninterpretive. The predominant imaging modality addressed in each was also recorded. Descriptive statistical analysis was performed. All six books had more interpretive content. On average, 1.4 pages of text focused on interpretation for every one page focused on noninterpretive content. Seventeen images/figures were dedicated to interpretive skills for every one focused on noninterpretive skills. In all books, the largest proportion of text and image content was dedicated to plain films (51.2%), with computed tomography (CT) a distant second (16%). The content on radiographs (3.1:1) and CT (1.6:1) was more interpretive than not. The current six top-selling medical student radiology textbooks contain a preponderance of material teaching image interpretation compared to material teaching noninterpretive skills, such as appropriate imaging examination selection, rational utilization, and patient safety. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Parallel fuzzy connected image segmentation on GPU

PubMed Central

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

2011-01-01

Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA’s compute unified device Architecture (cuda) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. PMID:21859037

Parallel fuzzy connected image segmentation on GPU.

PubMed

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K; Miller, Robert W

2011-07-01

Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

Medical imaging, PACS, and imaging informatics: retrospective.

PubMed

Huang, H K

2014-01-01

Historical reviews of PACS (picture archiving and communication system) and imaging informatics development from different points of view have been published in the past (Huang in Euro J Radiol 78:163-176, 2011; Lemke in Euro J Radiol 78:177-183, 2011; Inamura and Jong in Euro J Radiol 78:184-189, 2011). This retrospective attempts to look at the topic from a different angle by identifying certain basic medical imaging inventions in the 1960s and 1970s which had conceptually defined basic components of PACS guiding its course of development in the 1980s and 1990s, as well as subsequent imaging informatics research in the 2000s. In medical imaging, the emphasis was on the innovations at Georgetown University in Washington, DC, in the 1960s and 1970s. During the 1980s and 1990s, research and training support from US government agencies and public and private medical imaging manufacturers became available for training of young talents in biomedical physics and for developing the key components required for PACS development. In the 2000s, computer hardware and software as well as communication networks advanced by leaps and bounds, opening the door for medical imaging informatics to flourish. Because many key components required for the PACS operation were developed by the UCLA PACS Team and its collaborative partners in the 1980s, this presentation is centered on that aspect. During this period, substantial collaborative research efforts by many individual teams in the US and in Japan were highlighted. Credits are due particularly to the Pattern Recognition Laboratory at Georgetown University, and the computed radiography (CR) development at the Fuji Electric Corp. in collaboration with Stanford University in the 1970s; the Image Processing Laboratory at UCLA in the 1980s-1990s; as well as the early PACS development at the Hokkaido University, Sapporo, Japan, in the late 1970s, and film scanner and digital radiography developed by Konishiroku Photo Ind. Co. Ltd. (Konica-Minolta), Japan, in the 1980-1990s. Major support from the US National Institutes of Health and other federal agencies and private medical imaging industry are appreciated. The NATO (North Atlantic Treaty Organization) Advanced Study Institute (ASI) sponsored the International PACS Conference at Evian, France, in 1990, the contents and presentations of which convinced a half dozen high-level US military healthcare personnel, including surgeons and radiologists, that PACS was feasible and would greatly streamline the current military healthcare services. The impact of the post-conference summary by these individuals to their superiors opened the doors for long-term support of PACS development by the US Military Healthcare Services. PACS and imaging informatics have thus emerged as a daily clinical necessity.

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

PubMed Central

Filipovic, Nenad D.

2017-01-01

Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration. PMID:28611851

Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers.

PubMed

Milankovic, Ivan L; Mijailovic, Nikola V; Filipovic, Nenad D; Peulic, Aleksandar S

2017-01-01

Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

Stereoscopic Vascular Models of the Head and Neck: A Computed Tomography Angiography Visualization

ERIC Educational Resources Information Center

Cui, Dongmei; Lynch, James C.; Smith, Andrew D.; Wilson, Timothy D.; Lehman, Michael N.

2016-01-01

Computer-assisted 3D models are used in some medical and allied health science schools; however, they are often limited to online use and 2D flat screen-based imaging. Few schools take advantage of 3D stereoscopic learning tools in anatomy education and clinically relevant anatomical variations when teaching anatomy. A new approach to teaching…

Implemented a wireless communication system for VGA capsule endoscope.

PubMed

Moon, Yeon-Kwan; Lee, Jyung Hyun; Park, Hee-Joon; Cho, Jin-Ho; Choi, Hyun-Chul

2014-01-01

Recently, several medical devices that use wireless communication are under development. In this paper, the small size frequency shift keying (FSK) transmitter and a monofilar antenna for the capsule endoscope, enabling the medical device to transmit VGA-size images of the intestine. To verify the functionality of the proposed wireless communication system, computer simulations and animal experiments were performed with the implemented capsule endoscope that includes the proposed wireless communication system. Several fundamental experiments are carried out using the implemented transmitter and antenna, and animal in-vivo experiments were performed to verify VGA image transmission.

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Ideal AFROC and FROC observers.

PubMed

Khurd, Parmeshwar; Liu, Bin; Gindi, Gene

2010-02-01

Detection of multiple lesions in images is a medically important task and free-response receiver operating characteristic (FROC) analyses and its variants, such as alternative FROC (AFROC) analyses, are commonly used to quantify performance in such tasks. However, ideal observers that optimize FROC or AFROC performance metrics have not yet been formulated in the general case. If available, such ideal observers may turn out to be valuable for imaging system optimization and in the design of computer aided diagnosis techniques for lesion detection in medical images. In this paper, we derive ideal AFROC and FROC observers. They are ideal in that they maximize, amongst all decision strategies, the area, or any partial area, under the associated AFROC or FROC curve. Calculation of observer performance for these ideal observers is computationally quite complex. We can reduce this complexity by considering forms of these observers that use false positive reports derived from signal-absent images only. We also consider a Bayes risk analysis for the multiple-signal detection task with an appropriate definition of costs. A general decision strategy that minimizes Bayes risk is derived. With particular cost constraints, this general decision strategy reduces to the decision strategy associated with the ideal AFROC or FROC observer.

