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.

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.

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.

Client-side Medical Image Colorization in a Collaborative Environment.

PubMed

Virag, Ioan; Stoicu-Tivadar, Lăcrămioara; Crişan-Vida, Mihaela

2015-01-01

The paper presents an application related to collaborative medicine using a browser based medical visualization system with focus on the medical image colorization process and the underlying open source web development technologies involved. Browser based systems allow physicians to share medical data with their remotely located counterparts or medical students, assisting them during patient diagnosis, treatment monitoring, surgery planning or for educational purposes. This approach brings forth the advantage of ubiquity. The system can be accessed from a any device, in order to process the images, assuring the independence towards having a specific proprietary operating system. The current work starts with processing of DICOM (Digital Imaging and Communications in Medicine) files and ends with the rendering of the resulting bitmap images on a HTML5 (fifth revision of the HyperText Markup Language) canvas element. The application improves the image visualization emphasizing different tissue densities.

Anniversary Paper: Image processing and manipulation through the pages of Medical Physics

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

Armato, Samuel G. III; Ginneken, Bram van; Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht

The language of radiology has gradually evolved from ''the film'' (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to ''the image,'' an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatialmore » alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the ''image'' as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.« less

From spoken narratives to domain knowledge: mining linguistic data for medical image understanding.

PubMed

Guo, Xuan; Yu, Qi; Alm, Cecilia Ovesdotter; Calvelli, Cara; Pelz, Jeff B; Shi, Pengcheng; Haake, Anne R

2014-10-01

Extracting useful visual clues from medical images allowing accurate diagnoses requires physicians' domain knowledge acquired through years of systematic study and clinical training. This is especially true in the dermatology domain, a medical specialty that requires physicians to have image inspection experience. Automating or at least aiding such efforts requires understanding physicians' reasoning processes and their use of domain knowledge. Mining physicians' references to medical concepts in narratives during image-based diagnosis of a disease is an interesting research topic that can help reveal experts' reasoning processes. It can also be a useful resource to assist with design of information technologies for image use and for image case-based medical education systems. We collected data for analyzing physicians' diagnostic reasoning processes by conducting an experiment that recorded their spoken descriptions during inspection of dermatology images. In this paper we focus on the benefit of physicians' spoken descriptions and provide a general workflow for mining medical domain knowledge based on linguistic data from these narratives. The challenge of a medical image case can influence the accuracy of the diagnosis as well as how physicians pursue the diagnostic process. Accordingly, we define two lexical metrics for physicians' narratives--lexical consensus score and top N relatedness score--and evaluate their usefulness by assessing the diagnostic challenge levels of corresponding medical images. We also report on clustering medical images based on anchor concepts obtained from physicians' medical term usage. These analyses are based on physicians' spoken narratives that have been preprocessed by incorporating the Unified Medical Language System for detecting medical concepts. The image rankings based on lexical consensus score and on top 1 relatedness score are well correlated with those based on challenge levels (Spearman correlation>0.5 and Kendall correlation>0.4). Clustering results are largely improved based on our anchor concept method (accuracy>70% and mutual information>80%). Physicians' spoken narratives are valuable for the purpose of mining the domain knowledge that physicians use in medical image inspections. We also show that the semantic metrics introduced in the paper can be successfully applied to medical image understanding and allow discussion of additional uses of these metrics. Copyright © 2014 Elsevier B.V. All rights reserved.

A Web simulation of medical image reconstruction and processing as an educational tool.

PubMed

Papamichail, Dimitrios; Pantelis, Evaggelos; Papagiannis, Panagiotis; Karaiskos, Pantelis; Georgiou, Evangelos

2015-02-01

Web educational resources integrating interactive simulation tools provide students with an in-depth understanding of the medical imaging process. The aim of this work was the development of a purely Web-based, open access, interactive application, as an ancillary learning tool in graduate and postgraduate medical imaging education, including a systematic evaluation of learning effectiveness. The pedagogic content of the educational Web portal was designed to cover the basic concepts of medical imaging reconstruction and processing, through the use of active learning and motivation, including learning simulations that closely resemble actual tomographic imaging systems. The user can implement image reconstruction and processing algorithms under a single user interface and manipulate various factors to understand the impact on image appearance. A questionnaire for pre- and post-training self-assessment was developed and integrated in the online application. The developed Web-based educational application introduces the trainee in the basic concepts of imaging through textual and graphical information and proceeds with a learning-by-doing approach. Trainees are encouraged to participate in a pre- and post-training questionnaire to assess their knowledge gain. An initial feedback from a group of graduate medical students showed that the developed course was considered as effective and well structured. An e-learning application on medical imaging integrating interactive simulation tools was developed and assessed in our institution.

A web-based solution for 3D medical image visualization

NASA Astrophysics Data System (ADS)

Hou, Xiaoshuai; Sun, Jianyong; Zhang, Jianguo

2015-03-01

In this presentation, we present a web-based 3D medical image visualization solution which enables interactive large medical image data processing and visualization over the web platform. To improve the efficiency of our solution, we adopt GPU accelerated techniques to process images on the server side while rapidly transferring images to the HTML5 supported web browser on the client side. Compared to traditional local visualization solution, our solution doesn't require the users to install extra software or download the whole volume dataset from PACS server. By designing this web-based solution, it is feasible for users to access the 3D medical image visualization service wherever the internet is available.

The Holistic Processing Account of Visual Expertise in Medical Image Perception: A Review

PubMed Central

Sheridan, Heather; Reingold, Eyal M.

2017-01-01

In the field of medical image perception, the holistic processing perspective contends that experts can rapidly extract global information about the image, which can be used to guide their subsequent search of the image (Swensson, 1980; Nodine and Kundel, 1987; Kundel et al., 2007). In this review, we discuss the empirical evidence supporting three different predictions that can be derived from the holistic processing perspective: Expertise in medical image perception is domain-specific, experts use parafoveal and/or peripheral vision to process large regions of the image in parallel, and experts benefit from a rapid initial glimpse of an image. In addition, we discuss a pivotal recent study (Litchfield and Donovan, 2016) that seems to contradict the assumption that experts benefit from a rapid initial glimpse of the image. To reconcile this finding with the existing literature, we suggest that global processing may serve multiple functions that extend beyond the initial glimpse of the image. Finally, we discuss future research directions, and we highlight the connections between the holistic processing account and similar theoretical perspectives and findings from other domains of visual expertise. PMID:29033865

The Holistic Processing Account of Visual Expertise in Medical Image Perception: A Review.

PubMed

Sheridan, Heather; Reingold, Eyal M

2017-01-01

In the field of medical image perception, the holistic processing perspective contends that experts can rapidly extract global information about the image, which can be used to guide their subsequent search of the image (Swensson, 1980; Nodine and Kundel, 1987; Kundel et al., 2007). In this review, we discuss the empirical evidence supporting three different predictions that can be derived from the holistic processing perspective: Expertise in medical image perception is domain-specific, experts use parafoveal and/or peripheral vision to process large regions of the image in parallel, and experts benefit from a rapid initial glimpse of an image. In addition, we discuss a pivotal recent study (Litchfield and Donovan, 2016) that seems to contradict the assumption that experts benefit from a rapid initial glimpse of the image. To reconcile this finding with the existing literature, we suggest that global processing may serve multiple functions that extend beyond the initial glimpse of the image. Finally, we discuss future research directions, and we highlight the connections between the holistic processing account and similar theoretical perspectives and findings from other domains of visual expertise.

A web service system supporting three-dimensional post-processing of medical images based on WADO protocol.

PubMed

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

2015-02-01

Three-dimensional post-processing operations on the volume data generated by a series of CT or MR images had important significance on image reading and diagnosis. As a part of the DIOCM standard, WADO service defined how to access DICOM objects on the Web, but it didn't involve three-dimensional post-processing operations on the series images. This paper analyzed the technical features of three-dimensional post-processing operations on the volume data, and then designed and implemented a web service system for three-dimensional post-processing operations of medical images based on the WADO protocol. In order to improve the scalability of the proposed system, the business tasks and calculation operations were separated into two modules. As results, it was proved that the proposed system could support three-dimensional post-processing service of medical images for multiple clients at the same moment, which met the demand of accessing three-dimensional post-processing operations on the volume data on the web.

Medical Imaging.

ERIC Educational Resources Information Center

Barker, M. C. J.

1996-01-01

Discusses four main types of medical imaging (x-ray, radionuclide, ultrasound, and magnetic resonance) and considers their relative merits. Describes important recent and possible future developments in image processing. (Author/MKR)

Volumetric image interpretation in radiology: scroll behavior and cognitive processes.

PubMed

den Boer, Larissa; van der Schaaf, Marieke F; Vincken, Koen L; Mol, Chris P; Stuijfzand, Bobby G; van der Gijp, Anouk

2018-05-16

The interpretation of medical images is a primary task for radiologists. Besides two-dimensional (2D) images, current imaging technologies allow for volumetric display of medical images. Whereas current radiology practice increasingly uses volumetric images, the majority of studies on medical image interpretation is conducted on 2D images. The current study aimed to gain deeper insight into the volumetric image interpretation process by examining this process in twenty radiology trainees who all completed four volumetric image cases. Two types of data were obtained concerning scroll behaviors and think-aloud data. Types of scroll behavior concerned oscillations, half runs, full runs, image manipulations, and interruptions. Think-aloud data were coded by a framework of knowledge and skills in radiology including three cognitive processes: perception, analysis, and synthesis. Relating scroll behavior to cognitive processes showed that oscillations and half runs coincided more often with analysis and synthesis than full runs, whereas full runs coincided more often with perception than oscillations and half runs. Interruptions were characterized by synthesis and image manipulations by perception. In addition, we investigated relations between cognitive processes and found an overall bottom-up way of reasoning with dynamic interactions between cognitive processes, especially between perception and analysis. In sum, our results highlight the dynamic interactions between these processes and the grounding of cognitive processes in scroll behavior. It suggests, that the types of scroll behavior are relevant to describe how radiologists interact with and manipulate volumetric images.

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.

A similarity-based data warehousing environment for medical images.

PubMed

Teixeira, Jefferson William; Annibal, Luana Peixoto; Felipe, Joaquim Cezar; Ciferri, Ricardo Rodrigues; Ciferri, Cristina Dutra de Aguiar

2015-11-01

A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

A Review on Medical Image Registration as an Optimization Problem

PubMed Central

Song, Guoli; Han, Jianda; Zhao, Yiwen; Wang, Zheng; Du, Huibin

2017-01-01

Objective: In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem. Methods: The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images. Result: The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process. Conclusion: The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration. PMID:28845149

Image processing based detection of lung cancer on CT scan images

NASA Astrophysics Data System (ADS)

Abdillah, Bariqi; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

In this paper, we implement and analyze the image processing method for detection of lung cancer. Image processing techniques are widely used in several medical problems for picture enhancement in the detection phase to support the early medical treatment. In this research we proposed a detection method of lung cancer based on image segmentation. Image segmentation is one of intermediate level in image processing. Marker control watershed and region growing approach are used to segment of CT scan image. Detection phases are followed by image enhancement using Gabor filter, image segmentation, and features extraction. From the experimental results, we found the effectiveness of our approach. The results show that the best approach for main features detection is watershed with masking method which has high accuracy and robust.

An open architecture for medical image workstation

NASA Astrophysics Data System (ADS)

Liang, Liang; Hu, Zhiqiang; Wang, Xiangyun

2005-04-01

Dealing with the difficulties of integrating various medical image viewing and processing technologies with a variety of clinical and departmental information systems and, in the meantime, overcoming the performance constraints in transferring and processing large-scale and ever-increasing image data in healthcare enterprise, we design and implement a flexible, usable and high-performance architecture for medical image workstations. This architecture is not developed for radiology only, but for any workstations in any application environments that may need medical image retrieving, viewing, and post-processing. This architecture contains an infrastructure named Memory PACS and different kinds of image applications built on it. The Memory PACS is in charge of image data caching, pre-fetching and management. It provides image applications with a high speed image data access and a very reliable DICOM network I/O. In dealing with the image applications, we use dynamic component technology to separate the performance-constrained modules from the flexibility-constrained modules so that different image viewing or processing technologies can be developed and maintained independently. We also develop a weakly coupled collaboration service, through which these image applications can communicate with each other or with third party applications. We applied this architecture in developing our product line and it works well. In our clinical sites, this architecture is applied not only in Radiology Department, but also in Ultrasonic, Surgery, Clinics, and Consultation Center. Giving that each concerned department has its particular requirements and business routines along with the facts that they all have different image processing technologies and image display devices, our workstations are still able to maintain high performance and high usability.

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.

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

A new concept for medical imaging centered on cellular phone technology.

PubMed

Granot, Yair; Ivorra, Antoni; Rubinsky, Boris

2008-04-30

According to World Health Organization reports, some three quarters of the world population does not have access to medical imaging. In addition, in developing countries over 50% of medical equipment that is available is not being used because it is too sophisticated or in disrepair or because the health personnel are not trained to use it. The goal of this study is to introduce and demonstrate the feasibility of a new concept in medical imaging that is centered on cellular phone technology and which may provide a solution to medical imaging in underserved areas. The new system replaces the conventional stand-alone medical imaging device with a new medical imaging system made of two independent components connected through cellular phone technology. The independent units are: a) a data acquisition device (DAD) at a remote patient site that is simple, with limited controls and no image display capability and b) an advanced image reconstruction and hardware control multiserver unit at a central site. The cellular phone technology transmits unprocessed raw data from the patient site DAD and receives and displays the processed image from the central site. (This is different from conventional telemedicine where the image reconstruction and control is at the patient site and telecommunication is used to transmit processed images from the patient site). The primary goal of this study is to demonstrate that the cellular phone technology can function in the proposed mode. The feasibility of the concept is demonstrated using a new frequency division multiplexing electrical impedance tomography system, which we have developed for dynamic medical imaging, as the medical imaging modality. The system is used to image through a cellular phone a simulation of breast cancer tumors in a medical imaging diagnostic mode and to image minimally invasive tissue ablation with irreversible electroporation in a medical imaging interventional mode.

Acquiring skill at medical image inspection: learning localized in early visual processes

NASA Astrophysics Data System (ADS)

Sowden, Paul T.; Davies, Ian R. L.; Roling, Penny; Watt, Simon J.

1997-04-01

Acquisition of the skill of medical image inspection could be due to changes in visual search processes, 'low-level' sensory learning, and higher level 'conceptual learning.' Here, we report two studies that investigate the extent to which learning in medical image inspection involves low- level learning. Early in the visual processing pathway cells are selective for direction of luminance contrast. We exploit this in the present studies by using transfer across direction of contrast as a 'marker' to indicate the level of processing at which learning occurs. In both studies twelve observers trained for four days at detecting features in x- ray images (experiment one equals discs in the Nijmegen phantom, experiment two equals micro-calcification clusters in digitized mammograms). Half the observers examined negative luminance contrast versions of the images and the remainder examined positive contrast versions. On the fifth day, observers swapped to inspect their respective opposite contrast images. In both experiments leaning occurred across sessions. In experiment one, learning did not transfer across direction of luminance contrast, while in experiment two there was only partial transfer. These findings are consistent with the contention that some of the leaning was localized early in the visual processing pathway. The implications of these results for current medical image inspection training schedules are discussed.

A comparison of ordinary fuzzy and intuitionistic fuzzy approaches in visualizing the image of flat electroencephalography

NASA Astrophysics Data System (ADS)

Zenian, Suzelawati; Ahmad, Tahir; Idris, Amidora

2017-09-01

Medical imaging is a subfield in image processing that deals with medical images. It is very crucial in visualizing the body parts in non-invasive way by using appropriate image processing techniques. Generally, image processing is used to enhance visual appearance of images for further interpretation. However, the pixel values of an image may not be precise as uncertainty arises within the gray values of an image due to several factors. In this paper, the input and output images of Flat Electroencephalography (fEEG) of an epileptic patient at varied time are presented. Furthermore, ordinary fuzzy and intuitionistic fuzzy approaches are implemented to the input images and the results are compared between these two approaches.

Medical Image Processing Server applied to Quality Control of Nuclear Medicine.

NASA Astrophysics Data System (ADS)

Vergara, C.; Graffigna, J. P.; Marino, E.; Omati, S.; Holleywell, P.

2016-04-01

This paper is framed within the area of medical image processing and aims to present the process of installation, configuration and implementation of a processing server of medical images (MIPS) in the Fundación Escuela de Medicina Nuclear located in Mendoza, Argentina (FUESMEN). It has been developed in the Gabinete de Tecnologia Médica (GA.TE.ME), Facultad de Ingeniería-Universidad Nacional de San Juan. MIPS is a software that using the DICOM standard, can receive medical imaging studies of different modalities or viewing stations, then it executes algorithms and finally returns the results to other devices. To achieve the objectives previously mentioned, preliminary tests were conducted in the laboratory. More over, tools were remotely installed in clinical enviroment. The appropiate protocols for setting up and using them in different services were established once defined those suitable algorithms. Finally, it’s important to focus on the implementation and training that is provided in FUESMEN, using nuclear medicine quality control processes. Results on implementation are exposed in this work.

Ongoing quality control in digital radiography: Report of AAPM Imaging Physics Committee Task Group 151.

PubMed

Jones, A Kyle; Heintz, Philip; Geiser, William; Goldman, Lee; Jerjian, Khachig; Martin, Melissa; Peck, Donald; Pfeiffer, Douglas; Ranger, Nicole; Yorkston, John

2015-11-01

Quality control (QC) in medical imaging is an ongoing process and not just a series of infrequent evaluations of medical imaging equipment. The QC process involves designing and implementing a QC program, collecting and analyzing data, investigating results that are outside the acceptance levels for the QC program, and taking corrective action to bring these results back to an acceptable level. The QC process involves key personnel in the imaging department, including the radiologist, radiologic technologist, and the qualified medical physicist (QMP). The QMP performs detailed equipment evaluations and helps with oversight of the QC program, the radiologic technologist is responsible for the day-to-day operation of the QC program. The continued need for ongoing QC in digital radiography has been highlighted in the scientific literature. The charge of this task group was to recommend consistency tests designed to be performed by a medical physicist or a radiologic technologist under the direction of a medical physicist to identify problems with an imaging system that need further evaluation by a medical physicist, including a fault tree to define actions that need to be taken when certain fault conditions are identified. The focus of this final report is the ongoing QC process, including rejected image analysis, exposure analysis, and artifact identification. These QC tasks are vital for the optimal operation of a department performing digital radiography.

Ongoing quality control in digital radiography: Report of AAPM Imaging Physics Committee Task Group 151

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

Jones, A. Kyle, E-mail: kyle.jones@mdanderson.org; Geiser, William; Heintz, Philip

Quality control (QC) in medical imaging is an ongoing process and not just a series of infrequent evaluations of medical imaging equipment. The QC process involves designing and implementing a QC program, collecting and analyzing data, investigating results that are outside the acceptance levels for the QC program, and taking corrective action to bring these results back to an acceptable level. The QC process involves key personnel in the imaging department, including the radiologist, radiologic technologist, and the qualified medical physicist (QMP). The QMP performs detailed equipment evaluations and helps with oversight of the QC program, the radiologic technologist ismore » responsible for the day-to-day operation of the QC program. The continued need for ongoing QC in digital radiography has been highlighted in the scientific literature. The charge of this task group was to recommend consistency tests designed to be performed by a medical physicist or a radiologic technologist under the direction of a medical physicist to identify problems with an imaging system that need further evaluation by a medical physicist, including a fault tree to define actions that need to be taken when certain fault conditions are identified. The focus of this final report is the ongoing QC process, including rejected image analysis, exposure analysis, and artifact identification. These QC tasks are vital for the optimal operation of a department performing digital radiography.« less

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.

Web-based interactive 2D/3D medical image processing and visualization software.

PubMed

Mahmoudi, Seyyed Ehsan; Akhondi-Asl, Alireza; Rahmani, Roohollah; Faghih-Roohi, Shahrooz; Taimouri, Vahid; Sabouri, Ahmad; Soltanian-Zadeh, Hamid

2010-05-01

There are many medical image processing software tools available for research and diagnosis purposes. However, most of these tools are available only as local applications. This limits the accessibility of the software to a specific machine, and thus the data and processing power of that application are not available to other workstations. Further, there are operating system and processing power limitations which prevent such applications from running on every type of workstation. By developing web-based tools, it is possible for users to access the medical image processing functionalities wherever the internet is available. In this paper, we introduce a pure web-based, interactive, extendable, 2D and 3D medical image processing and visualization application that requires no client installation. Our software uses a four-layered design consisting of an algorithm layer, web-user-interface layer, server communication layer, and wrapper layer. To compete with extendibility of the current local medical image processing software, each layer is highly independent of other layers. A wide range of medical image preprocessing, registration, and segmentation methods are implemented using open source libraries. Desktop-like user interaction is provided by using AJAX technology in the web-user-interface. For the visualization functionality of the software, the VRML standard is used to provide 3D features over the web. Integration of these technologies has allowed implementation of our purely web-based software with high functionality without requiring powerful computational resources in the client side. The user-interface is designed such that the users can select appropriate parameters for practical research and clinical studies. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.

PubMed

Monteiro, Eriksson; Costa, Carlos; Oliveira, José Luís

2017-05-01

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

Near-infrared spectroscopic tissue imaging for medical applications

DOEpatents

Demos,; Stavros, Staggs [Livermore, CA; Michael, C [Tracy, CA

2006-03-21

Near infrared imaging using elastic light scattering and tissue autofluorescence are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Near-infrared spectroscopic tissue imaging for medical applications

DOEpatents

Demos, Stavros [Livermore, CA; Staggs, Michael C [Tracy, CA

2006-12-12

Near infrared imaging using elastic light scattering and tissue autofluorescence are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Three-dimensional imaging technology offers promise in medicine.

PubMed

Karako, Kenji; Wu, Qiong; Gao, Jianjun

2014-04-01

Medical imaging plays an increasingly important role in the diagnosis and treatment of disease. Currently, medical equipment mainly has two-dimensional (2D) imaging systems. Although this conventional imaging largely satisfies clinical requirements, it cannot depict pathologic changes in 3 dimensions. The development of three-dimensional (3D) imaging technology has encouraged advances in medical imaging. Three-dimensional imaging technology offers doctors much more information on a pathology than 2D imaging, thus significantly improving diagnostic capability and the quality of treatment. Moreover, the combination of 3D imaging with augmented reality significantly improves surgical navigation process. The advantages of 3D imaging technology have made it an important component of technological progress in the field of medical imaging.

Architecture of distributed picture archiving and communication systems for storing and processing high resolution medical images

NASA Astrophysics Data System (ADS)

Tokareva, Victoria

2018-04-01

New generation medicine demands a better quality of analysis increasing the amount of data collected during checkups, and simultaneously decreasing the invasiveness of a procedure. Thus it becomes urgent not only to develop advanced modern hardware, but also to implement special software infrastructure for using it in everyday clinical practice, so-called Picture Archiving and Communication Systems (PACS). Developing distributed PACS is a challenging task for nowadays medical informatics. The paper discusses the architecture of distributed PACS server for processing large high-quality medical images, with respect to technical specifications of modern medical imaging hardware, as well as international standards in medical imaging software. The MapReduce paradigm is proposed for image reconstruction by server, and the details of utilizing the Hadoop framework for this task are being discussed in order to provide the design of distributed PACS as ergonomic and adapted to the needs of end users as possible.

FAST: framework for heterogeneous medical image computing and visualization.

PubMed

Smistad, Erik; Bozorgi, Mohammadmehdi; Lindseth, Frank

2015-11-01

Computer systems are becoming increasingly heterogeneous in the sense that they consist of different processors, such as multi-core CPUs and graphic processing units. As the amount of medical image data increases, it is crucial to exploit the computational power of these processors. However, this is currently difficult due to several factors, such as driver errors, processor differences, and the need for low-level memory handling. This paper presents a novel FrAmework for heterogeneouS medical image compuTing and visualization (FAST). The framework aims to make it easier to simultaneously process and visualize medical images efficiently on heterogeneous systems. FAST uses common image processing programming paradigms and hides the details of memory handling from the user, while enabling the use of all processors and cores on a system. The framework is open-source, cross-platform and available online. Code examples and performance measurements are presented to show the simplicity and efficiency of FAST. The results are compared to the insight toolkit (ITK) and the visualization toolkit (VTK) and show that the presented framework is faster with up to 20 times speedup on several common medical imaging algorithms. FAST enables efficient medical image computing and visualization on heterogeneous systems. Code examples and performance evaluations have demonstrated that the toolkit is both easy to use and performs better than existing frameworks, such as ITK and VTK.

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.

The whole mesh deformation model: a fast image segmentation method suitable for effective parallelization

NASA Astrophysics Data System (ADS)

Lenkiewicz, Przemyslaw; Pereira, Manuela; Freire, Mário M.; Fernandes, José

2013-12-01

In this article, we propose a novel image segmentation method called the whole mesh deformation (WMD) model, which aims at addressing the problems of modern medical imaging. Such problems have raised from the combination of several factors: (1) significant growth of medical image volumes sizes due to increasing capabilities of medical acquisition devices; (2) the will to increase the complexity of image processing algorithms in order to explore new functionality; (3) change in processor development and turn towards multi processing units instead of growing bus speeds and the number of operations per second of a single processing unit. Our solution is based on the concept of deformable models and is characterized by a very effective and precise segmentation capability. The proposed WMD model uses a volumetric mesh instead of a contour or a surface to represent the segmented shapes of interest, which allows exploiting more information in the image and obtaining results in shorter times, independently of image contents. The model also offers a good ability for topology changes and allows effective parallelization of workflow, which makes it a very good choice for large datasets. We present a precise model description, followed by experiments on artificial images and real medical data.

GPU computing in medical physics: a review.

PubMed

Pratx, Guillem; Xing, Lei

2011-05-01

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.

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.

Color transfer algorithm in medical images

NASA Astrophysics Data System (ADS)

Wang, Weihong; Xu, Yangfa

2007-12-01

In digital virtual human project, image data acquires from the freezing slice of human body specimen. The color and brightness between a group of images of a certain organ could be quite different. The quality of these images could bring great difficulty in edge extraction, segmentation, as well as 3D reconstruction process. Thus it is necessary to unify the color of the images. The color transfer algorithm is a good algorithm to deal with this kind of problem. This paper introduces the principle of this algorithm and uses it in the medical image processing.

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.

An open data mining framework for the analysis of medical images: application on obstructive nephropathy microscopy images.

PubMed

Doukas, Charalampos; Goudas, Theodosis; Fischer, Simon; Mierswa, Ingo; Chatziioannou, Aristotle; Maglogiannis, Ilias

2010-01-01

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

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.

The semiotics of medical image Segmentation.

PubMed

Baxter, John S H; Gibson, Eli; Eagleson, Roy; Peters, Terry M

2018-02-01

As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical image segmentation. Taking advantage of Peircean semiotic traditions and new philosophical inquiry into the structure and quality of metaphors, we can construct a unified framework for the interpretation of medical image segmentation as a sign exchange in which each sign acts as an interface metaphor. This allows for a notion of finite semiosis, described through a schematic medium, that can rigorously describe how clinicians and computers interpret the signs mediating their interaction. Altogether, this framework provides a unified approach to the understanding and development of medical image segmentation interfaces. Copyright © 2017 Elsevier B.V. All rights reserved.

Integration of medical imaging into a multi-institutional hospital information system structure.

PubMed

Dayhoff, R E

1995-01-01

The Department of Veterans Affairs (VA) is providing integrated text and image data to its clinical users at its Washington and Baltimore medical centers and, soon, at nine other medical centers. The DHCP Imaging System records clinically significant diagnostic images selected by medical specialists in a variety of departments, including cardiology, gastroenterology, pathology, dermatology, surgery, radiology, podiatry, dentistry, and emergency medicine. These images, which include color and gray scale images, and electrocardiogram waveforms, are displayed on workstations located throughout the medical centers. Integration of clinical images with the VA's electronic mail system allows transfer of data from one medical center to another. The ability to incorporate transmitted text and image data into on-line patient records at the collaborating sites is an important aspect of professional consultation. In order to achieve the maximum benefits from an integrated patient record system, a critical mass of information must be available for clinicians. When there is also seamless support for administration, it becomes possible to re-engineer the processes involved in providing medical care.

Medical image informatics infrastructure design and applications.

PubMed

Huang, H K; Wong, S T; Pietka, E

1997-01-01

Picture archiving and communication systems (PACS) is a system integration of multimodality images and health information systems designed for improving the operation of a radiology department. As it evolves, PACS becomes a hospital image document management system with a voluminous image and related data file repository. A medical image informatics infrastructure can be designed to take advantage of existing data, providing PACS with add-on value for health care service, research, and education. A medical image informatics infrastructure (MIII) consists of the following components: medical images and associated data (including PACS database), image processing, data/knowledge base management, visualization, graphic user interface, communication networking, and application oriented software. This paper describes these components and their logical connection, and illustrates some applications based on the concept of the MIII.

Quantitative assessment of the impact of biomedical image acquisition on the results obtained from image analysis and processing.

PubMed

Koprowski, Robert

2014-07-04

Dedicated, automatic algorithms for image analysis and processing are becoming more and more common in medical diagnosis. When creating dedicated algorithms, many factors must be taken into consideration. They are associated with selecting the appropriate algorithm parameters and taking into account the impact of data acquisition on the results obtained. An important feature of algorithms is the possibility of their use in other medical units by other operators. This problem, namely operator's (acquisition) impact on the results obtained from image analysis and processing, has been shown on a few examples. The analysed images were obtained from a variety of medical devices such as thermal imaging, tomography devices and those working in visible light. The objects of imaging were cellular elements, the anterior segment and fundus of the eye, postural defects and others. In total, almost 200'000 images coming from 8 different medical units were analysed. All image analysis algorithms were implemented in C and Matlab. For various algorithms and methods of medical imaging, the impact of image acquisition on the results obtained is different. There are different levels of algorithm sensitivity to changes in the parameters, for example: (1) for microscope settings and the brightness assessment of cellular elements there is a difference of 8%; (2) for the thyroid ultrasound images there is a difference in marking the thyroid lobe area which results in a brightness assessment difference of 2%. The method of image acquisition in image analysis and processing also affects: (3) the accuracy of determining the temperature in the characteristic areas on the patient's back for the thermal method - error of 31%; (4) the accuracy of finding characteristic points in photogrammetric images when evaluating postural defects - error of 11%; (5) the accuracy of performing ablative and non-ablative treatments in cosmetology - error of 18% for the nose, 10% for the cheeks, and 7% for the forehead. Similarly, when: (7) measuring the anterior eye chamber - there is an error of 20%; (8) measuring the tooth enamel thickness - error of 15%; (9) evaluating the mechanical properties of the cornea during pressure measurement - error of 47%. The paper presents vital, selected issues occurring when assessing the accuracy of designed automatic algorithms for image analysis and processing in bioengineering. The impact of acquisition of images on the problems arising in their analysis has been shown on selected examples. It has also been indicated to which elements of image analysis and processing special attention should be paid in their design.

75 FR 68200 - Medical Devices; Radiology Devices; Reclassification of Full-Field Digital Mammography System

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-05

... exposure control, image processing and reconstruction programs, patient and equipment supports, component..., acquisition workstation, automatic exposure control, image processing and reconstruction programs, patient and... may include was revised by adding automatic exposure control, image processing and reconstruction...

An interactive medical image segmentation framework using iterative refinement.

PubMed

Kalshetti, Pratik; Bundele, Manas; Rahangdale, Parag; Jangra, Dinesh; Chattopadhyay, Chiranjoy; Harit, Gaurav; Elhence, Abhay

2017-04-01

Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Segmentation of anatomical structures of the heart based on echocardiography

NASA Astrophysics Data System (ADS)

Danilov, V. V.; Skirnevskiy, I. P.; Gerget, O. M.

2017-01-01

Nowadays, many practical applications in the field of medical image processing require valid and reliable segmentation of images in the capacity of input data. Some of the commonly used imaging techniques are ultrasound, CT, and MRI. However, the main difference between the other medical imaging equipment and EchoCG is that it is safer, low cost, non-invasive and non-traumatic. Three-dimensional EchoCG is a non-invasive imaging modality that is complementary and supplementary to two-dimensional imaging and can be used to examine the cardiovascular function and anatomy in different medical settings. The challenging problems, presented by EchoCG image processing, such as speckle phenomena, noise, temporary non-stationarity of processes, unsharp boundaries, attenuation, etc. forced us to consider and compare existing methods and then to develop an innovative approach that can tackle the problems connected with clinical applications. Actual studies are related to the analysis and development of a cardiac parameters automatic detection system by EchoCG that will provide new data on the dynamics of changes in cardiac parameters and improve the accuracy and reliability of the diagnosis. Research study in image segmentation has highlighted the capabilities of image-based methods for medical applications. The focus of the research is both theoretical and practical aspects of the application of the methods. Some of the segmentation approaches can be interesting for the imaging and medical community. Performance evaluation is carried out by comparing the borders, obtained from the considered methods to those manually prescribed by a medical specialist. Promising results demonstrate the possibilities and the limitations of each technique for image segmentation problems. The developed approach allows: to eliminate errors in calculating the geometric parameters of the heart; perform the necessary conditions, such as speed, accuracy, reliability; build a master model that will be an indispensable assistant for operations on a beating heart.

WE-E-12A-01: Medical Physics 1.0 to 2.0: MRI, Displays, Informatics

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

Pickens, D; Flynn, M; Peck, D

Medical Physics 2.0 is a bold vision for an existential transition of clinical imaging physics in face of the new realities of value-based and evidence-based medicine, comparative effectiveness, and meaningful use. It speaks to how clinical imaging physics can expand beyond traditional insular models of inspection and acceptance testing, oriented toward compliance, towards team-based models of operational engagement, prospective definition and assurance of effective use, and retrospective evaluation of clinical performance. Organized into four sessions of the AAPM, this particular session focuses on three specific modalities as outlined below. MRI 2.0: This presentation will look into the future of clinicalmore » MR imaging and what the clinical medical physicist will need to be doing as the technology of MR imaging evolves. Many of the measurement techniques used today will need to be expanded to address the advent of higher field imaging systems and dedicated imagers for specialty applications. Included will be the need to address quality assurance and testing metrics for multi-channel MR imagers and hybrid devices such as MR/PET systems. New pulse sequences and acquisition methods, increasing use of MR spectroscopy, and real-time guidance procedures will place the burden on the medical physicist to define and use new tools to properly evaluate these systems, but the clinical applications must be understood so that these tools are use correctly. Finally, new rules, clinical requirements, and regulations will mean that the medical physicist must actively work to keep her/his sites compliant and must work closely with physicians to ensure best performance of these systems. Informatics Display 1.0 to 2.0: Medical displays are an integral part of medical imaging operation. The DICOM and AAPM (TG18) efforts have led to clear definitions of performance requirements of monochrome medical displays that can be followed by medical physicists to ensure proper performance. However, effective implementation of that oversight has been challenging due to the number and extend of medical displays in use at a facility. The advent of color display and mobile displays has added additional challenges to the task of the medical physicist. This informatics display lecture first addresses the current display guidelines (the 1.0 paradigm) and further outlines the initiatives and prospects for color and mobile displays (the 2.0 paradigm). Informatics Management 1.0 to 2.0: Imaging informatics is part of every radiology practice today. Imaging informatics covers everything from the ordering of a study, through the data acquisition and processing, display and archiving, reporting of findings and the billing for the services performed. The standardization of the processes used to manage the information and methodologies to integrate these standards is being developed and advanced continuously. These developments are done in an open forum and imaging organizations and professionals all have a part in the process. In the Informatics Management presentation, the flow of information and the integration of the standards used in the processes will be reviewed. The role of radiologists and physicists in the process will be discussed. Current methods (the 1.0 paradigm) and evolving methods (the 2.0 paradigm) for validation of informatics systems function will also be discussed. Learning Objectives: Identify requirements for improving quality assurance and compliance tools for advanced and hybrid MRI systems. Identify the need for new quality assurance metrics and testing procedures for advanced systems. Identify new hardware systems and new procedures needed to evaluate MRI systems. Understand the components of current medical physics expectation for medical displays. Understand the role and prospect fo medical physics for color and mobile display devices. Understand different areas of imaging informatics and the methodology for developing informatics standards. Understand the current status of informatics standards and the role of physicists and radiologists in the process, and the current technology for validating the function of these systems.« less

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.

Improving healthcare services using web based platform for management of medical case studies.

PubMed

Ogescu, Cristina; Plaisanu, Claudiu; Udrescu, Florian; Dumitru, Silviu

2008-01-01

The paper presents a web based platform for management of medical cases, support for healthcare specialists in taking the best clinical decision. Research has been oriented mostly on multimedia data management, classification algorithms for querying, retrieving and processing different medical data types (text and images). The medical case studies can be accessed by healthcare specialists and by students as anonymous case studies providing trust and confidentiality in Internet virtual environment. The MIDAS platform develops an intelligent framework to manage sets of medical data (text, static or dynamic images), in order to optimize the diagnosis and the decision process, which will reduce the medical errors and will increase the quality of medical act. MIDAS is an integrated project working on medical information retrieval from heterogeneous, distributed medical multimedia database.

Intelligent distributed medical image management

NASA Astrophysics Data System (ADS)

Garcia, Hong-Mei C.; Yun, David Y.