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.

Image reconstruction from few-view CT data by gradient-domain dictionary learning.

PubMed

Hu, Zhanli; Liu, Qiegen; Zhang, Na; Zhang, Yunwan; Peng, Xi; Wu, Peter Z; Zheng, Hairong; Liang, Dong

2016-05-21

Decreasing the number of projections is an effective way to reduce the radiation dose exposed to patients in medical computed tomography (CT) imaging. However, incomplete projection data for CT reconstruction will result in artifacts and distortions. In this paper, a novel dictionary learning algorithm operating in the gradient-domain (Grad-DL) is proposed for few-view CT reconstruction. Specifically, the dictionaries are trained from the horizontal and vertical gradient images, respectively and the desired image is reconstructed subsequently from the sparse representations of both gradients by solving the least-square method. Since the gradient images are sparser than the image itself, the proposed approach could lead to sparser representations than conventional DL methods in the image-domain, and thus a better reconstruction quality is achieved. To evaluate the proposed Grad-DL algorithm, both qualitative and quantitative studies were employed through computer simulations as well as real data experiments on fan-beam and cone-beam geometry. The results show that the proposed algorithm can yield better images than the existing algorithms.

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets.

PubMed

Scharfe, Michael; Pielot, Rainer; Schreiber, Falk

2010-01-11

Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks. We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from http://cbe.ipk-gatersleben.de. The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.

Storage and distribution of pathology digital images using integrated web-based viewing systems.

PubMed

Marchevsky, Alberto M; Dulbandzhyan, Ronda; Seely, Kevin; Carey, Steve; Duncan, Raymond G

2002-05-01

Health care providers have expressed increasing interest in incorporating digital images of gross pathology specimens and photomicrographs in routine pathology reports. To describe the multiple technical and logistical challenges involved in the integration of the various components needed for the development of a system for integrated Web-based viewing, storage, and distribution of digital images in a large health system. An Oracle version 8.1.6 database was developed to store, index, and deploy pathology digital photographs via our Intranet. The database allows for retrieval of images by patient demographics or by SNOMED code information. The Intranet of a large health system accessible from multiple computers located within the medical center and at distant private physician offices. The images can be viewed using any of the workstations of the health system that have authorized access to our Intranet, using a standard browser or a browser configured with an external viewer or inexpensive plug-in software, such as Prizm 2.0. The images can be printed on paper or transferred to film using a digital film recorder. Digital images can also be displayed at pathology conferences by using wireless local area network (LAN) and secure remote technologies. The standardization of technologies and the adoption of a Web interface for all our computer systems allows us to distribute digital images from a pathology database to a potentially large group of users distributed in multiple locations throughout a large medical center.

Advantages and Disadvantages in Image Processing with Free Software in Radiology.

PubMed

Mujika, Katrin Muradas; Méndez, Juan Antonio Juanes; de Miguel, Andrés Framiñan

2018-01-15

Currently, there are sophisticated applications that make it possible to visualize medical images and even to manipulate them. These software applications are of great interest, both from a teaching and a radiological perspective. In addition, some of these applications are known as Free Open Source Software because they are free and the source code is freely available, and therefore it can be easily obtained even on personal computers. Two examples of free open source software are Osirix Lite® and 3D Slicer®. However, this last group of free applications have limitations in its use. For the radiological field, manipulating and post-processing images is increasingly important. Consequently, sophisticated computing tools that combine software and hardware to process medical images are needed. In radiology, graphic workstations allow their users to process, review, analyse, communicate and exchange multidimensional digital images acquired with different image-capturing radiological devices. These radiological devices are basically CT (Computerised Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), etc. Nevertheless, the programs included in these workstations have a high cost which always depends on the software provider and is always subject to its norms and requirements. With this study, we aim to present the advantages and disadvantages of these radiological image visualization systems in the advanced management of radiological studies. We will compare the features of the VITREA2® and AW VolumeShare 5® radiology workstation with free open source software applications like OsiriX® and 3D Slicer®, with examples from specific studies.

CT imaging, then and now: a 30-year review of the economics of computed tomography.

PubMed

Stockburger, Wayne T

2004-01-01

The first computed tomography (CT) scanner in the US was installed in June 1973 at the Mayo Clinic in Rochester, MN. By the end of 1974, 44 similar systems had been installed at medical facilities around the country. Less than 4 years after the introduction of CT imaging in the US, at least 400 CT systems had been installed. The practice of pneumoencephalography was eliminated. The use of nuclear medicine brain scans significantly diminished. At the time, CT imaging was limited to head studies, but with the introduction of contrast agents and full body CT systems the changes in the practice of medicine became even more significant. CT imaging was hailed by the US medical community as the greatest advance in radiology since the discovery of x-rays. But the rapid spread of CT systems, their frequency of use, and the associated increase in healthcare costs combined to draw the attention of decision-makers within the federal and state governments, specifically to establish policies regarding the acquisition and use of diagnostic technologies. Initially, CT imaging was limited to neurological applications, but in the 30 years since its inception, capabilities and applications have been expanded as a result of the advancements in technology and software development. While neurological disorders are still a common reason for CT imaging, many other medical disciplines (oncology, emergency medicine, orthopedics, etc.) have found CT imaging to be the definitive tool for diagnostic information. As such, the clinical demand for CT imaging has steadily increased. Economically, the development of CT imaging has been one of success, even in the face of governmental action to restrict its acquisition and utilization by healthcare facilities. CTimaging has increased the cost of healthcare, but in turn has added unquantifiable value to the practice of medicine in the US.

[Basic concept in computer assisted surgery].

PubMed

Merloz, Philippe; Wu, Hao

2006-03-01

To investigate application of medical digital imaging systems and computer technologies in orthopedics. The main computer-assisted surgery systems comprise the four following subcategories. (1) A collection and recording process for digital data on each patient, including preoperative images (CT scans, MRI, standard X-rays), intraoperative visualization (fluoroscopy, ultrasound), and intraoperative position and orientation of surgical instruments or bone sections (using 3D localises). Data merging based on the matching of preoperative imaging (CT scans, MRI, standard X-rays) and intraoperative visualization (anatomical landmarks, or bone surfaces digitized intraoperatively via 3D localiser; intraoperative ultrasound images processed for delineation of bone contours). (2) In cases where only intraoperative images are used for computer-assisted surgical navigation, the calibration of the intraoperative imaging system replaces the merged data system, which is then no longer necessary. (3) A system that provides aid in decision-making, so that the surgical approach is planned on basis of multimodal information: the interactive positioning of surgical instruments or bone sections transmitted via pre- or intraoperative images, display of elements to guide surgical navigation (direction, axis, orientation, length and diameter of a surgical instrument, impingement, etc. ). And (4) A system that monitors the surgical procedure, thereby ensuring that the optimal strategy defined at the preoperative stage is taken into account. It is possible that computer-assisted orthopedic surgery systems will enable surgeons to better assess the accuracy and reliability of the various operative techniques, an indispensable stage in the optimization of surgery.

Visuospatial skills and computer game experience influence the performance of virtual endoscopy.

PubMed

Enochsson, Lars; Isaksson, Bengt; Tour, René; Kjellin, Ann; Hedman, Leif; Wredmark, Torsten; Tsai-Felländer, Li

2004-11-01

Advanced medical simulators have been introduced to facilitate surgical and endoscopic training and thereby improve patient safety. Residents trained in the Procedicus Minimally Invasive Surgical Trainer-Virtual Reality (MIST-VR) laparoscopic simulator perform laparoscopic cholecystectomy safer and faster than a control group. Little has been reported regarding whether factors like gender, computer experience, and visuospatial tests can predict the performance with a medical simulator. Our aim was to investigate whether such factors influence the performance of simulated gastroscopy. Seventeen medical students were asked about computer gaming experiences. Before virtual endoscopy, they performed the visuospatial test PicCOr, which discriminates the ability of the tested person to create a three-dimensional image from a two-dimensional presentation. Each student performed one gastroscopy (level 1, case 1) in the GI Mentor II, Simbionix, and several variables related to performance were registered. Percentage of time spent with a clear view in the endoscope correlated well with the performance on the PicSOr test (r = 0.56, P < 0.001). Efficiency of screening also correlated with PicSOr (r = 0.23, P < 0.05). In students with computer gaming experience, the efficiency of screening increased (33.6% +/- 3.1% versus 22.6% +/- 2.8%, P < 0.05) and the duration of the examination decreased by 1.5 minutes (P < 0.05). A similar trend was seen in men compared with women. The visuospatial test PicSOr predicts the results with the endoscopic simulator GI Mentor II. Two-dimensional image experience, as in computer games, also seems to affect the outcome.

Medicine: The final frontier in cancer diagnosis

NASA Astrophysics Data System (ADS)

Leachman, Sancy A.; Merlino, Glenn

2017-01-01

A computer, trained to classify skin cancers using image analysis alone, can now identify certain cancers as successfully as can skin-cancer doctors. What are the implications for the future of medical diagnosis? See Letter p.115

Technology 2004, Vol. 2

NASA Technical Reports Server (NTRS)

1995-01-01

Proceedings from symposia of the Technology 2004 Conference, November 8-10, 1994, Washington, DC. Volume 2 features papers on computers and software, virtual reality simulation, environmental technology, video and imaging, medical technology and life sciences, robotics and artificial intelligence, and electronics.

Patient-directed Internet-based Medical Image Exchange: Experience from an Initial Multicenter Implementation

PubMed Central

Greco, Giampaolo; Patel, Anand S.; Lewis, Sara C.; Shi, Wei; Rasul, Rehana; Torosyan, Mary; Erickson, Bradley J.; Hiremath, Atheeth; Moskowitz, Alan J.; Tellis, Wyatt M.; Siegel, Eliot L.; Arenson, Ronald L.; Mendelson, David S.

2015-01-01

Rationale and Objectives Inefficient transfer of personal health records among providers negatively impacts quality of health care and increases cost. This multicenter study evaluates the implementation of the first Internet-based image-sharing system that gives patients ownership and control of their imaging exams, including assessment of patient satisfaction. Materials and Methods Patients receiving any medical imaging exams in four academic centers were eligible to have images uploaded into an online, Internet-based personal health record. Satisfaction surveys were provided during recruitment with questions on ease of use, privacy and security, and timeliness of access to images. Responses were rated on a five-point scale and compared using logistic regression and McNemar's test. Results A total of 2562 patients enrolled from July 2012 to August 2013. The median number of imaging exams uploaded per patient was 5. Most commonly, exams were plain X-rays (34.7%), computed tomography (25.7%), and magnetic resonance imaging (16.1%). Of 502 (19.6%) patient surveys returned, 448 indicated the method of image sharing (Internet, compact discs [CDs], both, other). Nearly all patients (96.5%) responded favorably to having direct access to images, and 78% reported viewing their medical images independently. There was no difference between Internet and CD users in satisfaction with privacy and security and timeliness of access to medical images. A greater percentage of Internet users compared to CD users reported access without difficulty (88.3% vs. 77.5%, P < 0.0001). Conclusion A patient-directed, interoperable, Internet-based image-sharing system is feasible and surpasses the use of CDs with respect to accessibility of imaging exams while generating similar satisfaction with respect to privacy. PMID:26625706

Patient-directed Internet-based Medical Image Exchange: Experience from an Initial Multicenter Implementation.