1995-05-01

The rapid advancements in high performance global communication have accelerated cooperative image-based medical services to a new frontier. Traditional image-based medical services such as radiology and diagnostic consultation can now fully utilize multimedia technologies in order to provide novel services, including remote cooperative medical triage, distributed virtual simulation of operations, as well as cross-country collaborative medical research and training. Fast (efficient) and easy (flexible) retrieval of relevant images remains a critical requirement for the provision of remote medical services. This paper describes the database system requirements, identifies technological building blocks for meeting the requirements, and presents a system architecture for our target image database system, MISSION-DBS, which has been designed to fulfill the goals of Project MISSION (medical imaging support via satellite integrated optical network) -- an experimental high performance gigabit satellite communication network with access to remote supercomputing power, medical image databases, and 3D visualization capabilities in addition to medical expertise anywhere and anytime around the country. The MISSION-DBS design employs a synergistic fusion of techniques in distributed databases (DDB) and artificial intelligence (AI) for storing, migrating, accessing, and exploring images. The efficient storage and retrieval of voluminous image information is achieved by integrating DDB modeling and AI techniques for image processing while the flexible retrieval mechanisms are accomplished by combining attribute- based and content-based retrievals.

Hello World Deep Learning in Medical Imaging.

PubMed

Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George

2018-05-03

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

Colour application on mammography image segmentation

NASA Astrophysics Data System (ADS)

Embong, R.; Aziz, N. M. Nik Ab.; Karim, A. H. Abd; Ibrahim, M. R.

2017-09-01

The segmentation process is one of the most important steps in image processing and computer vision since it is vital in the initial stage of image analysis. Segmentation of medical images involves complex structures and it requires precise segmentation result which is necessary for clinical diagnosis such as the detection of tumour, oedema, and necrotic tissues. Since mammography images are grayscale, researchers are looking at the effect of colour in the segmentation process of medical images. Colour is known to play a significant role in the perception of object boundaries in non-medical colour images. Processing colour images require handling more data, hence providing a richer description of objects in the scene. Colour images contain ten percent (10%) additional edge information as compared to their grayscale counterparts. Nevertheless, edge detection in colour image is more challenging than grayscale image as colour space is considered as a vector space. In this study, we implemented red, green, yellow, and blue colour maps to grayscale mammography images with the purpose of testing the effect of colours on the segmentation of abnormality regions in the mammography images. We applied the segmentation process using the Fuzzy C-means algorithm and evaluated the percentage of average relative error of area for each colour type. The results showed that all segmentation with the colour map can be done successfully even for blurred and noisy images. Also the size of the area of the abnormality region is reduced when compare to the segmentation area without the colour map. The green colour map segmentation produced the smallest percentage of average relative error (10.009%) while yellow colour map segmentation gave the largest percentage of relative error (11.367%).

Image Reconstruction is a New Frontier of Machine Learning.

PubMed

Wang, Ge; Ye, Jong Chu; Mueller, Klaus; Fessler, Jeffrey A

2018-06-01

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of "Machine learning for image reconstruction." This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme "Deep learning in medical imaging" [item 2) in the Appendix]. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings.

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.

The Impact of a Health IT Changeover on Medical Imaging Department Work Processes and Turnaround Times

PubMed Central

Georgiou, A.; Lymer, S.; Hordern, A.; Ridley, L.; Westbrook, J.

2015-01-01

Summary Objectives To assess the impact of introducing a new Picture Archiving and Communication System (PACS) and Radiology Information System (RIS) on: (i) Medical Imaging work processes; and (ii) turnaround times (TATs) for x-ray and CT scan orders initiated in the Emergency Department (ED). Methods We employed a mixed method study design comprising: (i) semi-structured interviews with Medical Imaging Department staff; and (ii) retrospectively extracted ED data before (March/April 2010) and after (March/April 2011 and 2012) the introduction of a new PACS/RIS. TATs were calculated as: processing TAT (median time from image ordering to examination) and reporting TAT (median time from examination to final report). Results Reporting TAT for x-rays decreased significantly after introduction of the new PACS/RIS; from a median of 76 hours to 38 hours per order (p<.0001) for patients discharged from the ED, and from 84 hours to 35 hours (p<.0001) for patients admitted to hospital. Medical Imaging staff reported that the changeover to the new PACS/RIS led to gains in efficiency, particularly regarding the accessibility of images and patient-related information. Nevertheless, assimilation of the new PACS/RIS with existing Departmental work processes was considered inadequate and in some instances unsafe. Issues highlighted related to the synchronization of work tasks (e.g., porter arrangements) and the material set up of the work place (e.g., the number and location of computers). Conclusions The introduction of new health IT can be a “double-edged sword” providing improved efficiency but at the same time introducing potential hazards affecting the effectiveness of the Medical Imaging Department. PMID:26448790

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service.

PubMed

Bao, Shunxing; Plassard, Andrew J; Landman, Bennett A; Gokhale, Aniruddha

2017-04-01

Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance from these approaches is, however, impeded by standard network switches since they can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. To that end, a cloud-based "medical image processing-as-a-service" offers promise in utilizing the ecosystem of Apache Hadoop, which is a flexible framework providing distributed, scalable, fault tolerant storage and parallel computational modules, and HBase, which is a NoSQL database built atop Hadoop's distributed file system. Despite this promise, HBase's load distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). This paper makes two contributions to address these concerns by describing key cloud engineering principles and technology enhancements we made to the Apache Hadoop ecosystem for medical imaging applications. First, we propose a row-key design for HBase, which is a necessary step that is driven by the hierarchical organization of imaging data. Second, we propose a novel data allocation policy within HBase to strongly enforce collocation of hierarchically related imaging data. The proposed enhancements accelerate data processing by minimizing network usage and localizing processing to machines where the data already exist. Moreover, our approach is amenable to the traditional scan, subject, and project-level analysis procedures, and is compatible with standard command line/scriptable image processing software. Experimental results for an illustrative sample of imaging data reveals that our new HBase policy results in a three-fold time improvement in conversion of classic DICOM to NiFTI file formats when compared with the default HBase region split policy, and nearly a six-fold improvement over a commonly available network file system (NFS) approach even for relatively small file sets. Moreover, file access latency is lower than network attached storage.

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.

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

[A solution for display and processing of DICOM images in web PACS].

PubMed

Xue, Wei-jing; Lu, Wen; Wang, Hai-yang; Meng, Jian

2009-03-01

Use the technique of Java Applet to realize the supporting of DICOM image in ordinary Web browser, thereby to expand the processing function of medical image. First analyze the format of DICOM file and design a class which can acquire the pixels, then design two Applet classes, of which one is used to disposal the DICOM image, the other is used to display DICOM image that have been disposaled in the first Applet. They all embedded in the View page, and they communicate by Applet Context object. The method designed in this paper can make users display and process DICOM images directly by using ordinary Web browser, which makes Web PACS not only have the advantages of B/S model, but also have the advantages of the C/S model. Java Applet is the key for expanding the Web browser's function in Web PACS, which provides a guideline to sharing of medical images.

Finding glenoid surface on scapula in 3D medical images for shoulder joint implant operation planning: 3D OCR

NASA Astrophysics Data System (ADS)

Mohammad Sadeghi, Majid; Kececi, Emin Faruk; Bilsel, Kerem; Aralasmak, Ayse

2017-03-01

Medical imaging has great importance in earlier detection, better treatment and follow-up of diseases. 3D Medical image analysis with CT Scan and MRI images has also been used to aid surgeries by enabling patient specific implant fabrication, where having a precise three dimensional model of associated body parts is essential. In this paper, a 3D image processing methodology for finding the plane on which the glenoid surface has a maximum surface area is proposed. Finding this surface is the first step in designing patient specific shoulder joint implant.

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.

The clinical information system GastroBase: integration of image processing and laboratory communication.

PubMed

Kocna, P

1995-01-01

GastroBase, a clinical information system, incorporates patient identification, medical records, images, laboratory data, patient history, physical examination, and other patient-related information. Program modules are written in C; all data is processed using Novell-Btrieve data manager. Patient identification database represents the main core of this information systems. A graphic library developed in the past year and graphic modules with a special video-card enables the storing, archiving, and linking of different images to the electronic patient-medical-record. GastroBase has been running for more than four years in daily routine and the database contains more than 25,000 medical records and 1,500 images. This new version of GastroBase is now incorporated into the clinical information system of University Clinic in Prague.

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.

A specialized plug-in software module for computer-aided quantitative measurement of medical images.

PubMed

Wang, Q; Zeng, Y J; Huo, P; Hu, J L; Zhang, J H

2003-12-01

This paper presents a specialized system for quantitative measurement of medical images. Using Visual C++, we developed a computer-aided software based on Image-Pro Plus (IPP), a software development platform. When transferred to the hard disk of a computer by an MVPCI-V3A frame grabber, medical images can be automatically processed by our own IPP plug-in for immunohistochemical analysis, cytomorphological measurement and blood vessel segmentation. In 34 clinical studies, the system has shown its high stability, reliability and ease of utility.

The role of extra-foveal processing in 3D imaging

NASA Astrophysics Data System (ADS)

Eckstein, Miguel P.; Lago, Miguel A.; Abbey, Craig K.

2017-03-01

The field of medical image quality has relied on the assumption that metrics of image quality for simple visual detection tasks are a reliable proxy for the more clinically realistic visual search tasks. Rank order of signal detectability across conditions often generalizes from detection to search tasks. Here, we argue that search in 3D images represents a paradigm shift in medical imaging: radiologists typically cannot exhaustively scrutinize all regions of interest with the high acuity fovea requiring detection of signals with extra-foveal areas (visual periphery) of the human retina. We hypothesize that extra-foveal processing can alter the detectability of certain types of signals in medical images with important implications for search in 3D medical images. We compare visual search of two different types of signals in 2D vs. 3D images. We show that a small microcalcification-like signal is more highly detectable than a larger mass-like signal in 2D search, but its detectability largely decreases (relative to the larger signal) in the 3D search task. Utilizing measurements of observer detectability as a function retinal eccentricity and observer eye fixations we can predict the pattern of results in the 2D and 3D search studies. Our findings: 1) suggest that observer performance findings with 2D search might not always generalize to 3D search; 2) motivate the development of a new family of model observers that take into account the inhomogeneous visual processing across the retina (foveated model observers).

Medical image diagnoses by artificial neural networks with image correlation, wavelet transform, simulated annealing

NASA Astrophysics Data System (ADS)

Szu, Harold H.

1993-09-01

Classical artificial neural networks (ANN) and neurocomputing are reviewed for implementing a real time medical image diagnosis. An algorithm known as the self-reference matched filter that emulates the spatio-temporal integration ability of the human visual system might be utilized for multi-frame processing of medical imaging data. A Cauchy machine, implementing a fast simulated annealing schedule, can determine the degree of abnormality by the degree of orthogonality between the patient imagery and the class of features of healthy persons. An automatic inspection process based on multiple modality image sequences is simulated by incorporating the following new developments: (1) 1-D space-filling Peano curves to preserve the 2-D neighborhood pixels' relationship; (2) fast simulated Cauchy annealing for the global optimization of self-feature extraction; and (3) a mini-max energy function for the intra-inter cluster-segregation respectively useful for top-down ANN designs.

Hierarchical and successive approximate registration of the non-rigid medical image based on thin-plate splines

NASA Astrophysics Data System (ADS)

Hu, Jinyan; Li, Li; Yang, Yunfeng

2017-06-01

The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.

Development of imaging biomarkers and generation of big data.

PubMed

Alberich-Bayarri, Ángel; Hernández-Navarro, Rafael; Ruiz-Martínez, Enrique; García-Castro, Fabio; García-Juan, David; Martí-Bonmatí, Luis

2017-06-01

Several image processing algorithms have emerged to cover unmet clinical needs but their application to radiological routine with a clear clinical impact is still not straightforward. Moving from local to big infrastructures, such as Medical Imaging Biobanks (millions of studies), or even more, Federations of Medical Imaging Biobanks (in some cases totaling to hundreds of millions of studies) require the integration of automated pipelines for fast analysis of pooled data to extract clinically relevant conclusions, not uniquely linked to medical imaging, but in combination to other information such as genetic profiling. A general strategy for the development of imaging biomarkers and their integration in the cloud for the quantitative management and exploitation in large databases is herein presented. The proposed platform has been successfully launched and is being validated nowadays among the early adopters' community of radiologists, clinicians, and medical imaging researchers.

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.

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.

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

The application of use case modeling in designing medical imaging information systems.

PubMed

Safdari, Reza; Farzi, Jebraeil; Ghazisaeidi, Marjan; Mirzaee, Mahboobeh; Goodini, Azadeh

2013-01-01

Introduction. The essay at hand is aimed at examining the application of use case modeling in analyzing and designing information systems to support Medical Imaging services. Methods. The application of use case modeling in analyzing and designing health information systems was examined using electronic databases (Pubmed, Google scholar) resources and the characteristics of the modeling system and its effect on the development and design of the health information systems were analyzed. Results. Analyzing the subject indicated that Provident modeling of health information systems should provide for quick access to many health data resources in a way that patients' data can be used in order to expand distant services and comprehensive Medical Imaging advices. Also these experiences show that progress in the infrastructure development stages through gradual and repeated evolution process of user requirements is stronger and this can lead to a decline in the cycle of requirements engineering process in the design of Medical Imaging information systems. Conclusion. Use case modeling approach can be effective in directing the problems of health and Medical Imaging information systems towards understanding, focusing on the start and analysis, better planning, repetition, and control.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Medical image segmentation based on SLIC superpixels model

NASA Astrophysics Data System (ADS)

Chen, Xiang-ting; Zhang, Fan; Zhang, Ruo-ya

2017-01-01

Medical imaging has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, medical images have many unstable factors such as complex imaging mechanism, the target displacement will cause constructed defect and the partial volume effect will lead to error and equipment wear, which increases the complexity of subsequent image processing greatly. The segmentation algorithm which based on SLIC (Simple Linear Iterative Clustering, SLIC) superpixels is used to eliminate the influence of constructed defect and noise by means of the feature similarity in the preprocessing stage. At the same time, excellent clustering effect can reduce the complexity of the algorithm extremely, which provides an effective basis for the rapid diagnosis of experts.

Web-based platform for collaborative medical imaging research

NASA Astrophysics Data System (ADS)

Rittner, Leticia; Bento, Mariana P.; Costa, André L.; Souza, Roberto M.; Machado, Rubens C.; Lotufo, Roberto A.

2015-03-01

Medical imaging research depends basically on the availability of large image collections, image processing and analysis algorithms, hardware and a multidisciplinary research team. It has to be reproducible, free of errors, fast, accessible through a large variety of devices spread around research centers and conducted simultaneously by a multidisciplinary team. Therefore, we propose a collaborative research environment, named Adessowiki, where tools and datasets are integrated and readily available in the Internet through a web browser. Moreover, processing history and all intermediate results are stored and displayed in automatic generated web pages for each object in the research project or clinical study. It requires no installation or configuration from the client side and offers centralized tools and specialized hardware resources, since processing takes place in the cloud.

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.

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.

Image analysis and modeling in medical image computing. Recent developments and advances.

PubMed

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

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.

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.

Migration of medical image data archived using mini-PACS to full-PACS.

PubMed

Jung, Haijo; Kim, Hee-Joung; Kang, Won-Suk; Lee, Sang-Ho; Kim, Sae-Rome; Ji, Chang Lyong; Kim, Jung-Han; Yoo, Sun Kook; Kim, Ki-Hwang

2004-06-01

This study evaluated the migration to full-PACS of medical image data archived using mini-PACS at two hospitals of the Yonsei University Medical Center, Seoul, Korea. A major concern in the migration of medical data is to match the image data from the mini-PACS with the hospital OCS (Ordered Communication System). Prior to carrying out the actual migration process, the principles, methods, and anticipated results for the migration with respect to both cost and effectiveness were evaluated. Migration gateway workstations were established and a migration software tool was developed. The actual migration process was performed based on the results of several migration simulations. Our conclusions were that a migration plan should be carefully prepared and tailored to the individual hospital environment because the server system, archive media, network, OCS, and policy for data management may be unique.

Medical ultrasound - From inner space to outer space

NASA Technical Reports Server (NTRS)

Rooney, J. A.

1984-01-01

During the last decade, medical ultrasound has rapidly become a widely accepted imaging modality used in many medical specialties. It has the advantages that it is noninvasive, does not use ionizing radiation, is relatively inexpensive and is easy to use. Future trends in ultrasound include expanded areas of use, advanced signal processing and digital image analysis including tissue characterization and three-dimensional reconstructions.

Hadoop-based implementation of processing medical diagnostic records for visual patient system

NASA Astrophysics Data System (ADS)

Yang, Yuanyuan; Shi, Liehang; Xie, Zhe; Zhang, Jianguo

2018-03-01

We have innovatively introduced Visual Patient (VP) concept and method visually to represent and index patient imaging diagnostic records (IDR) in last year SPIE Medical Imaging (SPIE MI 2017), which can enable a doctor to review a large amount of IDR of a patient in a limited appointed time slot. In this presentation, we presented a new approach to design data processing architecture of VP system (VPS) to acquire, process and store various kinds of IDR to build VP instance for each patient in hospital environment based on Hadoop distributed processing structure. We designed this system architecture called Medical Information Processing System (MIPS) with a combination of Hadoop batch processing architecture and Storm stream processing architecture. The MIPS implemented parallel processing of various kinds of clinical data with high efficiency, which come from disparate hospital information system such as PACS, RIS LIS and HIS.

Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data.

PubMed

Fischer, Felix; Selver, M Alper; Gezer, Sinem; Dicle, Oğuz; Hillen, Walter

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data. The Grayscale Softcopy Presentation State extension of the Digital Imaging and Communications in Medicine (DICOM) standard resolves this issue for two-dimensional (2D) data by introducing an extensive set of parameters, namely 2D Presentation States (2DPR), that describe how an image should be displayed. 2DPR allows storing these parameters instead of storing parameter applied images, which cause unnecessary duplication of the image data. Since there is currently no corresponding extension for 3D data, in this study, a DICOM-compliant object called 3D presentation states (3DPR) is proposed for the parameterization and storage of 3D medical volumes. To accomplish this, the 3D medical visualization process is divided into four tasks, namely pre-processing, segmentation, post-processing, and rendering. The important parameters of each task are determined. Special focus is given to the compression of segmented data, parameterization of the rendering process, and DICOM-compliant implementation of the 3DPR object. The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists. The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service

PubMed Central

Bao, Shunxing; Plassard, Andrew J.; Landman, Bennett A.; Gokhale, Aniruddha

2017-01-01

Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance from these approaches is, however, impeded by standard network switches since they can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. To that end, a cloud-based “medical image processing-as-a-service” offers promise in utilizing the ecosystem of Apache Hadoop, which is a flexible framework providing distributed, scalable, fault tolerant storage and parallel computational modules, and HBase, which is a NoSQL database built atop Hadoop’s distributed file system. Despite this promise, HBase’s load distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). This paper makes two contributions to address these concerns by describing key cloud engineering principles and technology enhancements we made to the Apache Hadoop ecosystem for medical imaging applications. First, we propose a row-key design for HBase, which is a necessary step that is driven by the hierarchical organization of imaging data. Second, we propose a novel data allocation policy within HBase to strongly enforce collocation of hierarchically related imaging data. The proposed enhancements accelerate data processing by minimizing network usage and localizing processing to machines where the data already exist. Moreover, our approach is amenable to the traditional scan, subject, and project-level analysis procedures, and is compatible with standard command line/scriptable image processing software. Experimental results for an illustrative sample of imaging data reveals that our new HBase policy results in a three-fold time improvement in conversion of classic DICOM to NiFTI file formats when compared with the default HBase region split policy, and nearly a six-fold improvement over a commonly available network file system (NFS) approach even for relatively small file sets. Moreover, file access latency is lower than network attached storage. PMID:28884169

Physics of fractional imaging in biomedicine.