PubMed

Greco, Giampaolo; Patel, Anand S; Lewis, Sara C; Shi, Wei; Rasul, Rehana; Torosyan, Mary; Erickson, Bradley J; Hiremath, Atheeth; Moskowitz, Alan J; Tellis, Wyatt M; Siegel, Eliot L; Arenson, Ronald L; Mendelson, David S

2016-02-01

Inefficient transfer of personal health records among providers negatively impacts quality of health care and increases cost. This multicenter study evaluates the implementation of the first Internet-based image-sharing system that gives patients ownership and control of their imaging exams, including assessment of patient satisfaction. Patients receiving any medical imaging exams in four academic centers were eligible to have images uploaded into an online, Internet-based personal health record. Satisfaction surveys were provided during recruitment with questions on ease of use, privacy and security, and timeliness of access to images. Responses were rated on a five-point scale and compared using logistic regression and McNemar's test. A total of 2562 patients enrolled from July 2012 to August 2013. The median number of imaging exams uploaded per patient was 5. Most commonly, exams were plain X-rays (34.7%), computed tomography (25.7%), and magnetic resonance imaging (16.1%). Of 502 (19.6%) patient surveys returned, 448 indicated the method of image sharing (Internet, compact discs [CDs], both, other). Nearly all patients (96.5%) responded favorably to having direct access to images, and 78% reported viewing their medical images independently. There was no difference between Internet and CD users in satisfaction with privacy and security and timeliness of access to medical images. A greater percentage of Internet users compared to CD users reported access without difficulty (88.3% vs. 77.5%, P < 0.0001). A patient-directed, interoperable, Internet-based image-sharing system is feasible and surpasses the use of CDs with respect to accessibility of imaging exams while generating similar satisfaction with respect to privacy. Copyright © 2015 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Coral disease and health workshop: Coral Histopathology II, July 12-14, 2005

USGS Publications Warehouse

Galloway, S.B.; Woodley, Cheryl M.; McLaughlin, S.M.; Work, Thierry M.; Bochsler, V.S.; Meteyer, Carol U.; Sileo, Louis; Peters, E.C.; Kramarsky-Winters, E.; Morado, J. Frank; Parnell, P.G.; Rotstein, D.S.; Harely, R.A.; Reynolds, T.L.

2005-01-01

An exciting highlight of this meeting was provided by Professor Robert Ogilvie (MUSC Department of Cell Biology and Anatomy) when he introduced participants to a new digital technology that is revolutionizing histology and histopathology in the medical field. The Virtual Slide technology creates digital images of histological tissue sections by computer scanning actual slides in high definition and storing the images for retrieval and viewing. Virtual slides now allow any investigator with access to a computer and the web to view, search, annotate and comment on the same tissue sections in real time. Medical and veterinary slide libraries across the country are being converted into virtual slides to enhance biomedical education, research and diagnosis. The coral health and disease researchers at this workshop deem virtual slides as a significant way to increase capabilities in coral histology and a means for pathology consultations on coral disease cases on a global scale. 

SedCT: MATLAB™ tools for standardized and quantitative processing of sediment core computed tomography (CT) data collected using a medical CT scanner

NASA Astrophysics Data System (ADS)

Reilly, B. T.; Stoner, J. S.; Wiest, J.

2017-08-01

Computed tomography (CT) of sediment cores allows for high-resolution images, three-dimensional volumes, and down core profiles. These quantitative data are generated through the attenuation of X-rays, which are sensitive to sediment density and atomic number, and are stored in pixels as relative gray scale values or Hounsfield units (HU). We present a suite of MATLAB™ tools specifically designed for routine sediment core analysis as a means to standardize and better quantify the products of CT data collected on medical CT scanners. SedCT uses a graphical interface to process Digital Imaging and Communications in Medicine (DICOM) files, stitch overlapping scanned intervals, and create down core HU profiles in a manner robust to normal coring imperfections. Utilizing a random sampling technique, SedCT reduces data size and allows for quick processing on typical laptop computers. SedCTimage uses a graphical interface to create quality tiff files of CT slices that are scaled to a user-defined HU range, preserving the quantitative nature of CT images and easily allowing for comparison between sediment cores with different HU means and variance. These tools are presented along with examples from lacustrine and marine sediment cores to highlight the robustness and quantitative nature of this method.

Anniversary Paper: Development of x-ray computed tomography: The role of Medical Physics and AAPM from the 1970s to present

PubMed Central

Pan, Xiaochuan; Siewerdsen, Jeffrey; La Riviere, Patrick J.; Kalender, Willi A.

2008-01-01

The AAPM, through its members, meetings, and its flagship journal Medical Physics, has played an important role in the development and growth of x-ray tomography in the last 50 years. From a spate of early articles in the 1970s characterizing the first commercial computed tomography (CT) scanners through the “slice wars” of the 1990s and 2000s, the history of CT and related techniques such as tomosynthesis can readily be traced through the pages of Medical Physics and the annals of the AAPM and RSNA/AAPM Annual Meetings. In this article, the authors intend to give a brief review of the role of Medical Physics and the AAPM in CT and tomosynthesis imaging over the last few decades. PMID:18777932

Fast and robust multimodal image registration using a local derivative pattern.