PubMed

Sohail, Ayesha; Bég, O A; Li, Zhiwu; Celik, Sebahattin

2018-03-12

The mathematics of imaging is a growing field of research and is evolving rapidly parallel to evolution in the field of imaging. Imaging, which is a sub-field of biomedical engineering, considers novel approaches to visualize biological tissues with the general goal of improving health. "Medical imaging research provides improved diagnostic tools in clinical settings and supports the development of drugs and other therapies. The data acquisition and diagnostic interpretation with minimum error are the important technical aspects of medical imaging. The image quality and resolution are really important in portraying the internal aspects of patient's body. Although there are several user friendly resources for processing image features, such as enhancement, colour manipulation and compression, the development of new processing methods is still worthy of efforts. In this article we aim to present the role of fractional calculus in imaging with the aid of practical examples. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Visual Communications and Image Processing

NASA Astrophysics Data System (ADS)

Hsing, T. Russell

1987-07-01

This special issue of Optical Engineering is concerned with visual communications and image processing. The increase in communication of visual information over the past several decades has resulted in many new image processing and visual communication systems being put into service. The growth of this field has been rapid in both commercial and military applications. The objective of this special issue is to intermix advent technology in visual communications and image processing with ideas generated from industry, universities, and users through both invited and contributed papers. The 15 papers of this issue are organized into four different categories: image compression and transmission, image enhancement, image analysis and pattern recognition, and image processing in medical applications.

Potential medical applications of TAE

NASA Technical Reports Server (NTRS)

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

1986-01-01

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

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.

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.

Impact of defective pixels in AMLCDs on the perception of medical images

NASA Astrophysics Data System (ADS)

Kimpe, Tom; Sneyders, Yuri

2006-03-01

With LCD displays, each pixel has its own individual transistor that controls the transmittance of that pixel. Occasionally, these individual transistors will short or alternatively malfunction, resulting in a defective pixel that always shows the same brightness. With ever increasing resolution of displays the number of defect pixels per display increases accordingly. State of the art processes are capable of producing displays with no more than one faulty transistor out of 3 million. A five Mega Pixel medical LCD panel contains 15 million individual sub pixels (3 sub pixels per pixel), each having an individual transistor. This means that a five Mega Pixel display on average will have 5 failing pixels. This paper investigates the visibility of defective pixels and analyzes the possible impact of defective pixels on the perception of medical images. JND simulations were done to study the effect of defective pixels on medical images. Our results indicate that defective LCD pixels can mask subtle features in medical images in an unexpectedly broad area around the defect and therefore may reduce the quality of diagnosis for specific high-demanding areas such as mammography. As a second contribution an innovative solution is proposed. A specialized image processing algorithm can make defective pixels completely invisible and moreover can also recover the information of the defect so that the radiologist perceives the medical image correctly. This correction algorithm has been validated with both JND simulations and psycho visual tests.

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.

On-demand server-side image processing for web-based DICOM image display

NASA Astrophysics Data System (ADS)

Sakusabe, Takaya; Kimura, Michio; Onogi, Yuzo

2000-04-01

Low cost image delivery is needed in modern networked hospitals. If a hospital has hundreds of clients, cost of client systems is a big problem. Naturally, a Web-based system is the most effective solution. But a Web browser could not display medical images with certain image processing such as a lookup table transformation. We developed a Web-based medical image display system using Web browser and on-demand server-side image processing. All images displayed on a Web page are generated from DICOM files on a server, delivered on-demand. User interaction on the Web page is handled by a client-side scripting technology such as JavaScript. This combination makes a look-and-feel of an imaging workstation not only for its functionality but also for its speed. Real time update of images with tracing mouse motion is achieved on Web browser without any client-side image processing which may be done by client-side plug-in technology such as Java Applets or ActiveX. We tested performance of the system in three cases. Single client, small number of clients in a fast speed network, and large number of clients in a normal speed network. The result shows that there are very slight overhead for communication and very scalable in number of clients.

Distributed decision making in action: diagnostic imaging investigations within the bigger picture.

PubMed

Makanjee, Chandra R; Bergh, Anne-Marie; Hoffmann, Willem A

2018-03-01

Decision making in the health care system - specifically with regard to diagnostic imaging investigations - occurs at multiple levels. Professional role players from various backgrounds are involved in making these decisions, from the point of referral to the outcomes of the imaging investigation. The aim of this study was to map the decision-making processes and pathways involved when patients are referred for diagnostic imaging investigations and to explore distributed decision-making events at the points of contact with patients within a health care system. A two-phased qualitative study was conducted in an academic public health complex with the district hospital as entry point. The first phase included case studies of 24 conveniently selected patients, and the second phase involved 12 focus group interviews with health care providers. Data analysis was based on Rapley's interpretation of decision making as being distributed across time, situations and actions, and including different role players and technologies. Clinical decisions incorporating imaging investigations are distributed across the three vital points of contact or decision-making events, namely the initial patient consultation, the diagnostic imaging investigation and the post-investigation consultation. Each of these decision-making events is made up of a sequence of discrete decision-making moments based on the transfer of retrospective, current and prospective information and its transformation into knowledge. This paper contributes to the understanding of the microstructural processes (the 'when' and 'where') involved in the distribution of decisions related to imaging investigations. It also highlights the interdependency in decision-making events of medical and non-medical providers within a single medical encounter. © 2017 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.

Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives.

PubMed

Handels, H; Ehrhardt, J

2009-01-01

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

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.

Asymmetry and irregularity border as discrimination factor between melanocytic lesions

NASA Astrophysics Data System (ADS)

Sbrissa, David; Pratavieira, Sebastião.; Salvio, Ana Gabriela; Kurachi, Cristina; Bagnato, Vanderlei Salvadori; Costa, Luciano Da Fontoura; Travieso, Gonzalo

2015-06-01

Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.

3D optical imagery for motion compensation in a limb ultrasound system

NASA Astrophysics Data System (ADS)

Ranger, Bryan J.; Feigin, Micha; Zhang, Xiang; Mireault, Al; Raskar, Ramesh; Herr, Hugh M.; Anthony, Brian W.

2016-04-01

Conventional processes for prosthetic socket fabrication are heavily subjective, often resulting in an interface to the human body that is neither comfortable nor completely functional. With nearly 100% of amputees reporting that they experience discomfort with the wearing of their prosthetic limb, designing an effective interface to the body can significantly affect quality of life and future health outcomes. Active research in medical imaging and biomechanical tissue modeling of residual limbs has led to significant advances in computer aided prosthetic socket design, demonstrating an interest in moving toward more quantifiable processes that are still patient-specific. In our work, medical ultrasonography is being pursued to acquire data that may quantify and improve the design process and fabrication of prosthetic sockets while greatly reducing cost compared to an MRI-based framework. This paper presents a prototype limb imaging system that uses a medical ultrasound probe, mounted to a mechanical positioning system and submerged in a water bath. The limb imaging is combined with three-dimensional optical imaging for motion compensation. Images are collected circumferentially around the limb and combined into cross-sectional axial image slices, resulting in a compound image that shows tissue distributions and anatomical boundaries similar to magnetic resonance imaging. In this paper we provide a progress update on our system development, along with preliminary results as we move toward full volumetric imaging of residual limbs for prosthetic socket design. This demonstrates a novel multi-modal approach to residual limb imaging.

Real-time image mosaicing for medical applications.

PubMed

Loewke, Kevin E; Camarillo, David B; Jobst, Christopher A; Salisbury, J Kenneth

2007-01-01

In this paper we describe the development of a robotically-assisted image mosaicing system for medical applications. The processing occurs in real-time due to a fast initial image alignment provided by robotic position sensing. Near-field imaging, defined by relatively large camera motion, requires translations as well as pan and tilt orientations to be measured. To capture these measurements we use 5-d.o.f. sensing along with a hand-eye calibration to account for sensor offset. This sensor-based approach speeds up the mosaicing, eliminates cumulative errors, and readily handles arbitrary camera motions. Our results have produced visually satisfactory mosaics on a dental model but can be extended to other medical images.

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.

Increasing the speed of medical image processing in MatLab®

PubMed Central

Bister, M; Yap, CS; Ng, KH; Tok, CH

2007-01-01

MatLab® has often been considered an excellent environment for fast algorithm development but is generally perceived as slow and hence not fit for routine medical image processing, where large data sets are now available e.g., high-resolution CT image sets with typically hundreds of 512x512 slices. Yet, with proper programming practices – vectorization, pre-allocation and specialization – applications in MatLab® can run as fast as in C language. In this article, this point is illustrated with fast implementations of bilinear interpolation, watershed segmentation and volume rendering. PMID:21614269

[Research applications in digital radiology. Big data and co].

PubMed

Müller, H; Hanbury, A

2016-02-01

Medical imaging produces increasingly complex images (e.g. thinner slices and higher resolution) with more protocols, so that image reading has also become much more complex. More information needs to be processed and usually the number of radiologists available for these tasks has not increased to the same extent. The objective of this article is to present current research results from projects on the use of image data for clinical decision support. An infrastructure that can allow large volumes of data to be accessed is presented. In this way the best performing tools can be identified without the medical data having to leave secure servers. The text presents the results of the VISCERAL and Khresmoi EU-funded projects, which allow the analysis of previous cases from institutional archives to support decision-making and for process automation. The results also represent a secure evaluation environment for medical image analysis. This allows the use of data extracted from past cases to solve information needs occurring when diagnosing new cases. The presented research prototypes allow direct extraction of knowledge from the visual data of the images and to use this for decision support or process automation. Real clinical use has not been tested but several subjective user tests showed the effectiveness and efficiency of the process. The future in radiology will clearly depend on better use of the important knowledge in clinical image archives to automate processes and aid decision-making via big data analysis. This can help concentrate the work of radiologists towards the most important parts of diagnostics.

[Research on non-rigid registration of multi-modal medical image based on Demons algorithm].

PubMed

Hao, Peibo; Chen, Zhen; Jiang, Shaofeng; Wang, Yang

2014-02-01

Non-rigid medical image registration is a popular subject in the research areas of the medical image and has an important clinical value. In this paper we put forward an improved algorithm of Demons, together with the conservation of gray model and local structure tensor conservation model, to construct a new energy function processing multi-modal registration problem. We then applied the L-BFGS algorithm to optimize the energy function and solve complex three-dimensional data optimization problem. And finally we used the multi-scale hierarchical refinement ideas to solve large deformation registration. The experimental results showed that the proposed algorithm for large de formation and multi-modal three-dimensional medical image registration had good effects.

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.

[Imaging center - optimization of the imaging process].

PubMed

Busch, H-P

2013-04-01

Hospitals around the world are under increasing pressure to optimize the economic efficiency of treatment processes. Imaging is responsible for a great part of the success but also of the costs of treatment. In routine work an excessive supply of imaging methods leads to an "as well as" strategy up to the limit of the capacity without critical reflection. Exams that have no predictable influence on the clinical outcome are an unjustified burden for the patient. They are useless and threaten the financial situation and existence of the hospital. In recent years the focus of process optimization was exclusively on the quality and efficiency of performed single examinations. In the future critical discussion of the effectiveness of single exams in relation to the clinical outcome will be more important. Unnecessary exams can be avoided, only if in addition to the optimization of single exams (efficiency) there is an optimization strategy for the total imaging process (efficiency and effectiveness). This requires a new definition of processes (Imaging Pathway), new structures for organization (Imaging Center) and a new kind of thinking on the part of the medical staff. Motivation has to be changed from gratification of performed exams to gratification of process quality (medical quality, service quality, economics), including the avoidance of additional (unnecessary) exams. © Georg Thieme Verlag KG Stuttgart · New York.

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.

Image processing and machine learning for fully automated probabilistic evaluation of medical images.

PubMed

Sajn, Luka; Kukar, Matjaž

2011-12-01

The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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.

Using normalization 3D model for automatic clinical brain quantative analysis and evaluation

NASA Astrophysics Data System (ADS)

Lin, Hong-Dun; Yao, Wei-Jen; Hwang, Wen-Ju; Chung, Being-Tau; Lin, Kang-Ping

2003-05-01

Functional medical imaging, such as PET or SPECT, is capable of revealing physiological functions of the brain, and has been broadly used in diagnosing brain disorders by clinically quantitative analysis for many years. In routine procedures, physicians manually select desired ROIs from structural MR images and then obtain physiological information from correspondent functional PET or SPECT images. The accuracy of quantitative analysis thus relies on that of the subjectively selected ROIs. Therefore, standardizing the analysis procedure is fundamental and important in improving the analysis outcome. In this paper, we propose and evaluate a normalization procedure with a standard 3D-brain model to achieve precise quantitative analysis. In the normalization process, the mutual information registration technique was applied for realigning functional medical images to standard structural medical images. Then, the standard 3D-brain model that shows well-defined brain regions was used, replacing the manual ROIs in the objective clinical analysis. To validate the performance, twenty cases of I-123 IBZM SPECT images were used in practical clinical evaluation. The results show that the quantitative analysis outcomes obtained from this automated method are in agreement with the clinical diagnosis evaluation score with less than 3% error in average. To sum up, the method takes advantage of obtaining precise VOIs, information automatically by well-defined standard 3-D brain model, sparing manually drawn ROIs slice by slice from structural medical images in traditional procedure. That is, the method not only can provide precise analysis results, but also improve the process rate for mass medical images in clinical.

Aerospace Technology Innovation. Volume 10

NASA Technical Reports Server (NTRS)

Turner, Janelle (Editor); Cousins, Liz (Editor); Bennett, Evonne (Editor); Vendette, Joel (Editor); West, Kenyon (Editor)

2002-01-01

Whether finding new applications for existing NASA technologies or developing unique marketing strategies to demonstrate them, NASA's offices are committed to identifying unique partnering opportunities. Through their efforts NASA leverages resources through joint research and development, and gains new insight into the core areas relevant to all NASA field centers. One of the most satisfying aspects of my job comes when I learn of a mission-driven technology that can be spun-off to touch the lives of everyday people. NASA's New Partnerships in Medical Diagnostic Imaging is one such initiative. Not only does it promise to provide greater dividends for the country's investment in aerospace research, but also to enhance the American quality of life. This issue of Innovation highlights the new NASA-sponsored initiative in medical imaging. Early in 2001, NASA announced the launch of the New Partnerships in Medical Diagnostic Imaging initiative to promote the partnership and commercialization of NASA technologies in the medical imaging industry. NASA and the medical imaging industry share a number of crosscutting technologies in areas such as high-performance detectors and image-processing tools. Many of the opportunities for joint development and technology transfer to the medical imaging market also hold the promise for future spin back to NASA.

Capacitive micromachined ultrasonic transducers for medical imaging and therapy.

PubMed

Khuri-Yakub, Butrus T; Oralkan, Omer

2011-05-01

Capacitive micromachined ultrasonic transducers (CMUTs) have been subject to extensive research for the last two decades. Although they were initially developed for air-coupled applications, today their main application space is medical imaging and therapy. This paper first presents a brief description of CMUTs, their basic structure, and operating principles. Our progression of developing several generations of fabrication processes is discussed with an emphasis on the advantages and disadvantages of each process. Monolithic and hybrid approaches for integrating CMUTs with supporting integrated circuits are surveyed. Several prototype transducer arrays with integrated frontend electronic circuits we developed and their use for 2-D and 3-D, anatomical and functional imaging, and ablative therapies are described. The presented results prove the CMUT as a MEMS technology for many medical diagnostic and therapeutic applications.

Capacitive micromachined ultrasonic transducers for medical imaging and therapy

PubMed Central

Khuri-Yakub, Butrus T.; Oralkan, Ömer

2011-01-01

Capacitive micromachined ultrasonic transducers (CMUTs) have been subject to extensive research for the last two decades. Although they were initially developed for air-coupled applications, today their main application space is medical imaging and therapy. This paper first presents a brief description of CMUTs, their basic structure, and operating principles. Our progression of developing several generations of fabrication processes is discussed with an emphasis on the advantages and disadvantages of each process. Monolithic and hybrid approaches for integrating CMUTs with supporting integrated circuits are surveyed. Several prototype transducer arrays with integrated frontend electronic circuits we developed and their use for 2-D and 3-D, anatomical and functional imaging, and ablative therapies are described. The presented results prove the CMUT as a MEMS technology for many medical diagnostic and therapeutic applications. PMID:21860542

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.

Multidimensional Processing and Visual Rendering of Complex 3D Biomedical Images

NASA Technical Reports Server (NTRS)

Sams, Clarence F.

2016-01-01

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

Speckle reduction in echocardiography by temporal compounding and anisotropic diffusion filtering

NASA Astrophysics Data System (ADS)

Giraldo-Guzmán, Jader; Porto-Solano, Oscar; Cadena-Bonfanti, Alberto; Contreras-Ortiz, Sonia H.

2015-01-01

Echocardiography is a medical imaging technique based on ultrasound signals that is used to evaluate heart anatomy and physiology. Echocardiographic images are affected by speckle, a type of multiplicative noise that obscures details of the structures, and reduces the overall image quality. This paper shows an approach to enhance echocardiography using two processing techniques: temporal compounding and anisotropic diffusion filtering. We used twenty echocardiographic videos that include one or three cardiac cycles to test the algorithms. Two images from each cycle were aligned in space and averaged to obtain the compound images. These images were then processed using anisotropic diffusion filters to further improve their quality. Resultant images were evaluated using quality metrics and visual assessment by two medical doctors. The average total improvement on signal-to-noise ratio was up to 100.29% for videos with three cycles, and up to 32.57% for videos with one cycle.

Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images

PubMed Central

Levenson, Richard M.; Krupinski, Elizabeth A.; Navarro, Victor M.; Wasserman, Edward A.

2015-01-01

Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools. PMID:26581091

Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images.

PubMed

Levenson, Richard M; Krupinski, Elizabeth A; Navarro, Victor M; Wasserman, Edward A

2015-01-01

Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)-which share many visual system properties with humans-can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds' histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task-namely, classification of suspicious mammographic densities (masses)-the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds' successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.