PubMed

Jiang, Dongsheng; Shi, Yonghong; Chen, Xinrong; Wang, Manning; Song, Zhijian

2017-02-01

Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations. dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications. dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12 mm to 2.30 mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference. The results reveal that dLDP can achieve superior performance regarding both accuracy and time efficiency in general multimodal image registration. In addition, dLDP also indicates the potential for clinical ultrasound guided intervention. © 2016 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Laser-wakefield accelerators as hard x-ray sources for 3D medical imaging of human bone

PubMed Central

Cole, J. M.; Wood, J. C.; Lopes, N. C.; Poder, K.; Abel, R. L.; Alatabi, S.; Bryant, J. S. J.; Jin, A.; Kneip, S.; Mecseki, K.; Symes, D. R.; Mangles, S. P. D.; Najmudin, Z.

2015-01-01

A bright μm-sized source of hard synchrotron x-rays (critical energy Ecrit > 30 keV) based on the betatron oscillations of laser wakefield accelerated electrons has been developed. The potential of this source for medical imaging was demonstrated by performing micro-computed tomography of a human femoral trabecular bone sample, allowing full 3D reconstruction to a resolution below 50 μm. The use of a 1 cm long wakefield accelerator means that the length of the beamline (excluding the laser) is dominated by the x-ray imaging distances rather than the electron acceleration distances. The source possesses high peak brightness, which allows each image to be recorded with a single exposure and reduces the time required for a full tomographic scan. These properties make this an interesting laboratory source for many tomographic imaging applications. PMID:26283308

Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

PubMed

Diamant, Idit; Klang, Eyal; Amitai, Michal; Konen, Eli; Goldberger, Jacob; Greenspan, Hayit

2017-06-01

We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value 0.001). We demonstrated that classification based on informative selected set of words results in significant improvement. Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.

Evaluation of Teeth Development in Unilateral Cleft Lip and Palate Patients in Mixed Dentition by Using Medical Image Control Systems.

PubMed

Gezgin, O; Botsali, M S

2018-02-01

The aim of this study was to evaluate the crown and root development in patients with cleft lip and palate using medical software programmes. In our study, 25 patients with mixed dentition unilateral cleft lip and palate were examined with cone-beam computed tomography (CBCT). The tomography images obtained as high resolution medical images on the computer control system (MIMICS 15.0, Materialise, Leuven, Belgium and SOLIDWORKS 2014 Premium, Concord, Massachusetts) were converted to three-dimensional volumetric images. These three-dimensional images of the cleft on the sides of the teeth in the crown and root growth were measured by mesiodistal length and crown/root rate with volume and area. These measurements were compared with a control group of healthy individuals. There were no statistically significant differences in the volume, surface area and MD size, crown/root ratio of central incisor, canine, first premolar and second premolar teeth within defect, and healthy teeth. However, it was found that there was a significant difference between the volume, surface area and MD size, and crown/root ratio of the lateral teeth in each group. In particular, among patients with cleft lip and palate, on obtaining a solid model of the tooth structure by using these programs, tooth development can be examined in more detail, diagnosis can be made more reliable, as well as in treatment planning. We believe that these programs can be used to resolve certain limitations such as a lack of an application to be used in routine dental treatment and in particular the need to do more study.

Case retrieval in medical databases by fusing heterogeneous information.

PubMed

Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Roux, Christian; Cochener, Béatrice

2011-01-01

A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.

Initial experience with SPECT imaging of the brain using I-123 p-iodoamphetamine in focal epilepsy

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

LaManna, M.M.; Sussman, N.M.; Harner, R.N.

1989-06-01

Nineteen patients with complex partial seizures refractory to medical treatment were examined with routine electroencephalography (EEG), video EEG monitoring, computed tomography or magnetic resonance imaging, neuropsychological tests and interictal single photon emission computed tomography (SPECT) with I-123 iodoamphetamine (INT). In 18 patients, SPECT identified areas of focal reduction in tracer uptake that correlated with the epileptogenic focus identified on the EEG. In addition, SPECT disclosed other areas of neurologic dysfunction as elicited on neuropsychological tests. Thus, IMP SPECT is a useful tool for localizing epileptogenic foci and their associated dynamic deficits.

Computer-assisted diagnosis of melanoma.

PubMed

Fuller, Collin; Cellura, A Paul; Hibler, Brian P; Burris, Katy

2016-03-01

The computer-assisted diagnosis of melanoma is an exciting area of research where imaging techniques are combined with diagnostic algorithms in an attempt to improve detection and outcomes for patients with skin lesions suspicious for malignancy. Once an image has been acquired, it undergoes a processing pathway which includes preprocessing, enhancement, segmentation, feature extraction, feature selection, change detection, and ultimately classification. Practicality for everyday clinical use remains a vital question. A successful model must obtain results that are on par or outperform experienced dermatologists, keep costs at a minimum, be user-friendly, and be time efficient with high sensitivity and specificity. ©2015 Frontline Medical Communications.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics.