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

Constrained Adaptive Beamforming for Improved Contrast in Breast Ultrasound

DTIC Science & Technology

2008-06-01

Medical Imaging 2007: Ultrasonic Imaging and Signal Processing Proceedings, vol. 6513, San Diego , CA, Feb. 18, 2007. 12. Guenther, D.A., Walker...Transactions on, vol. 6, pp. 185-192, 1987. [23] A. P. Schachat, R. P. Murphy, and A. Patz, Medical Retina, vol. 2, 1 ed. St. Louis: The C. V. Mosby

A low noise stenography method for medical images with QR encoding of patient information

NASA Astrophysics Data System (ADS)

Patiño-Vanegas, Alberto; Contreras-Ortiz, Sonia H.; Martinez-Santos, Juan C.

2017-03-01

This paper proposes an approach to facilitate the process of individualization of patients from their medical images, without compromising the inherent confidentiality of medical data. The identification of a patient from a medical image is not often the goal of security methods applied to image records. Usually, any identification data is removed from shared records, and security features are applied to determine ownership. We propose a method for embedding a QR-code containing information that can be used to individualize a patient. This is done so that the image to be shared does not differ significantly from the original image. The QR-code is distributed in the image by changing several pixels according to a threshold value based on the average value of adjacent pixels surrounding the point of interest. The results show that the code can be embedded and later fully recovered with minimal changes in the UIQI index - less than 0.1% of different.

Medical Image Encryption: An Application for Improved Padding Based GGH Encryption Algorithm

PubMed Central

Sokouti, Massoud; Zakerolhosseini, Ali; Sokouti, Babak

2016-01-01

Medical images are regarded as important and sensitive data in the medical informatics systems. For transferring medical images over an insecure network, developing a secure encryption algorithm is necessary. Among the three main properties of security services (i.e., confidentiality, integrity, and availability), the confidentiality is the most essential feature for exchanging medical images among physicians. The Goldreich Goldwasser Halevi (GGH) algorithm can be a good choice for encrypting medical images as both the algorithm and sensitive data are represented by numeric matrices. Additionally, the GGH algorithm does not increase the size of the image and hence, its complexity will remain as simple as O(n2). However, one of the disadvantages of using the GGH algorithm is the Chosen Cipher Text attack. In our strategy, this shortcoming of GGH algorithm has been taken in to consideration and has been improved by applying the padding (i.e., snail tour XORing), before the GGH encryption process. For evaluating their performances, three measurement criteria are considered including (i) Number of Pixels Change Rate (NPCR), (ii) Unified Average Changing Intensity (UACI), and (iii) Avalanche effect. The results on three different sizes of images showed that padding GGH approach has improved UACI, NPCR, and Avalanche by almost 100%, 35%, and 45%, respectively, in comparison to the standard GGH algorithm. Also, the outcomes will make the padding GGH resist against the cipher text, the chosen cipher text, and the statistical attacks. Furthermore, increasing the avalanche effect of more than 50% is a promising achievement in comparison to the increased complexities of the proposed method in terms of encryption and decryption processes. PMID:27857824

User Oriented Platform for Data Analytics in Medical Imaging Repositories.

PubMed

Valerio, Miguel; Godinho, Tiago Marques; Costa, Carlos

2016-01-01

The production of medical imaging studies and associated data has been growing in the last decades. Their primary use is to support medical diagnosis and treatment processes. However, the secondary use of the tremendous amount of stored data is generally more limited. Nowadays, medical imaging repositories have turned into rich databanks holding not only the images themselves, but also a wide range of metadata related to the medical practice. Exploring these repositories through data analysis and business intelligence techniques has the potential of increasing the efficiency and quality of the medical practice. Nevertheless, the continuous production of tremendous amounts of data makes their analysis difficult by conventional approaches. This article proposes a novel automated methodology to derive knowledge from medical imaging repositories that does not disrupt the regular medical practice. Our method is able to apply statistical analysis and business intelligence techniques directly on top of live institutional repositories. It is a Web-based solution that provides extensive dashboard capabilities, including complete charting and reporting options, combined with data mining components. Moreover, it enables the operator to set a wide multitude of query parameters and operators through the use of an intuitive graphical interface.

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.

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.

Flexible medical image management using service-oriented architecture.

PubMed

Shaham, Oded; Melament, Alex; Barak-Corren, Yuval; Kostirev, Igor; Shmueli, Noam; Peres, Yardena

2012-01-01

Management of medical images increasingly involves the need for integration with a variety of information systems. To address this need, we developed Content Management Offering (CMO), a platform for medical image management supporting interoperability through compliance with standards. CMO is based on the principles of service-oriented architecture, implemented with emphasis on three areas: clarity of business process definition, consolidation of service configuration management, and system scalability. Owing to the flexibility of this platform, a small team is able to accommodate requirements of customers varying in scale and in business needs. We describe two deployments of CMO, highlighting the platform's value to customers. CMO represents a flexible approach to medical image management, which can be applied to a variety of information technology challenges in healthcare and life sciences organizations.

Visual servoing in medical robotics: a survey. Part I: endoscopic and direct vision imaging - techniques and applications.

PubMed

Azizian, Mahdi; Khoshnam, Mahta; Najmaei, Nima; Patel, Rajni V

2014-09-01

Intra-operative imaging is widely used to provide visual feedback to a clinician when he/she performs a procedure. In visual servoing, surgical instruments and parts of tissue/body are tracked by processing the acquired images. This information is then used within a control loop to manoeuvre a robotic manipulator during a procedure. A comprehensive search of electronic databases was completed for the period 2000-2013 to provide a survey of the visual servoing applications in medical robotics. The focus is on medical applications where image-based tracking is used for closed-loop control of a robotic system. Detailed classification and comparative study of various contributions in visual servoing using endoscopic or direct visual images are presented and summarized in tables and diagrams. The main challenges in using visual servoing for medical robotic applications are identified and potential future directions are suggested. 'Supervised automation of medical robotics' is found to be a major trend in this field. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Role of Sonographic Imaging in Occupational Therapy Practice

PubMed Central

2015-01-01

Occupational therapy practice is grounded in the delivery of occupation-centered, patient-driven treatments that engage clients in the process of doing to improve health. As emerging technologies, such as medical imaging, find their way into rehabilitation practice, it is imperative that occupational therapy practitioners assess whether and how these tools can be incorporated into treatment regimens that are dually responsive to the medical model of health care and to the profession’s foundation in occupation. Most medical imaging modalities have a discrete place in occupation-based intervention as outcome measures or for patient education; however, sonographic imaging has the potential to blend multiple occupational therapy practice forms to document treatment outcomes, inform clinical reasoning, and facilitate improved functional performance when used as an accessory tool in direct intervention. Use of medical imaging is discussed as it relates to occupational foundations and the professional role within the context of providing efficient, effective patient-centered rehabilitative care. PMID:25871607

Radiomics in radiooncology - Challenging the medical physicist.

PubMed

Peeken, Jan C; Bernhofer, Michael; Wiestler, Benedikt; Goldberg, Tatyana; Cremers, Daniel; Rost, Burkhard; Wilkens, Jan J; Combs, Stephanie E; Nüsslin, Fridtjof

2018-04-01

Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team. The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

TRENCADIS--a WSRF grid MiddleWare for managing DICOM structured reporting objects.

PubMed

Blanquer, Ignacio; Hernandez, Vicente; Segrelles, Damià

2006-01-01

The adoption of the digital processing of medical data, especially on radiology, has leaded to the availability of millions of records (images and reports). However, this information is mainly used at patient level, being the extraction of information, organised according to administrative criteria, which make the extraction of knowledge difficult. Moreover, legal constraints make the direct integration of information systems complex or even impossible. On the other side, the widespread of the DICOM format has leaded to the inclusion of other information different from just radiological images. The possibility of coding radiology reports in a structured form, adding semantic information about the data contained in the DICOM objects, eases the process of structuring images according to content. DICOM Structured Reporting (DICOM-SR) is a specification of tags and sections to code and integrate radiology reports, with seamless references to findings and regions of interests of the associated images, movies, waveforms, signals, etc. The work presented in this paper aims at developing of a framework to efficiently and securely share medical images and radiology reports, as well as to provide high throughput processing services. This system is based on a previously developed architecture in the framework of the TRENCADIS project, and uses other components such as the security system and the Grid processing service developed in previous activities. The work presented here introduces a semantic structuring and an ontology framework, to organise medical images considering standard terminology and disease coding formats (SNOMED, ICD9, LOINC..).

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

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.

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.

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.

Object-oriented design of medical imaging software.

PubMed

Ligier, Y; Ratib, O; Logean, M; Girard, C; Perrier, R; Scherrer, J R

1994-01-01

A special software package for interactive display and manipulation of medical images was developed at the University Hospital of Geneva, as part of a hospital wide Picture Archiving and Communication System (PACS). This software package, called Osiris, was especially designed to be easily usable and adaptable to the needs of noncomputer-oriented physicians. The Osiris software has been developed to allow the visualization of medical images obtained from any imaging modality. It provides generic manipulation tools, processing tools, and analysis tools more specific to clinical applications. This software, based on an object-oriented paradigm, is portable and extensible. Osiris is available on two different operating systems: the Unix X-11/OSF-Motif based workstations, and the Macintosh family.

Achieving quality in cardiovascular imaging: proceedings from the American College of Cardiology-Duke University Medical Center Think Tank on Quality in Cardiovascular Imaging.

PubMed

Douglas, Pamela; Iskandrian, Ami E; Krumholz, Harlan M; Gillam, Linda; Hendel, Robert; Jollis, James; Peterson, Eric; Chen, Jersey; Masoudi, Frederick; Mohler, Emile; McNamara, Robert L; Patel, Manesh R; Spertus, John

2006-11-21

Cardiovascular imaging has enjoyed both rapid technological advances and sustained growth, yet less attention has been focused on quality than in other areas of cardiovascular medicine. To address this deficit, representatives from cardiovascular imaging societies, private payers, government agencies, the medical imaging industry, and experts in quality measurement met, and this report provides an overview of the discussions. A consensus definition of quality in imaging and a convergence of opinion on quality measures across imaging modalities was achieved and are intended to be the start of a process culminating in the development, dissemination, and adoption of quality measures for all cardiovascular imaging modalities.

Java-based browsing, visualization and processing of heterogeneous medical data from remote repositories.

PubMed

Masseroli, M; Bonacina, S; Pinciroli, F

2004-01-01

The actual development of distributed information technologies and Java programming enables employing them also in the medical arena to support the retrieval, integration and evaluation of heterogeneous data and multimodal images in a web browser environment. With this aim, we used them to implement a client-server architecture based on software agents. The client side is a Java applet running in a web browser and providing a friendly medical user interface to browse and visualize different patient and medical test data, integrating them properly. The server side manages secure connections and queries to heterogeneous remote databases and file systems containing patient personal and clinical data. Based on the Java Advanced Imaging API, processing and analysis tools were developed to support the evaluation of remotely retrieved bioimages through the quantification of their features in different regions of interest. The Java platform-independence allows the centralized management of the implemented prototype and its deployment to each site where an intranet or internet connection is available. Giving healthcare providers effective support for comprehensively browsing, visualizing and evaluating medical images and records located in different remote repositories, the developed prototype can represent an important aid in providing more efficient diagnoses and medical treatments.

Deformable Medical Image Registration: A Survey

PubMed Central

Sotiras, Aristeidis; Davatzikos, Christos; Paragios, Nikos

2013-01-01

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner. PMID:23739795

A generic framework for internet-based interactive applications of high-resolution 3-D medical image data.

PubMed

Liu, Danzhou; Hua, Kien A; Sugaya, Kiminobu

2008-09-01

With the advances in medical imaging devices, large volumes of high-resolution 3-D medical image data have been produced. These high-resolution 3-D data are very large in size, and severely stress storage systems and networks. Most existing Internet-based 3-D medical image interactive applications therefore deal with only low- or medium-resolution image data. While it is possible to download the whole 3-D high-resolution image data from the server and perform the image visualization and analysis at the client site, such an alternative is infeasible when the high-resolution data are very large, and many users concurrently access the server. In this paper, we propose a novel framework for Internet-based interactive applications of high-resolution 3-D medical image data. Specifically, we first partition the whole 3-D data into buckets, remove the duplicate buckets, and then, compress each bucket separately. We also propose an index structure for these buckets to efficiently support typical queries such as 3-D slicer and region of interest, and only the relevant buckets are transmitted instead of the whole high-resolution 3-D medical image data. Furthermore, in order to better support concurrent accesses and to improve the average response time, we also propose techniques for efficient query processing, incremental transmission, and client sharing. Our experimental study in simulated and realistic environments indicates that the proposed framework can significantly reduce storage and communication requirements, and can enable real-time interaction with remote high-resolution 3-D medical image data for many concurrent users.

[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.

Automated collection of medical images for research from heterogeneous systems: trials and tribulations

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P.; Young, K.; Halling-Brown, M. D.

2014-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. Over the past two decades both diagnostic and therapeutic imaging have undergone a rapid growth, the ability to be able to harness this large influx of medical images can provide an essential resource for research and training. Traditionally, the systematic collection of medical images for research from heterogeneous sites has not been commonplace within the NHS and is fraught with challenges including; data acquisition, storage, secure transfer and correct anonymisation. Here, we describe a semi-automated system, which comprehensively oversees the collection of both unprocessed and processed medical images from acquisition to a centralised database. The provision of unprocessed images within our repository enables a multitude of potential research possibilities that utilise the images. Furthermore, we have developed systems and software to integrate these data with their associated clinical data and annotations providing a centralised dataset for research. Currently we regularly collect digital mammography images from two sites and partially collect from a further three, with efforts to expand into other modalities and sites currently ongoing. At present we have collected 34,014 2D images from 2623 individuals. In this paper we describe our medical image collection system for research and discuss the wide spectrum of challenges faced during the design and implementation of such systems.

Evaluation of security algorithms used for security processing on DICOM images

NASA Astrophysics Data System (ADS)

Chen, Xiaomeng; Shuai, Jie; Zhang, Jianguo; Huang, H. K.

2005-04-01

In this paper, we developed security approach to provide security measures and features in PACS image acquisition and Tele-radiology image transmission. The security processing on medical images was based on public key infrastructure (PKI) and including digital signature and data encryption to achieve the security features of confidentiality, privacy, authenticity, integrity, and non-repudiation. There are many algorithms which can be used in PKI for data encryption and digital signature. In this research, we select several algorithms to perform security processing on different DICOM images in PACS environment, evaluate the security processing performance of these algorithms, and find the relationship between performance with image types, sizes and the implementation methods.

Towards real-time medical diagnostics using hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Bjorgan, Asgeir; Randeberg, Lise L.

2015-07-01

Hyperspectral imaging provides non-contact, high resolution spectral images which has a substantial diagnostic potential. This can be used for e.g. diagnosis and early detection of arthritis in finger joints. Processing speed is currently a limitation for clinical use of the technique. A real-time system for analysis and visualization using GPU processing and threaded CPU processing is presented. Images showing blood oxygenation, blood volume fraction and vessel enhanced images are among the data calculated in real-time. This study shows the potential of real-time processing in this context. A combination of the processing modules will be used in detection of arthritic finger joints from hyperspectral reflectance and transmittance data.

An evaluation on CT image acquisition method for medical VR applications

NASA Astrophysics Data System (ADS)

Jang, Seong-wook; Ko, Junho; Yoo, Yon-sik; Kim, Yoonsang

2017-02-01

Recent medical virtual reality (VR) applications to minimize re-operations are being studied for improvements in surgical efficiency and reduction of operation error. The CT image acquisition method considering three-dimensional (3D) modeling for medical VR applications is important, because the realistic model is required for the actual human organ. However, the research for medical VR applications has focused on 3D modeling techniques and utilized 3D models. In addition, research on a CT image acquisition method considering 3D modeling has never been reported. The conventional CT image acquisition method involves scanning a limited area of the lesion for the diagnosis of doctors once or twice. However, the medical VR application is required to acquire the CT image considering patients' various postures and a wider area than the lesion. A wider area than the lesion is required because of the necessary process of comparing bilateral sides for dyskinesia diagnosis of the shoulder, pelvis, and leg. Moreover, patients' various postures are required due to the different effects on the musculoskeletal system. Therefore, in this paper, we perform a comparative experiment on the acquired CT images considering image area (unilateral/bilateral) and patients' postures (neutral/abducted). CT images are acquired from 10 patients for the experiments, and the acquired CT images are evaluated based on the length per pixel and the morphological deviation. Finally, by comparing the experiment results, we evaluate the CT image acquisition method for medical VR applications.

21 CFR 892.2050 - Picture archiving and communications system.

Code of Federal Regulations, 2014 CFR

2014-04-01

... processing of medical images. Its hardware components may include workstations, digitizers, communications... hardcopy devices. The software components may provide functions for performing operations related to image...

21 CFR 892.2050 - Picture archiving and communications system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... processing of medical images. Its hardware components may include workstations, digitizers, communications... hardcopy devices. The software components may provide functions for performing operations related to image...

21 CFR 892.2050 - Picture archiving and communications system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... processing of medical images. Its hardware components may include workstations, digitizers, communications... hardcopy devices. The software components may provide functions for performing operations related to image...

21 CFR 892.2050 - Picture archiving and communications system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... processing of medical images. Its hardware components may include workstations, digitizers, communications... hardcopy devices. The software components may provide functions for performing operations related to image...

Medication Overuse Headache: Pathophysiological Insights from Structural and Functional Brain MRI Research.

PubMed

Schwedt, Todd J; Chong, Catherine D

2017-07-01

Research imaging of brain structure and function has helped to elucidate the pathophysiology of medication overuse headache (MOH). This is a narrative review of imaging research studies that have investigated brain structural and functional alterations associated with MOH. Studies included in this review have investigated abnormal structure and function of pain processing regions in people with MOH, functional patterns that might predispose individuals to development of MOH, similarity of brain functional patterns in patients with MOH to those found in people with addiction, brain structure that could predict headache improvement following discontinuation of the overused medication, and changes in brain structure and function after discontinuation of medication overuse. MOH is associated with atypical structure and function of brain regions responsible for pain processing as well as brain regions that are commonly implicated in addiction. Several studies have shown "normalization" of structure and function in pain processing regions following discontinuation of the overused medication and resolution of MOH. However, some of the abnormalities in regions also implicated in addiction tend to persist following discontinuation of the overused medication, suggesting that they are a brain trait that predisposes certain individuals to medication overuse and MOH. © 2017 American Headache Society.

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.

Cnn Based Retinal Image Upscaling Using Zero Component Analysis

NASA Astrophysics Data System (ADS)

Nasonov, A.; Chesnakov, K.; Krylov, A.

2017-05-01

The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless highresolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images. The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment, so the proposed algorithm is recommended to be used in real medical applications.

New method for identifying features of an image on a digital video display

NASA Astrophysics Data System (ADS)

Doyle, Michael D.

1991-04-01

The MetaMap process extends the concept of direct manipulation human-computer interfaces to new limits. Its specific capabilities include the correlation of discrete image elements to relevant text information and the correlation of these image features to other images as well as to program control mechanisms. The correlation is accomplished through reprogramming of both the color map and the image so that discrete image elements comprise unique sets of color indices. This process allows the correlation to be accomplished with very efficient data storage and program execution times. Image databases adapted to this process become object-oriented as a result. Very sophisticated interrelationships can be set up between images text and program control mechanisms using this process. An application of this interfacing process to the design of an interactive atlas of medical histology as well as other possible applications are described. The MetaMap process is protected by U. S. patent #4

Managing complex processing of medical image sequences by program supervision techniques

NASA Astrophysics Data System (ADS)

Crubezy, Monica; Aubry, Florent; Moisan, Sabine; Chameroy, Virginie; Thonnat, Monique; Di Paola, Robert

1997-05-01

Our objective is to offer clinicians wider access to evolving medical image processing (MIP) techniques, crucial to improve assessment and quantification of physiological processes, but difficult to handle for non-specialists in MIP. Based on artificial intelligence techniques, our approach consists in the development of a knowledge-based program supervision system, automating the management of MIP libraries. It comprises a library of programs, a knowledge base capturing the expertise about programs and data and a supervision engine. It selects, organizes and executes the appropriate MIP programs given a goal to achieve and a data set, with dynamic feedback based on the results obtained. It also advises users in the development of new procedures chaining MIP programs.. We have experimented the approach for an application of factor analysis of medical image sequences as a means of predicting the response of osteosarcoma to chemotherapy, with both MRI and NM dynamic image sequences. As a result our program supervision system frees clinical end-users from performing tasks outside their competence, permitting them to concentrate on clinical issues. Therefore our approach enables a better exploitation of possibilities offered by MIP and higher quality results, both in terms of robustness and reliability.

Telemedicine optoelectronic biomedical data processing system

NASA Astrophysics Data System (ADS)

Prosolovska, Vita V.

2010-08-01

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

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

NASA Astrophysics Data System (ADS)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Joint sparse coding based spatial pyramid matching for classification of color medical image.

PubMed

Shi, Jun; Li, Yi; Zhu, Jie; Sun, Haojie; Cai, Yin

2015-04-01

Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid matching (ScSPM) and its variants are popular for grayscale image classification, but cannot extract color information. In this paper, we propose a joint sparse coding based SPM (JScSPM) method for the classification of color medical images. A joint dictionary can represent both the color information in each color channel and the correlation between channels. Consequently, the joint sparse codes calculated from a joint dictionary can carry color information, and therefore this method can easily transform a feature descriptor originally designed for grayscale images to a color descriptor. A color hepatocellular carcinoma histological image dataset was used to evaluate the performance of the proposed JScSPM algorithm. Experimental results show that JScSPM provides significant improvements as compared with the majority voting based ScSPM and the original ScSPM for color medical image classification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Smart cloud system with image processing server in diagnosing brain diseases dedicated for hospitals with limited resources.

PubMed

Fahmi, Fahmi; Nasution, Tigor H; Anggreiny, Anggreiny

2017-01-01

The use of medical imaging in diagnosing brain disease is growing. The challenges are related to the big size of data and complexity of the image processing. High standard of hardware and software are demanded, which can only be provided in big hospitals. Our purpose was to provide a smart cloud system to help diagnosing brain diseases for hospital with limited infrastructure. The expertise of neurologists was first implanted in cloud server to conduct an automatic diagnosis in real time using image processing technique developed based on ITK library and web service. Users upload images through website and the result, in this case the size of tumor was sent back immediately. A specific image compression technique was developed for this purpose. The smart cloud system was able to measure the area and location of tumors, with average size of 19.91 ± 2.38 cm2 and an average response time 7.0 ± 0.3 s. The capability of the server decreased when multiple clients accessed the system simultaneously: 14 ± 0 s (5 parallel clients) and 27 ± 0.2 s (10 parallel clients). The cloud system was successfully developed to process and analyze medical images for diagnosing brain diseases in this case for tumor.

Interpretation of medical imaging data with a mobile application: a mobile digital imaging processing environment.

PubMed

Lin, Meng Kuan; Nicolini, Oliver; Waxenegger, Harald; Galloway, Graham J; Ullmann, Jeremy F P; Janke, Andrew L

2013-01-01

Digital Imaging Processing (DIP) requires data extraction and output from a visualization tool to be consistent. Data handling and transmission between the server and a user is a systematic process in service interpretation. The use of integrated medical services for management and viewing of imaging data in combination with a mobile visualization tool can be greatly facilitated by data analysis and interpretation. This paper presents an integrated mobile application and DIP service, called M-DIP. The objective of the system is to (1) automate the direct data tiling, conversion, pre-tiling of brain images from Medical Imaging NetCDF (MINC), Neuroimaging Informatics Technology Initiative (NIFTI) to RAW formats; (2) speed up querying of imaging measurement; and (3) display high-level of images with three dimensions in real world coordinates. In addition, M-DIP provides the ability to work on a mobile or tablet device without any software installation using web-based protocols. M-DIP implements three levels of architecture with a relational middle-layer database, a stand-alone DIP server, and a mobile application logic middle level realizing user interpretation for direct querying and communication. This imaging software has the ability to display biological imaging data at multiple zoom levels and to increase its quality to meet users' expectations. Interpretation of bioimaging data is facilitated by an interface analogous to online mapping services using real world coordinate browsing. This allows mobile devices to display multiple datasets simultaneously from a remote site. M-DIP can be used as a measurement repository that can be accessed by any network environment, such as a portable mobile or tablet device. In addition, this system and combination with mobile applications are establishing a virtualization tool in the neuroinformatics field to speed interpretation services.

Interpretation of Medical Imaging Data with a Mobile Application: A Mobile Digital Imaging Processing Environment

PubMed Central

Lin, Meng Kuan; Nicolini, Oliver; Waxenegger, Harald; Galloway, Graham J.; Ullmann, Jeremy F. P.; Janke, Andrew L.

2013-01-01

Digital Imaging Processing (DIP) requires data extraction and output from a visualization tool to be consistent. Data handling and transmission between the server and a user is a systematic process in service interpretation. The use of integrated medical services for management and viewing of imaging data in combination with a mobile visualization tool can be greatly facilitated by data analysis and interpretation. This paper presents an integrated mobile application and DIP service, called M-DIP. The objective of the system is to (1) automate the direct data tiling, conversion, pre-tiling of brain images from Medical Imaging NetCDF (MINC), Neuroimaging Informatics Technology Initiative (NIFTI) to RAW formats; (2) speed up querying of imaging measurement; and (3) display high-level of images with three dimensions in real world coordinates. In addition, M-DIP provides the ability to work on a mobile or tablet device without any software installation using web-based protocols. M-DIP implements three levels of architecture with a relational middle-layer database, a stand-alone DIP server, and a mobile application logic middle level realizing user interpretation for direct querying and communication. This imaging software has the ability to display biological imaging data at multiple zoom levels and to increase its quality to meet users’ expectations. Interpretation of bioimaging data is facilitated by an interface analogous to online mapping services using real world coordinate browsing. This allows mobile devices to display multiple datasets simultaneously from a remote site. M-DIP can be used as a measurement repository that can be accessed by any network environment, such as a portable mobile or tablet device. In addition, this system and combination with mobile applications are establishing a virtualization tool in the neuroinformatics field to speed interpretation services. PMID:23847587

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.

[Medical image elastic registration smoothed by unconstrained optimized thin-plate spline].

PubMed

Zhang, Yu; Li, Shuxiang; Chen, Wufan; Liu, Zhexing

2003-12-01

Elastic registration of medical image is an important subject in medical image processing. Previous work has concentrated on selecting the corresponding landmarks manually and then using thin-plate spline interpolating to gain the elastic transformation. However, the landmarks extraction is always prone to error, which will influence the registration results. Localizing the landmarks manually is also difficult and time-consuming. We the optimization theory to improve the thin-plate spline interpolation, and based on it, used an automatic method to extract the landmarks. Combining these two steps, we have proposed an automatic, exact and robust registration method and have gained satisfactory registration results.

Small blob identification in medical images using regional features from optimum scale.

PubMed

Zhang, Min; Wu, Teresa; Bennett, Kevin M

2015-04-01

Recent advances in medical imaging technology have greatly enhanced imaging-based diagnosis which requires computational effective and accurate algorithms to process the images (e.g., measure the objects) for quantitative assessment. In this research, we are interested in one type of imaging objects: small blobs. Examples of small blob objects are cells in histopathology images, glomeruli in MR images, etc. This problem is particularly challenging because the small blobs often have in homogeneous intensity distribution and an indistinct boundary against the background. Yet, in general, these blobs have similar sizes. Motivated by this finding, we propose a novel detector termed Hessian-based Laplacian of Gaussian (HLoG) using scale space theory as the foundation. Like most imaging detectors, an image is first smoothed via LoG. Hessian analysis is then launched to identify the single optimal scale on which a presegmentation is conducted. The advantage of the Hessian process is that it is capable of delineating the blobs. As a result, regional features can be retrieved. These features enable an unsupervised clustering algorithm for postpruning which should be more robust and sensitive than the traditional threshold-based postpruning commonly used in most imaging detectors. To test the performance of the proposed HLoG, two sets of 2-D grey medical images are studied. HLoG is compared against three state-of-the-art detectors: generalized LoG, Radial-Symmetry and LoG using precision, recall, and F-score metrics.We observe that HLoG statistically outperforms the compared detectors.

Imperceptible watermarking for security of fundus images in tele-ophthalmology applications and computer-aided diagnosis of retina diseases.

PubMed

Singh, Anushikha; Dutta, Malay Kishore

2017-12-01

The authentication and integrity verification of medical images is a critical and growing issue for patients in e-health services. Accurate identification of medical images and patient verification is an essential requirement to prevent error in medical diagnosis. The proposed work presents an imperceptible watermarking system to address the security issue of medical fundus images for tele-ophthalmology applications and computer aided automated diagnosis of retinal diseases. In the proposed work, patient identity is embedded in fundus image in singular value decomposition domain with adaptive quantization parameter to maintain perceptual transparency for variety of fundus images like healthy fundus or disease affected image. In the proposed method insertion of watermark in fundus image does not affect the automatic image processing diagnosis of retinal objects & pathologies which ensure uncompromised computer-based diagnosis associated with fundus image. Patient ID is correctly recovered from watermarked fundus image for integrity verification of fundus image at the diagnosis centre. The proposed watermarking system is tested in a comprehensive database of fundus images and results are convincing. results indicate that proposed watermarking method is imperceptible and it does not affect computer vision based automated diagnosis of retinal diseases. Correct recovery of patient ID from watermarked fundus image makes the proposed watermarking system applicable for authentication of fundus images for computer aided diagnosis and Tele-ophthalmology applications. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Myronakis, M; Cai, W; Dhou, S

Purpose: To design a comprehensive open-source, publicly available, graphical user interface (GUI) to facilitate the configuration, generation, processing and use of the 4D Extended Cardiac-Torso (XCAT) phantom. Methods: The XCAT phantom includes over 9000 anatomical objects as well as respiratory, cardiac and tumor motion. It is widely used for research studies in medical imaging and radiotherapy. The phantom generation process involves the configuration of a text script to parameterize the geometry, motion, and composition of the whole body and objects within it, and to generate simulated PET or CT images. To avoid the need for manual editing or script writing,more » our MATLAB-based GUI uses slider controls, drop-down lists, buttons and graphical text input to parameterize and process the phantom. Results: Our GUI can be used to: a) generate parameter files; b) generate the voxelized phantom; c) combine the phantom with a lesion; d) display the phantom; e) produce average and maximum intensity images from the phantom output files; f) incorporate irregular patient breathing patterns; and f) generate DICOM files containing phantom images. The GUI provides local help information using tool-tip strings on the currently selected phantom, minimizing the need for external documentation. The DICOM generation feature is intended to simplify the process of importing the phantom images into radiotherapy treatment planning systems or other clinical software. Conclusion: The GUI simplifies and automates the use of the XCAT phantom for imaging-based research projects in medical imaging or radiotherapy. This has the potential to accelerate research conducted with the XCAT phantom, or to ease the learning curve for new users. This tool does not include the XCAT phantom software itself. We would like to acknowledge funding from MRA, Varian Medical Systems Inc.« less

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

PubMed Central

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Digital image envelope: method and evaluation

NASA Astrophysics Data System (ADS)

Huang, H. K.; Cao, Fei; Zhou, Michael Z.; Mogel, Greg T.; Liu, Brent J.; Zhou, Xiaoqiang

2003-05-01

Health data security, characterized in terms of data privacy, authenticity, and integrity, is a vital issue when digital images and other patient information are transmitted through public networks in telehealth applications such as teleradiology. Mandates for ensuring health data security have been extensively discussed (for example The Health Insurance Portability and Accountability Act, HIPAA) and health informatics guidelines (such as the DICOM standard) are beginning to focus on issues of data continue to be published by organizing bodies in healthcare; however, there has not been a systematic method developed to ensure data security in medical imaging Because data privacy and authenticity are often managed primarily with firewall and password protection, we have focused our research and development on data integrity. We have developed a systematic method of ensuring medical image data integrity across public networks using the concept of the digital envelope. When a medical image is generated regardless of the modality, three processes are performed: the image signature is obtained, the DICOM image header is encrypted, and a digital envelope is formed by combining the signature and the encrypted header. The envelope is encrypted and embedded in the original image. This assures the security of both the image and the patient ID. The embedded image is encrypted again and transmitted across the network. The reverse process is performed at the receiving site. The result is two digital signatures, one from the original image before transmission, and second from the image after transmission. If the signatures are identical, there has been no alteration of the image. This paper concentrates in the method and evaluation of the digital image envelope.

Design of a web portal for interdisciplinary image retrieval from multiple online image resources.

PubMed

Kammerer, F J; Frankewitsch, T; Prokosch, H-U

2009-01-01

Images play an important role in medicine. Finding the desired images within the multitude of online image databases is a time-consuming and frustrating process. Existing websites do not meet all the requirements for an ideal learning environment for medical students. This work intends to establish a new web portal providing a centralized access point to a selected number of online image databases. A back-end system locates images on given websites and extracts relevant metadata. The images are indexed using UMLS and the MetaMap system provided by the US National Library of Medicine. Specially developed functions allow to create individual navigation structures. The front-end system suits the specific needs of medical students. A navigation structure consisting of several medical fields, university curricula and the ICD-10 was created. The images may be accessed via the given navigation structure or using different search functions. Cross-references are provided by the semantic relations of the UMLS. Over 25,000 images were identified and indexed. A pilot evaluation among medical students showed good first results concerning the acceptance of the developed navigation structures and search features. The integration of the images from different sources into the UMLS semantic network offers a quick and an easy-to-use learning environment.

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.

The Precise and Efficient Identification of Medical Order Forms Using Shape Trees

NASA Astrophysics Data System (ADS)

Henker, Uwe; Petersohn, Uwe; Ultsch, Alfred

A powerful and flexible technique to identify, classify and process documents using images from a scanning process is presented. The types of documents can be described to the system as a set of differentiating features in a case base using shape trees. The features are filtered and abstracted from an extremely reduced scanner image of the document. Classification rules are stored with the cases to enable precise recognition and further mark reading and Optical Character Recognition (OCR) process. The method is implemented in a system which actually processes the majority of requests for medical lab procedures in Germany. A large practical experiment with data from practitioners was performed. An average of 97% of the forms were correctly identified; none were identified incorrectly. This meets the quality requirements for most medical applications. The modular description of the recognition process allows for a flexible adaptation of future changes to the form and content of the document’s structures.

Medical color displays and their calibration

NASA Astrophysics Data System (ADS)

Fan, Jiahua; Roehrig, Hans; Dallas, W.; Krupinski, Elizabeth

2009-08-01

Color displays are increasingly used for medical imaging, replacing the traditional monochrome displays in radiology for multi-modality applications, 3D representation applications, etc. Color displays are also used increasingly because of wide spread application of Tele-Medicine, Tele-Dermatology and Digital Pathology. At this time, there is no concerted effort for calibration procedures for this diverse range of color displays in Telemedicine and in other areas of the medical field. Using a colorimeter to measure the display luminance and chrominance properties as well as some processing software we developed a first attempt to a color calibration protocol for the medical imaging field.

Before your very eyes: the value and limitations of eye tracking in medical education.

PubMed

Kok, Ellen M; Jarodzka, Halszka

2017-01-01

Medicine is a highly visual discipline. Physicians from many specialties constantly use visual information in diagnosis and treatment. However, they are often unable to explain how they use this information. Consequently, it is unclear how to train medical students in this visual processing. Eye tracking is a research technique that may offer answers to these open questions, as it enables researchers to investigate such visual processes directly by measuring eye movements. This may help researchers understand the processes that support or hinder a particular learning outcome. In this article, we clarify the value and limitations of eye tracking for medical education researchers. For example, eye tracking can clarify how experience with medical images mediates diagnostic performance and how students engage with learning materials. Furthermore, eye tracking can also be used directly for training purposes by displaying eye movements of experts in medical images. Eye movements reflect cognitive processes, but cognitive processes cannot be directly inferred from eye-tracking data. In order to interpret eye-tracking data properly, theoretical models must always be the basis for designing experiments as well as for analysing and interpreting eye-tracking data. The interpretation of eye-tracking data is further supported by sound experimental design and methodological triangulation. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Healthcare provider and patient perspectives on diagnostic imaging investigations.

PubMed

Makanjee, Chandra R; Bergh, Anne-Marie; Hoffmann, Willem A

2015-05-20

Much has been written about the patient-centred approach in doctor-patient consultations. Little is known about interactions and communication processes regarding healthcare providers' and patients' perspectives on expectations and experiences of diagnostic imaging investigations within the medical encounter. Patients journey through the health system from the point of referral to the imaging investigation itself and then to the post-imaging consultation. AIM AND SETTING: To explore healthcare provider and patient perspectives on interaction and communication processes during diagnostic imaging investigations as part of their clinical journey through a healthcare complex. A qualitative study was conducted, with two phases of data collection. Twenty-four patients were conveniently selected at a public district hospital complex and were followed throughout their journey in the hospital system, from admission to discharge. The second phase entailed focus group interviews conducted with providers in the district hospital and adjacent academic hospital (medical officers and family physicians, nurses, radiographers, radiology consultants and registrars). Two main themes guided our analysis: (1) provider perspectives; and (2) patient dispositions and reactions. Golden threads that cut across these themes are interactions and communication processes in the context of expectations, experiences of the imaging investigations and the outcomes thereof. Insights from this study provide a better understanding of the complexity of the processes and interactions between providers and patients during the imaging investigations conducted as part of their clinical pathway. The interactions and communication processes are provider-patient centred when a referral for a diagnostic imaging investigation is included.