PubMed

Sharma, Harshita; Alekseychuk, Alexander; Leskovsky, Peter; Hellwich, Olaf; Anand, R S; Zerbe, Norman; Hufnagl, Peter

2012-10-04

Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics

PubMed Central

2012-01-01

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923. PMID:23035717

Variational PDE Models in Image Processing

DTIC Science & Technology

2002-07-31

161–168, 2001. [22] T. F. Chan and L. A. Vese. Active contour and segmentation models using ge- ometric PDE’s for medical imaging. Malladi , R . (Ed...continuous “movie” NMOPQ (with some small time step R ), D E >LK @ ’s are the estimated optical flows (i.e. velocity fields) at each moment. During...Bertalmio, G. Sapiro, V. Caselles, and C. Ballester. Image inpainting. Computer Graphics, SIGGRAPH 2000, July, 2000. [6] G. Birkhoff and C. R . De Boor

GPUs benchmarking in subpixel image registration algorithm

NASA Astrophysics Data System (ADS)

Sanz-Sabater, Martin; Picazo-Bueno, Jose Angel; Micó, Vicente; Ferrerira, Carlos; Granero, Luis; Garcia, Javier

2015-05-01

Image registration techniques are used among different scientific fields, like medical imaging or optical metrology. The straightest way to calculate shifting between two images is using the cross correlation, taking the highest value of this correlation image. Shifting resolution is given in whole pixels which cannot be enough for certain applications. Better results can be achieved interpolating both images, as much as the desired resolution we want to get, and applying the same technique described before, but the memory needed by the system is significantly higher. To avoid memory consuming we are implementing a subpixel shifting method based on FFT. With the original images, subpixel shifting can be achieved multiplying its discrete Fourier transform by a linear phase with different slopes. This method is high time consuming method because checking a concrete shifting means new calculations. The algorithm, highly parallelizable, is very suitable for high performance computing systems. GPU (Graphics Processing Unit) accelerated computing became very popular more than ten years ago because they have hundreds of computational cores in a reasonable cheap card. In our case, we are going to register the shifting between two images, doing the first approach by FFT based correlation, and later doing the subpixel approach using the technique described before. We consider it as `brute force' method. So we will present a benchmark of the algorithm consisting on a first approach (pixel resolution) and then do subpixel resolution approaching, decreasing the shifting step in every loop achieving a high resolution in few steps. This program will be executed in three different computers. At the end, we will present the results of the computation, with different kind of CPUs and GPUs, checking the accuracy of the method, and the time consumed in each computer, discussing the advantages, disadvantages of the use of GPUs.

A human visual based binarization technique for histological images

NASA Astrophysics Data System (ADS)

Shreyas, Kamath K. M.; Rajendran, Rahul; Panetta, Karen; Agaian, Sos

2017-05-01

In the field of vision-based systems for object detection and classification, thresholding is a key pre-processing step. Thresholding is a well-known technique for image segmentation. Segmentation of medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), X-Ray, Phase Contrast Microscopy, and Histological images, present problems like high variability in terms of the human anatomy and variation in modalities. Recent advances made in computer-aided diagnosis of histological images help facilitate detection and classification of diseases. Since most pathology diagnosis depends on the expertise and ability of the pathologist, there is clearly a need for an automated assessment system. Histological images are stained to a specific color to differentiate each component in the tissue. Segmentation and analysis of such images is problematic, as they present high variability in terms of color and cell clusters. This paper presents an adaptive thresholding technique that aims at segmenting cell structures from Haematoxylin and Eosin stained images. The thresholded result can further be used by pathologists to perform effective diagnosis. The effectiveness of the proposed method is analyzed by visually comparing the results to the state of art thresholding methods such as Otsu, Niblack, Sauvola, Bernsen, and Wolf. Computer simulations demonstrate the efficiency of the proposed method in segmenting critical information.

[Problem list in computer-based patient records].

PubMed

Ludwig, C A

1997-01-14

Computer-based clinical information systems are capable of effectively processing even large amounts of patient-related data. However, physicians depend on rapid access to summarized, clearly laid out data on the computer screen to inform themselves about a patient's current clinical situation. In introducing a clinical workplace system, we therefore transformed the problem list-which for decades has been successfully used in clinical information management-into an electronic equivalent and integrated it into the medical record. The table contains a concise overview of diagnoses and problems as well as related findings. Graphical information can also be integrated into the table, and an additional space is provided for a summary of planned examinations or interventions. The digital form of the problem list makes it possible to use the entire list or selected text elements for generating medical documents. Diagnostic terms for medical reports are transferred automatically to corresponding documents. Computer technology has an immense potential for the further development of problem list concepts. With multimedia applications sound and images will be included in the problem list. For hyperlink purpose the problem list could become a central information board and table of contents of the medical record, thus serving as the starting point for database searches and supporting the user in navigating through the medical record.

Review of medical imaging with emphasis on X-ray detectors

NASA Astrophysics Data System (ADS)

Hoheisel, Martin

2006-07-01

Medical imaging can be looked at from two different perspectives, the medical and the physical. The medical point of view is application-driven and involves finding the best way of tackling a medical problem through imaging, i.e. either to answer a diagnostic question, or to facilitate a therapy. For this purpose, industry offers a broad spectrum of radiographic, fluoroscopic, and angiographic equipment. The requirements depend on the medical problem: which organs have to be imaged, which details have to be made visible, how to deal with the problem of motion if any, and so forth. In radiography, for instance, large detector sizes of up to 43 cm×43 cm and relatively high energies are needed to image a whole chest. In mammography, pixel sizes between 25 and 70 μm are favorable for good spatial resolution, which is essential for detecting microcalcifications. In cardiology, 30-60 images per second are required to follow the heart's motion. In computed tomography, marginal contrast differences down to one Hounsfield unit have to be resolved. In all cases, but especially in pediatrics, the required radiation dose must be kept as low as reasonably achievable. Moreover, three-dimensional(3D) reconstruction of image data allows much better orientation in the body, permitting a more accurate diagnosis, precise treatment planning, and image-guided therapy. Additional functional information from different modalities is very helpful, information such as perfusion, flow rate, diffusion, oxygen concentration, metabolism, and receptor affinity for specific molecules. To visualize, functional and anatomical information are fused into one combined image. The physical point of view is technology-driven. A choice of different energies from the electromagnetic spectrum is available for imaging; not only X-rays in the range of 10-150 keV, but also γ rays, which are used in nuclear medicine, X-rays in the MeV range, which are used in portal imaging to monitor radiation therapy, visible and near infrared light (1-3 eV) for retina inspection and mamma transillumination, and even Terahertz waves (0.5-20 meV) are under discussion. Feasibility is determined by the existing radiation sources, the materials available for absorbing and converting the radiation used, the microelectronic circuits for integrating or counting readout, and the computing power required to process and, where applicable, reconstruct data in real-time. Furthermore, other physical effects can be utilized such as the phase information a wave front receives when passing through an object. Some new developments will be discussed, e.g. energy-resolving methods for distinguishing different tissues in the patient, quanta-counting detection, phase contrast imaging, CCDs for very high spatial resolution, fast volume CT scanners, and organic semiconductors for a new generation of detection devices. Admittedly, apart from imaging performance, economic factors also have to be taken into account.

Methods for the analysis of ordinal response data in medical image quality assessment.

PubMed

Keeble, Claire; Baxter, Paul D; Gislason-Lee, Amber J; Treadgold, Laura A; Davies, Andrew G

2016-07-01

The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimization, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilization of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care.

Recent Advances in X-ray Cone-beam Computed Laminography.

PubMed

O'Brien, Neil S; Boardman, Richard P; Sinclair, Ian; Blumensath, Thomas

2016-10-06

X-ray computed tomography is an established volume imaging technique used routinely in medical diagnosis, industrial non-destructive testing, and a wide range of scientific fields. Traditionally, computed tomography uses scanning geometries with a single axis of rotation together with reconstruction algorithms specifically designed for this setup. Recently there has however been increasing interest in more complex scanning geometries. These include so called X-ray computed laminography systems capable of imaging specimens with large lateral dimensions or large aspect ratios, neither of which are well suited to conventional CT scanning procedures. Developments throughout this field have thus been rapid, including the introduction of novel system trajectories, the application and refinement of various reconstruction methods, and the use of recently developed computational hardware and software techniques to accelerate reconstruction times. Here we examine the advances made in the last several years and consider their impact on the state of the art.

Informatics in Radiology (infoRAD): personal computer security: part 2. Software Configuration and file protection.

PubMed

Caruso, Ronald D

2004-01-01

Proper configuration of software security settings and proper file management are necessary and important elements of safe computer use. Unfortunately, the configuration of software security options is often not user friendly. Safe file management requires the use of several utilities, most of which are already installed on the computer or available as freeware. Among these file operations are setting passwords, defragmentation, deletion, wiping, removal of personal information, and encryption. For example, Digital Imaging and Communications in Medicine medical images need to be anonymized, or "scrubbed," to remove patient identifying information in the header section prior to their use in a public educational or research environment. The choices made with respect to computer security may affect the convenience of the computing process. Ultimately, the degree of inconvenience accepted will depend on the sensitivity of the files and communications to be protected and the tolerance of the user. Copyright RSNA, 2004

A PACS archive architecture supported on cloud services.

PubMed

Silva, Luís A Bastião; Costa, Carlos; Oliveira, José Luis

2012-05-01

Diagnostic imaging procedures have continuously increased over the last decade and this trend may continue in coming years, creating a great impact on storage and retrieval capabilities of current PACS. Moreover, many smaller centers do not have financial resources or requirements that justify the acquisition of a traditional infrastructure. Alternative solutions, such as cloud computing, may help address this emerging need. A tremendous amount of ubiquitous computational power, such as that provided by Google and Amazon, are used every day as a normal commodity. Taking advantage of this new paradigm, an architecture for a Cloud-based PACS archive that provides data privacy, integrity, and availability is proposed. The solution is independent from the cloud provider and the core modules were successfully instantiated in examples of two cloud computing providers. Operational metrics for several medical imaging modalities were tabulated and compared for Google Storage, Amazon S3, and LAN PACS. A PACS-as-a-Service archive that provides storage of medical studies using the Cloud was developed. The results show that the solution is robust and that it is possible to store, query, and retrieve all desired studies in a similar way as in a local PACS approach. Cloud computing is an emerging solution that promises high scalability of infrastructures, software, and applications, according to a "pay-as-you-go" business model. The presented architecture uses the cloud to setup medical data repositories and can have a significant impact on healthcare institutions by reducing IT infrastructures.

Comparing features sets for content-based image retrieval in a medical-case database

NASA Astrophysics Data System (ADS)

Muller, Henning; Rosset, Antoine; Vallee, Jean-Paul; Geissbuhler, Antoine

2004-04-01

Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the texture analysis also leads to improved results but response time is going up equally due to the larger feature space. CBIRSs can be of great use in managing large medical image databases. They allow to find images that might otherwise be lost for research and publications. They also give students students the possibility to navigate within large image repositories. In the future, CBIR might also become more important in case-based reasoning and evidence-based medicine to support the diagnostics because first studies show good results.

Development of a high resolution voxelised head phantom for medical physics applications.