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

Autofluorescence detection and imaging of bladder cancer realized through a cystoscope

DOEpatents

Demos, Stavros G [Livermore, CA; deVere White, Ralph W [Sacramento, CA

2007-08-14

Near infrared imaging using elastic light scattering and tissue autofluorescence and utilizing interior examination techniques and equipment are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and/or tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Novel medical image enhancement algorithms

NASA Astrophysics Data System (ADS)

Agaian, Sos; McClendon, Stephen A.

2010-01-01

In this paper, we present two novel medical image enhancement algorithms. The first, a global image enhancement algorithm, utilizes an alpha-trimmed mean filter as its backbone to sharpen images. The second algorithm uses a cascaded unsharp masking technique to separate the high frequency components of an image in order for them to be enhanced using a modified adaptive contrast enhancement algorithm. Experimental results from enhancing electron microscopy, radiological, CT scan and MRI scan images, using the MATLAB environment, are then compared to the original images as well as other enhancement methods, such as histogram equalization and two forms of adaptive contrast enhancement. An image processing scheme for electron microscopy images of Purkinje cells will also be implemented and utilized as a comparison tool to evaluate the performance of our algorithm.

A Novel Texture-Quantization-Based Reversible Multiple Watermarking Scheme Applied to Health Information System.

PubMed

Turuk, Mousami; Dhande, Ashwin

2018-04-01

The recent innovations in information and communication technologies have appreciably changed the panorama of health information system (HIS). These advances provide new means to process, handle, and share medical images and also augment the medical image security issues in terms of confidentiality, reliability, and integrity. Digital watermarking has emerged as new era that offers acceptable solutions to the security issues in HIS. Texture is a significant feature to detect the embedding sites in an image, which further leads to substantial improvement in the robustness. However, considering the perspective of digital watermarking, this feature has received meager attention in the reported literature. This paper exploits the texture property of an image and presents a novel hybrid texture-quantization-based approach for reversible multiple watermarking. The watermarked image quality has been accessed by peak signal to noise ratio (PSNR), structural similarity measure (SSIM), and universal image quality index (UIQI), and the obtained results are superior to the state-of-the-art methods. The algorithm has been evaluated on a variety of medical imaging modalities (CT, MRA, MRI, US) and robustness has been verified, considering various image processing attacks including JPEG compression. The proposed scheme offers additional security using repetitive embedding of BCH encoded watermarks and ADM encrypted ECG signal. Experimental results achieved a maximum of 22,616 bits hiding capacity with PSNR of 53.64 dB.

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.

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.

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 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.

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.

Twofold processing for denoising ultrasound medical images.

PubMed

Kishore, P V V; Kumar, K V V; Kumar, D Anil; Prasad, M V D; Goutham, E N D; Rahul, R; Krishna, C B S Vamsi; Sandeep, Y

2015-01-01

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

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.

A DICOM Based Collaborative Platform for Real-Time Medical Teleconsultation on Medical Images.

PubMed

Maglogiannis, Ilias; Andrikos, Christos; Rassias, Georgios; Tsanakas, Panayiotis

2017-01-01

The paper deals with the design of a Web-based platform for real-time medical teleconsultation on medical images. The proposed platform combines the principles of heterogeneous Workflow Management Systems (WfMSs), the peer-to-peer networking architecture and the SPA (Single-Page Application) concept, to facilitate medical collaboration among healthcare professionals geographically distributed. The presented work leverages state-of-the-art features of the web to support peer-to-peer communication using the WebRTC (Web Real Time Communication) protocol and client-side data processing for creating an integrated collaboration environment. The paper discusses the technical details of implementation and presents the operation of the platform in practice along with some initial results.

Images as embedding maps and minimal surfaces: Movies, color, and volumetric medical images

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

Kimmel, R.; Malladi, R.; Sochen, N.

A general geometrical framework for image processing is presented. The authors consider intensity images as surfaces in the (x,I) space. The image is thereby a two dimensional surface in three dimensional space for gray level images. The new formulation unifies many classical schemes, algorithms, and measures via choices of parameters in a {open_quote}master{close_quotes} geometrical measure. More important, it is a simple and efficient tool for the design of natural schemes for image enhancement, segmentation, and scale space. Here the authors give the basic motivation and apply the scheme to enhance images. They present the concept of an image as amore » surface in dimensions higher than the three dimensional intuitive space. This will help them handle movies, color, and volumetric medical images.« less

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

NASA Astrophysics Data System (ADS)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

[Non-medical applications for brain MRI: Ethical considerations].

PubMed

Sarrazin, S; Fagot-Largeault, A; Leboyer, M; Houenou, J

2015-04-01

The recent neuroimaging techniques offer the possibility to better understand complex cognitive processes that are involved in mental disorders and thus have become cornerstone tools for research in psychiatry. The performances of functional magnetic resonance imaging are not limited to medical research and are used in non-medical fields. These recent applications represent new challenges for bioethics. In this article we aim at discussing the new ethical issues raised by the applications of the latest neuroimaging technologies to non-medical fields. We included a selection of peer-reviewed English medical articles after a search on NCBI Pubmed database and Google scholar from 2000 to 2013. We screened bibliographical tables for supplementary references. Websites of governmental French institutions implicated in ethical questions were also screened for governmental reports. Findings of brain areas supporting emotional responses and regulation have been used for marketing research, also called neuromarketing. The discovery of different brain activation patterns in antisocial disorder has led to changes in forensic psychiatry with the use of imaging techniques with unproven validity. Automated classification algorithms and multivariate statistical analyses of brain images have been applied to brain-reading techniques, aiming at predicting unconscious neural processes in humans. We finally report the current position of the French legislation recently revised and discuss the technical limits of such techniques. In the near future, brain imaging could find clinical applications in psychiatry as diagnostic or predictive tools. However, the latest advances in brain imaging are also used in non-scientific fields raising key ethical questions. Involvement of neuroscientists, psychiatrists, physicians but also of citizens in neuroethics discussions is crucial to challenge the risk of unregulated uses of brain imaging. Copyright © 2014 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

PACS viewer interoperability for teleconsultation based on DICOM

NASA Astrophysics Data System (ADS)

Salant, Eliot; Shani, Uri

2000-05-01

Real-time teleconsultation in radiology enables physicians to perform same-time consultation between remote peers, based on medical images. Since digital medical images are commonly viewed on PACS workstations, it is possible to use one of several methods for remote sharing of the computer screen. For instance, software products such as Microsoft NetMeeting, or IBM SameTime, can be used. However, the amount of image data transmitted can be very high, since even minute changes in an image window/level requires re-transmitting the entire image again and again. This is too inefficient. Looking for better methods, when restricting the problem to the use of same hardware and software of the same vendor, it is easier to develop a solution that employs a proprietary specialized protocol to coordinate the visualization process. Such is a solution that we developed, and which demonstrated an excellent performance advantage by transmitting only the graphical events between the machines, rather than the image pixels. Our solution did not inter-operate with other viewers. It worked only on X11/Motif systems, and only between compatible versions of the same viewer application. Our purpose in this paper is to enable inter-operability between viewers of different platforms, and different vendors. We distinguish three parts: Session control, audiovisual (multimedia) data exchange, and medical image sharing. We intend to deal only with the third component, assuming the use of existing standards for the first two parts. After a session between two or more parties is established, and optional audiovisual data channels are set, the medical consultation is considered as the coordinated exchange of medical image contents. Some requirements for the contents exchange protocol: In the first stage, the parties negotiate the actual set of capabilities to be used during the consultation, using a formal description of these capabilities. The capabilities that one station lacks over the other (such as specific image processing algorithms) can be 'borrowed.' In the second stage, when interaction starts, it should assume that the graphical user interface of the stations might be different, as well as working procedures. During the consultation, data is exchanged based on DICOM for the data model of medical image folders, and the data format of image objects.

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.

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.

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

A Neuroimaging Web Services Interface as a Cyber Physical System for Medical Imaging and Data Management in Brain Research: Design Study.

PubMed

Lizarraga, Gabriel; Li, Chunfei; Cabrerizo, Mercedes; Barker, Warren; Loewenstein, David A; Duara, Ranjan; Adjouadi, Malek

2018-04-26

Structural and functional brain images are essential imaging modalities for medical experts to study brain anatomy. These images are typically visually inspected by experts. To analyze images without any bias, they must be first converted to numeric values. Many software packages are available to process the images, but they are complex and difficult to use. The software packages are also hardware intensive. The results obtained after processing vary depending on the native operating system used and its associated software libraries; data processed in one system cannot typically be combined with data on another system. The aim of this study was to fulfill the neuroimaging community’s need for a common platform to store, process, explore, and visualize their neuroimaging data and results using Neuroimaging Web Services Interface: a series of processing pipelines designed as a cyber physical system for neuroimaging and clinical data in brain research. Neuroimaging Web Services Interface accepts magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and functional magnetic resonance imaging. These images are processed using existing and custom software packages. The output is then stored as image files, tabulated files, and MySQL tables. The system, made up of a series of interconnected servers, is password-protected and is securely accessible through a Web interface and allows (1) visualization of results and (2) downloading of tabulated data. All results were obtained using our processing servers in order to maintain data validity and consistency. The design is responsive and scalable. The processing pipeline started from a FreeSurfer reconstruction of Structural magnetic resonance imaging images. The FreeSurfer and regional standardized uptake value ratio calculations were validated using Alzheimer’s Disease Neuroimaging Initiative input images, and the results were posted at the Laboratory of Neuro Imaging data archive. Notable leading researchers in the field of Alzheimer’s Disease and epilepsy have used the interface to access and process the data and visualize the results. Tabulated results with unique visualization mechanisms help guide more informed diagnosis and expert rating, providing a truly unique multimodal imaging platform that combines magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and resting state functional magnetic resonance imaging. A quality control component was reinforced through expert visual rating involving at least 2 experts. To our knowledge, there is no validated Web-based system offering all the services that Neuroimaging Web Services Interface offers. The intent of Neuroimaging Web Services Interface is to create a tool for clinicians and researchers with keen interest on multimodal neuroimaging. More importantly, Neuroimaging Web Services Interface significantly augments the Alzheimer’s Disease Neuroimaging Initiative data, especially since our data contain a large cohort of Hispanic normal controls and Alzheimer’s Disease patients. The obtained results could be scrutinized visually or through the tabulated forms, informing researchers on subtle changes that characterize the different stages of the disease. ©Gabriel Lizarraga, Chunfei Li, Mercedes Cabrerizo, Warren Barker, David A Loewenstein, Ranjan Duara, Malek Adjouadi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.04.2018.

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).

Preliminary results of 3D dose calculations with MCNP-4B code from a SPECT image.

PubMed

Rodríguez Gual, M; Lima, F F; Sospedra Alfonso, R; González González, J; Calderón Marín, C

2004-01-01

Interface software was developed to generate the input file to run Monte Carlo MCNP-4B code from medical image in Interfile format version 3.3. The software was tested using a spherical phantom of tomography slides with known cumulated activity distribution in Interfile format generated with IMAGAMMA medical image processing system. The 3D dose calculation obtained with Monte Carlo MCNP-4B code was compared with the voxel S factor method. The results show a relative error between both methods less than 1 %.

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.

Using quantum filters to process images of diffuse axonal injury

NASA Astrophysics Data System (ADS)

Pineda Osorio, Mateo

2014-06-01

Some images corresponding to a diffuse axonal injury (DAI) are processed using several quantum filters such as Hermite Weibull and Morse. Diffuse axonal injury is a particular, common and severe case of traumatic brain injury (TBI). DAI involves global damage on microscopic scale of brain tissue and causes serious neurologic abnormalities. New imaging techniques provide excellent images showing cellular damages related to DAI. Said images can be processed with quantum filters, which accomplish high resolutions of dendritic and axonal structures both in normal and pathological state. Using the Laplacian operators from the new quantum filters, excellent edge detectors for neurofiber resolution are obtained. Image quantum processing of DAI images is made using computer algebra, specifically Maple. Quantum filter plugins construction is proposed as a future research line, which can incorporated to the ImageJ software package, making its use simpler for medical personnel.

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).

Auditing The Completeness and Legibility of Computerized Radiological Request Forms.

PubMed

Al Muallem, Yahya; Al Dogether, Majed; Househ, Mowafa; Saddik, Basema

2017-11-04

Certain Saudi healthcare organizations transfer outpatients to medical imaging departments for radiological examinations in a manual process that relies on the use of paper-based forms. With the increased implementation of electronic medical records in Saudi Hospitals, little is known about the completeness and legibility of information captured in  electronic-based medical imaging forms. The purpose of this study is to audit the completeness and legibility of medical imaging paper-based forms in comparison with electronic-based medical imaging forms. As a secondary objective, we also examined the number of errors found on the forms.An observational retrospective cross-sectional study was utilized to audit the completeness and legibility of both paper and electronic forms collected between March 1 and May 15, 2015. The study measured the association among categorical variables using Chi-Square analysis. The results of this investigation show a significant association between form completion and type of record (i.e., paper vs. electronic) where electronic-based systems were found to be more complete than paper-based records. Electrnoic based records were also found to improve form legibility, promote user adherence to complete the forms and minimize entry errors. In conclusion, electronic-based medical imaging forms are more complete and legible than paper based forms. Future studies should evaluate other hospitals and compare both legibility and completeness of electronic-based medical imaging forms and conduct usability evaluation studies with users to explore the impacts of system design on both completeness and legibility of electronic forms, in general, but more specifically, electronic-based medical imaging forms.

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.

Using photoshop filters to create anatomic line-art medical images.

PubMed

Kirsch, Jacobo; Geller, Brian S

2006-08-01

There are multiple ways to obtain anatomic drawings suitable for publication or presentations. This article demonstrates how to use Photoshop to alter digital radiologic images to create line-art illustrations in a quick and easy way. We present two simple to use methods; however, not every image can adequately be transformed and personal preferences and specific changes need to be applied to each image to obtain the desired result. There are multiple ways to obtain anatomic drawings suitable for publication or to prepare presentations. Medical illustrators have always played a major role in the radiology and medical education process. Whether used to teach a complex surgical or radiologic procedure, to define typical or atypical patterns of the spread of disease, or to illustrate normal or aberrant anatomy, medical illustration significantly affects learning (). However, if you are not an accomplished illustrator, the alternatives can be expensive (contacting a professional medical illustrator or buying an already existing stock of digital images) or simply not necessarily applicable to what you are trying to communicate. The purpose of this article is to demonstrate how using Photoshop (Adobe Systems, San Jose, CA) to alter digital radiologic images we can create line-art illustrations in a quick, inexpensive, and easy way in preparation for electronic presentations and publication.

Towards real-time remote processing of laparoscopic video

NASA Astrophysics Data System (ADS)

Ronaghi, Zahra; Duffy, Edward B.; Kwartowitz, David M.

2015-03-01

Laparoscopic surgery is a minimally invasive surgical technique where surgeons insert a small video camera into the patient's body to visualize internal organs and small tools to perform surgical procedures. However, the benefit of small incisions has a drawback of limited visualization of subsurface tissues, which can lead to navigational challenges in the delivering of therapy. Image-guided surgery (IGS) uses images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system of interest is the vision system of the daVinci-Si robotic surgical system (Intuitive Surgical, Sunnyvale, CA, USA). The video streams generate approximately 360 megabytes of data per second, demonstrating a trend towards increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Processing this data on a bedside PC has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second (fps) rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. The ability to acquire, process and visualize data in real-time is essential for performance of complex tasks as well as minimizing risk to the patient. As a result, utilizing high-speed networks to access computing clusters will lead to real-time medical image processing and improve surgical experiences by providing real-time augmented laparoscopic data. We aim to develop a medical video processing system using an OpenFlow software defined network that is capable of connecting to multiple remote medical facilities and HPC servers.

[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.

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.

Collection of sequential imaging events for research in breast cancer screening

NASA Astrophysics Data System (ADS)

Patel, M. N.; Young, K.; Halling-Brown, M. D.

2016-03-01

Due to the huge amount of research involving medical images, there is a widely accepted need for comprehensive collections of medical images to be made available for research. This demand led to the design and implementation of a flexible image repository, which retrospectively collects images and data from multiple sites throughout the UK. The OPTIMAM Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, annotations and expert-determined ground truths. Collection has been ongoing for over three years, providing the opportunity to collect sequential imaging events. Extensive alterations to the identification, collection, processing and storage arms of the system have been undertaken to support the introduction of sequential events, including interval cancers. These updates to the collection systems allow the acquisition of many more images, but more importantly, allow one to build on the existing high-dimensional data stored in the OMI-DB. A research dataset of this scale, which includes original normal and subsequent malignant cases along with expert derived and clinical annotations, is currently unique. These data provide a powerful resource for future research and has initiated new research projects, amongst which, is the quantification of normal cases by applying a large number of quantitative imaging features, with a priori knowledge that eventually these cases develop a malignancy. This paper describes, extensions to the OMI-DB collection systems and tools and discusses the prospective applications of having such a rich dataset for future research applications.

Block selective redaction for minimizing loss during de-identification of burned in text in irreversibly compressed JPEG medical images.

PubMed

Clunie, David A; Gebow, Dan

2015-01-01

Deidentification of medical images requires attention to both header information as well as the pixel data itself, in which burned-in text may be present. If the pixel data to be deidentified is stored in a compressed form, traditionally it is decompressed, identifying text is redacted, and if necessary, pixel data are recompressed. Decompression without recompression may result in images of excessive or intractable size. Recompression with an irreversible scheme is undesirable because it may cause additional loss in the diagnostically relevant regions of the images. The irreversible (lossy) JPEG compression scheme works on small blocks of the image independently, hence, redaction can selectively be confined only to those blocks containing identifying text, leaving all other blocks unchanged. An open source implementation of selective redaction and a demonstration of its applicability to multiframe color ultrasound images is described. The process can be applied either to standalone JPEG images or JPEG bit streams encapsulated in other formats, which in the case of medical images, is usually DICOM.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

PubMed

Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

2012-01-01

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

Unified Digital Image Display And Processing System

NASA Astrophysics Data System (ADS)

Horii, Steven C.; Maguire, Gerald Q.; Noz, Marilyn E.; Schimpf, James H.

1981-11-01

Our institution like many others, is faced with a proliferation of medical imaging techniques. Many of these methods give rise to digital images (e.g. digital radiography, computerized tomography (CT) , nuclear medicine and ultrasound). We feel that a unified, digital system approach to image management (storage, transmission and retrieval), image processing and image display will help in integrating these new modalities into the present diagnostic radiology operations. Future techniques are likely to employ digital images, so such a system could readily be expanded to include other image sources. We presently have the core of such a system. We can both view and process digital nuclear medicine (conventional gamma camera) images, positron emission tomography (PET) and CT images on a single system. Images from our recently installed digital radiographic unit can be added. Our paper describes our present system, explains the rationale for its configuration, and describes the directions in which it will expand.

[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.

Image-based electronic patient records for secured collaborative medical applications.