PubMed

Giacometti, V; Guatelli, S; Bazalova-Carter, M; Rosenfeld, A B; Schulte, R W

2017-01-01

Computational anthropomorphic phantoms have become an important investigation tool for medical imaging and dosimetry for radiotherapy and radiation protection. The development of computational phantoms with realistic anatomical features contribute significantly to the development of novel methods in medical physics. For many applications, it is desirable that such computational phantoms have a real-world physical counterpart in order to verify the obtained results. In this work, we report the development of a voxelised phantom, the HIGH_RES_HEAD, modelling a paediatric head based on the commercial phantom 715-HN (CIRS). HIGH_RES_HEAD is unique for its anatomical details and high spatial resolution (0.18×0.18mm 2 pixel size). The development of such a phantom was required to investigate the performance of a new proton computed tomography (pCT) system, in terms of detector technology and image reconstruction algorithms. The HIGH_RES_HEAD was used in an ad-hoc Geant4 simulation modelling the pCT system. The simulation application was previously validated with respect to experimental results. When compared to a standard spatial resolution voxelised phantom of the same paediatric head, it was shown that in pCT reconstruction studies, the use of the HIGH_RES_HEAD translates into a reduction from 2% to 0.7% of the average relative stopping power difference between experimental and simulated results thus improving the overall quality of the head phantom simulation. The HIGH_RES_HEAD can also be used for other medical physics applications such as treatment planning studies. A second version of the voxelised phantom was created that contains a prototypic base of skull tumour and surrounding organs at risk. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Reliable structural information from multiscale decomposition with the Mellor-Brady filter

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael

2009-08-01

Image-based medical diagnosis typically relies on the (poorly reproducible) subjective classification of textures in order to differentiate between diseased and healthy pathology. Clinicians claim that significant benefits would arise from quantitative measures to inform clinical decision making. The first step in generating such measures is to extract local image descriptors - from noise corrupted and often spatially and temporally coarse resolution medical signals - that are invariant to illumination, translation, scale and rotation of the features. The Dual-Tree Complex Wavelet Transform (DT-CWT) provides a wavelet multiresolution analysis (WMRA) tool e.g. in 2D with good properties, but has limited rotational selectivity. Also, it requires computationally-intensive steering due to the inherently 1D operations performed. The monogenic signal, which is defined in n >= 2D with the Riesz transform gives excellent orientation information without the need for steering. Recent work has suggested the Monogenic Riesz-Laplace wavelet transform as a possible tool for integrating these two concepts into a coherent mathematical framework. We have found that the proposed construction suffers from a lack of rotational invariance and is not optimal for retrieving local image descriptors. In this paper we show: 1. Local frequency and local phase from the monogenic signal are not equivalent, especially in the phase congruency model of a "feature", and so they are not interchangeable for medical image applications. 2. The accuracy of local phase computation may be improved by estimating the denoising parameters while maximizing a new measure of "featureness".

Normal anatomy and imaging of the hip: emphasis on impingement assessment.

PubMed

Jesse, Mary Kristen; Petersen, Brian; Strickland, Colin; Mei-Dan, Omer

2013-07-01

A comprehensive knowledge of normal hip anatomy and imaging techniques is essential in the evaluation and assessment of the patient with hip pain. This article reviews the osseous, soft tissue, and vascular components of the hip and the normal anatomical variants encountered in routine hip imaging. Basic and advanced hip imaging is discussed with particular emphasis on radiographic and computed tomography measurements and their utility in evaluating patients with developmental hip dysplasia and femoroacetabular impingement syndrome. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

An efficient dictionary learning algorithm and its application to 3-D medical image denoising.

PubMed

Li, Shutao; Fang, Leyuan; Yin, Haitao

2012-02-01

In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE

Three-dimensional printing in cardiology: Current applications and future challenges.

PubMed

Luo, Hongxing; Meyer-Szary, Jarosław; Wang, Zhongmin; Sabiniewicz, Robert; Liu, Yuhao

2017-01-01

Three-dimensional (3D) printing has attracted a huge interest in recent years. Broadly speaking, it refers to the technology which converts a predesigned virtual model to a touchable object. In clinical medicine, it usually converts a series of two-dimensional medical images acquired through computed tomography, magnetic resonance imaging or 3D echocardiography into a physical model. Medical 3D printing consists of three main steps: image acquisition, virtual reconstruction and 3D manufacturing. It is a promising tool for preoperative evaluation, medical device design, hemodynamic simulation and medical education, it is also likely to reduce operative risk and increase operative success. However, the most relevant studies are case reports or series which are underpowered in testing its actual effect on patient outcomes. The decision of making a 3D cardiac model may seem arbitrary since it is mostly based on a cardiologist's perceived difficulty in performing an interventional procedure. A uniform consensus is urgently necessary to standardize the key steps of 3D printing from imaging acquisition to final production. In the future, more clinical trials of rigorous design are possible to further validate the effect of 3D printing on the treatment of cardiovascular diseases. (Cardiol J 2017; 24, 4: 436-444).

Comprehensive security framework for the communication and storage of medical images

NASA Astrophysics Data System (ADS)

Slik, David; Montour, Mike; Altman, Tym

2003-05-01

Confidentiality, integrity verification and access control of medical imagery and associated metadata is critical for the successful deployment of integrated healthcare networks that extend beyond the department level. As medical imagery continues to become widely accessed across multiple administrative domains and geographically distributed locations, image data should be able to travel and be stored on untrusted infrastructure, including public networks and server equipment operated by external entities. Given these challenges associated with protecting large-scale distributed networks, measures must be taken to protect patient identifiable information while guarding against tampering, denial of service attacks, and providing robust audit mechanisms. The proposed framework outlines a series of security practices for the protection of medical images, incorporating Transport Layer Security (TLS), public and secret key cryptography, certificate management and a token based trusted computing base. It outlines measures that can be utilized to protect information stored within databases, online and nearline storage, and during transport over trusted and untrusted networks. In addition, it provides a framework for ensuring end-to-end integrity of image data from acquisition to viewing, and presents a potential solution to the challenges associated with access control across multiple administrative domains and institution user bases.