PubMed

Zhang, Jianguo; Sun, Jianyong; Yang, Yuanyuan; Liang, Chenwen; Yao, Yihong; Cai, Weihua; Jin, Jin; Zhang, Guozhen; Sun, Kun

2005-01-01

We developed a Web-based system to interactively display image-based electronic patient records (EPR) for secured intranet and Internet collaborative medical applications. The system consists of four major components: EPR DICOM gateway (EPR-GW), Image-based EPR repository server (EPR-Server), Web Server and EPR DICOM viewer (EPR-Viewer). In the EPR-GW and EPR-Viewer, the security modules of Digital Signature and Authentication are integrated to perform the security processing on the EPR data with integrity and authenticity. The privacy of EPR in data communication and exchanging is provided by SSL/TLS-based secure communication. This presentation gave a new approach to create and manage image-based EPR from actual patient records, and also presented a way to use Web technology and DICOM standard to build an open architecture for collaborative medical applications.

Mechanization in a New Medical School Library II. Serials and Circulation

PubMed Central

Payne, Ladye Margarete; Small, Louise; Divett, Robert T.

1966-01-01

The serials and circulation phases of the data-processing system in use at the University of New Mexico Library of the Medical Sciences are described. The development of the programs is also reported. The serials program uses simple punched card equipment. The circulation program uses the IBM 357 Data Collection System and punched card data-processing equipment. Images PMID:5921473

Adoption of high technology medical imaging and hospital quality and efficiency: Towards a conceptual framework.

PubMed

Sandoval, Guillermo A; Brown, Adalsteinn D; Wodchis, Walter P; Anderson, Geoffrey M

2018-05-17

Measuring the value of medical imaging is challenging, in part, due to the lack of conceptual frameworks underlying potential mechanisms where value may be assessed. To address this gap, this article proposes a framework that builds on the large body of literature on quality of hospital care and the classic structure-process-outcome paradigm. The framework was also informed by the literature on adoption of technological innovations and introduces 2 distinct though related aspects of imaging technology not previously addressed specifically in the literature on quality of hospital care: adoption (a structural hospital characteristic) and use (an attribute of the process of care). The framework hypothesizes a 2-part causality where adoption is proposed to be a central, linking factor between hospital structural characteristics, market factors, and hospital outcomes (ie, quality and efficiency). The first part indicates that hospital structural characteristics and market factors influence or facilitate the adoption of high technology medical imaging within an institution. The presence of this technology, in turn, is hypothesized to improve the ability of the hospital to deliver high quality and efficient care. The second part describes this ability throughout 3 main mechanisms pointing to the importance of imaging use on patients, to the presence of staff and qualified care providers, and to some elements of organizational capacity capturing an enhanced clinical environment. The framework has the potential to assist empirical investigations of the value of adoption and use of medical imaging, and to advance understanding of the mechanisms that produce quality and efficiency in hospitals. Copyright © 2018 John Wiley & Sons, Ltd.

[Study of Image Quality Comparison Based on the MTF Method Between Different Medical Rigid Endoscopes in an In Vitro Model].

PubMed

Wang, Yunlong; Ji, Jun; Jiang, Changsong; Huang, Zengyue

2015-04-01

This study was aimed to use the method of modulation transfer function (MTF) to compare image quality among three different Olympus medical rigid cystoscopes in an in vitro model. During the experimental processes, we firstly used three different types of cystoscopes (i. e. OLYMPUS cystourethroscopy with FOV of 12 degrees, OLYMPUS Germany A22003A and OLYMPUS A2013A) to collect raster images at different brightness with industrial camera and computer from the resolution target which is with different spatial frequency, and then we processed the collected images using MALAB software with the optical transfer function MTF to obtain the values of MTF at different brightness and different spatial frequency. We then did data mathematical statistics and compared imaging quality. The statistical data showed that all three MTF values were smaller than 1. MTF values with the spatial frequency gradually increasing would decrease approaching 0 at the same brightness. When the brightness enhanced in the same process at the same spatial frequency, MTF values showed a slowly increasing trend. The three endoscopes' MTF values were completely different. In some cases the MTF values had a large difference, and the maximum difference could reach 0.7. Conclusion can be derived from analysis of experimental data that three Olympus medical rigid cystoscopes have completely different imaging quality abilities. The No. 3 endoscope OLYMPUS A2013A has low resolution but high contrast. The No. 1 endoscope OLYMPUS cystourethroscopy with FOV of 12 degrees, on the contrary, had high resolution and lower contrast. The No. 2 endoscope OLYMPUS Germany A22003A had high contrast and high resolution, and its image quality was the best.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Is airport baggage inspection just another medical image?

NASA Astrophysics Data System (ADS)

Gale, Alastair G.; Mugglestone, Mark D.; Purdy, Kevin J.; McClumpha, A.

2000-04-01

A similar inspection situation to medical imaging appears to be that of the airport security screener who examines X-ray images of passenger baggage. There is, however, little research overlap between the two areas. Studies of observer performance in examining medical images have led to a conceptual model which has been used successfully to understand diagnostic errors and develop appropriate training strategies. The model stresses three processes of; visual search, detection of potential targets, and interpretation of these areas; with most errors being due to the latter two factors. An initial study is reported on baggage inspection, using several brief image presentations, to examine the applicability of such a medical model to this domain. The task selected was the identification of potential Improvised Explosive Devices (IEDs). Specifically investigated was the visual search behavior of inspectors. It was found that IEDs could be identified in a very brief image presentation, with increased presentation time this performance improved. Participants fixated on IEDs very early on and sometimes concentrated wholly on this part of the baggage display. When IEDs were missed this was mainly due to interpretative factors rather than visual search or IED detection. It is argued that the observer model can be applied successfully to this scenario.

Open-source software platform for medical image segmentation applications

NASA Astrophysics Data System (ADS)

Namías, R.; D'Amato, J. P.; del Fresno, M.

2017-11-01

Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We present the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.

A Rotor Tip Vortex Tracing Algorithm for Image Post-Processing

NASA Technical Reports Server (NTRS)

Overmeyer, Austin D.

2015-01-01

A neurite tracing algorithm, originally developed for medical image processing, was used to trace the location of the rotor tip vortex in density gradient flow visualization images. The tracing algorithm was applied to several representative test images to form case studies. The accuracy of the tracing algorithm was compared to two current methods including a manual point and click method and a cross-correlation template method. It is shown that the neurite tracing algorithm can reduce the post-processing time to trace the vortex by a factor of 10 to 15 without compromising the accuracy of the tip vortex location compared to other methods presented in literature.

Open source software in a practical approach for post processing of radiologic images.

PubMed

Valeri, Gianluca; Mazza, Francesco Antonino; Maggi, Stefania; Aramini, Daniele; La Riccia, Luigi; Mazzoni, Giovanni; Giovagnoni, Andrea

2015-03-01

The purpose of this paper is to evaluate the use of open source software (OSS) to process DICOM images. We selected 23 programs for Windows and 20 programs for Mac from 150 possible OSS programs including DICOM viewers and various tools (converters, DICOM header editors, etc.). The programs selected all meet the basic requirements such as free availability, stand-alone application, presence of graphical user interface, ease of installation and advanced features beyond simple display monitor. Capabilities of data import, data export, metadata, 2D viewer, 3D viewer, support platform and usability of each selected program were evaluated on a scale ranging from 1 to 10 points. Twelve programs received a score higher than or equal to eight. Among them, five obtained a score of 9: 3D Slicer, MedINRIA, MITK 3M3, VolView, VR Render; while OsiriX received 10. OsiriX appears to be the only program able to perform all the operations taken into consideration, similar to a workstation equipped with proprietary software, allowing the analysis and interpretation of images in a simple and intuitive way. OsiriX is a DICOM PACS workstation for medical imaging and software for image processing for medical research, functional imaging, 3D imaging, confocal microscopy and molecular imaging. This application is also a good tool for teaching activities because it facilitates the attainment of learning objectives among students and other specialists.

Medical Image Analysis Facility

NASA Technical Reports Server (NTRS)

1978-01-01

To improve the quality of photos sent to Earth by unmanned spacecraft. NASA's Jet Propulsion Laboratory (JPL) developed a computerized image enhancement process that brings out detail not visible in the basic photo. JPL is now applying this technology to biomedical research in its Medical lrnage Analysis Facility, which employs computer enhancement techniques to analyze x-ray films of internal organs, such as the heart and lung. A major objective is study of the effects of I stress on persons with heart disease. In animal tests, computerized image processing is being used to study coronary artery lesions and the degree to which they reduce arterial blood flow when stress is applied. The photos illustrate the enhancement process. The upper picture is an x-ray photo in which the artery (dotted line) is barely discernible; in the post-enhancement photo at right, the whole artery and the lesions along its wall are clearly visible. The Medical lrnage Analysis Facility offers a faster means of studying the effects of complex coronary lesions in humans, and the research now being conducted on animals is expected to have important application to diagnosis and treatment of human coronary disease. Other uses of the facility's image processing capability include analysis of muscle biopsy and pap smear specimens, and study of the microscopic structure of fibroprotein in the human lung. Working with JPL on experiments are NASA's Ames Research Center, the University of Southern California School of Medicine, and Rancho Los Amigos Hospital, Downey, California.

TU-AB-204-01: Device Approval Process

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

Delfino, J.

The responsibilities of the Food and Drug Administration (FDA) have increased since the inception of the Food and Drugs Act in 1906. Medical devices first came under comprehensive regulation with the passage of the 1938 Food, Drug, and Cosmetic Act. In 1971 FDA also took on the responsibility for consumer protection against unnecessary exposure to radiation-emitting devices for home and occupational use. However it was not until 1976, under the Medical Device Regulation Act, that the FDA was responsible for the safety and effectiveness of medical devices. This session will be presented by the Division of Radiological Health (DRH) andmore » the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) from the Center for Devices and Radiological Health (CDRH) at the FDA. The symposium will discuss on how we protect and promote public health with a focus on medical physics applications organized into four areas: pre-market device review, post-market surveillance, device compliance, current regulatory research efforts and partnerships with other organizations. The pre-market session will summarize the pathways FDA uses to regulate the investigational use and commercialization of diagnostic imaging and radiation therapy medical devices in the US, highlighting resources available to assist investigators and manufacturers. The post-market session will explain the post-market surveillance and compliance activities FDA performs to monitor the safety and effectiveness of devices on the market. The third session will describe research efforts that support the regulatory mission of the Agency. An overview of our regulatory research portfolio to advance our understanding of medical physics and imaging technologies and approaches to their evaluation will be discussed. Lastly, mechanisms that FDA uses to seek public input and promote collaborations with professional, government, and international organizations, such as AAPM, International Electrotechnical Commission (IEC), Image Gently, and the Quantitative Imaging Biomarkers Alliance (QIBA) among others, to fulfill FDA’s mission will be discussed. Learning Objectives: Understand FDA’s pre-market and post-market review processes for medical devices Understand FDA’s current regulatory research activities in the areas of medical physics and imaging products Understand how being involved with AAPM and other organizations can also help to promote innovative, safe and effective medical devices J. Delfino, nothing to disclose.« less

TU-AB-204-00: CDRH/FDA Regulatory Processes and Device Science Activities

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

NONE

The responsibilities of the Food and Drug Administration (FDA) have increased since the inception of the Food and Drugs Act in 1906. Medical devices first came under comprehensive regulation with the passage of the 1938 Food, Drug, and Cosmetic Act. In 1971 FDA also took on the responsibility for consumer protection against unnecessary exposure to radiation-emitting devices for home and occupational use. However it was not until 1976, under the Medical Device Regulation Act, that the FDA was responsible for the safety and effectiveness of medical devices. This session will be presented by the Division of Radiological Health (DRH) andmore » the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) from the Center for Devices and Radiological Health (CDRH) at the FDA. The symposium will discuss on how we protect and promote public health with a focus on medical physics applications organized into four areas: pre-market device review, post-market surveillance, device compliance, current regulatory research efforts and partnerships with other organizations. The pre-market session will summarize the pathways FDA uses to regulate the investigational use and commercialization of diagnostic imaging and radiation therapy medical devices in the US, highlighting resources available to assist investigators and manufacturers. The post-market session will explain the post-market surveillance and compliance activities FDA performs to monitor the safety and effectiveness of devices on the market. The third session will describe research efforts that support the regulatory mission of the Agency. An overview of our regulatory research portfolio to advance our understanding of medical physics and imaging technologies and approaches to their evaluation will be discussed. Lastly, mechanisms that FDA uses to seek public input and promote collaborations with professional, government, and international organizations, such as AAPM, International Electrotechnical Commission (IEC), Image Gently, and the Quantitative Imaging Biomarkers Alliance (QIBA) among others, to fulfill FDA’s mission will be discussed. Learning Objectives: Understand FDA’s pre-market and post-market review processes for medical devices Understand FDA’s current regulatory research activities in the areas of medical physics and imaging products Understand how being involved with AAPM and other organizations can also help to promote innovative, safe and effective medical devices J. Delfino, nothing to disclose.« less

Is it possible to eliminate patient identification errors in medical imaging?

PubMed

Danaher, Luke A; Howells, Joan; Holmes, Penny; Scally, Peter

2011-08-01

The aim of this article is to review a system that validates and documents the process of ensuring the correct patient, correct site and side, and correct procedure (commonly referred to as the 3 C's) within medical imaging. A 4-step patient identification and procedure matching process was developed using health care and aviation models. The process was established in medical imaging departments after a successful interventional radiology pilot program. The success of the project was evaluated using compliance audit data, incident reporting data before and after the implementation of the process, and a staff satisfaction survey. There was 95% to 100% verification of site and side and 100% verification of correct patient, procedure, and consent. Correct patient data and side markers were present in 82% to 95% of cases. The number of incidents before and after the implementation of the 3 C's was difficult to assess because of a change in reporting systems and incident underreporting. More incidents are being reported, particularly "near misses." All near misses were related to incorrect patient identification stickers being placed on request forms. The majority of staff members surveyed found the process easy (55.8%), quick (47.7%), relevant (51.7%), and useful (60.9%). Although identification error is difficult to eliminate, practical initiatives can engender significant systems improvement in complex health care environments. Crown Copyright © 2011. Published by Elsevier Inc. All rights reserved.

TU-C-218-01: Effective Medical Imaging Physics Education.

PubMed

Sprawls, P

2012-06-01

A practical and applied knowledge of physics and the associated technology is required for the clinically effective and safe use of the various medical imaging modalities. This is needed by all involved in the imaging process, including radiologists, especially residents in training, technologists, and physicists who provide consultation on optimum and safe procedures and as educators for the other imaging professionals. This area of education is undergoing considerable change and evolution for three reasons: 1. Increasing capabilities and complexity of medical imaging technology and procedures, 2.Expanding scope and availability of educational resources, especially on the internet, and 3. A significant increase in our knowledge of the mental learning process and the design of learning activities to optimize effectiveness and efficiency, especially for clinically applied physics. This course will address those three issues by providing guidance on establishing appropriate clinically focused learning outcomes, a review of the brain function for enhancing clinically applied physics, and the design and delivery of effective learning activities beginning with the classroom and continuing through learning physics during the clinical practice of radiology. Characteristics of each type of learning activity will be considered with respect to effectiveness and efficiency in achieving appropriate learning outcomes. A variety of available resources will be identified and demonstrated for use in the different phases of learning process. A major focus is on enhancing the role of the medical physicist in clinical radiology both as a resource and educator with contemporary technology being the tool, but not the teacher. 1. Develop physics learning objectives that will support effective and safe medical imaging procedures. 2. Understand specific brain functions that are involved in learning and applying physics. 3. Describe the characteristics and development of mental knowledge structures for applied clinical physics. 4. List the established levels of learning and associate each with specific functions that can be performed. 5. Analyze the different types of learning activities (classroom, individual study, clinical, etc.) with respect to effectiveness and efficiency. 6. Design and Provide a comprehensive physics education program with each activity optimized with respect to outcomes and available resources. © 2012 American Association of Physicists in Medicine.

Telemedicine and distributed medical intelligence.

PubMed

Warner, D; Tichenor, J M; Balch, D C

1996-01-01

Recent trends in health care informatics and telemedicine indicate that systems are being developed with a primary focus on technology and business, not on the process of medicine itself. The authors present a new model of health care information, distributed medical intelligence, which promotes the development of an integrative medical communication system addressing the process of providing expert medical knowledge to the point of need. The model incorporates audio, video, high-resolution still images, and virtual reality applications into an integrated medical communications network. Three components of the model (care portals, Docking Station, and the bridge) are described. The implementation of this model at the East Carolina University School of Medicine is also outlined.

Fast processing of microscopic images using object-based extended depth of field.

PubMed

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Pannarut, Montri; Shaw, Philip J; Tongsima, Sissades

2016-12-22

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue. The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time. We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time. This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.

Radiomics: Images Are More than Pictures, They Are Data

PubMed Central

Kinahan, Paul E.; Hricak, Hedvig

2016-01-01

In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer. PMID:26579733

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.

Clinical image processing engine

NASA Astrophysics Data System (ADS)

Han, Wei; Yao, Jianhua; Chen, Jeremy; Summers, Ronald

2009-02-01

Our group provides clinical image processing services to various institutes at NIH. We develop or adapt image processing programs for a variety of applications. However, each program requires a human operator to select a specific set of images and execute the program, as well as store the results appropriately for later use. To improve efficiency, we design a parallelized clinical image processing engine (CIPE) to streamline and parallelize our service. The engine takes DICOM images from a PACS server, sorts and distributes the images to different applications, multithreads the execution of applications, and collects results from the applications. The engine consists of four modules: a listener, a router, a job manager and a data manager. A template filter in XML format is defined to specify the image specification for each application. A MySQL database is created to store and manage the incoming DICOM images and application results. The engine achieves two important goals: reduce the amount of time and manpower required to process medical images, and reduce the turnaround time for responding. We tested our engine on three different applications with 12 datasets and demonstrated that the engine improved the efficiency dramatically.

Medical image processing using neural networks based on multivalued and universal binary neurons

NASA Astrophysics Data System (ADS)

Aizenberg, Igor N.; Aizenberg, Naum N.; Gotko, Eugen S.; Sochka, Vladimir A.

1998-06-01

Cellular Neural Networks (CNN) has become a very good mean for solution of the different kind of image processing problems. CNN based on multi-valued neurons (CNN-MVN) and CNN based on universal binary neurons (CNN-UBN) are the specific kinds of the CNN. MVN and UBN are neurons with complex-valued weights, and complex internal arithmetic. Their main feature is possibility of implementation of the arbitrary mapping between inputs and output described by the MVN, and arbitrary (not only threshold) Boolean function (UBN). Great advantage of the CNN is possibility of implementation of the any linear and many non-linear filters in spatial domain. Together with noise removing using CNN it is possible to implement filters, which can amplify high and medium frequencies. These filters are a very good mean for solution of the enhancement problem, and problem of details extraction against complex background. So, CNN make it possible to organize all the processing process from filtering until extraction of the important details. Organization of this process for medical image processing is considered in the paper. A major attention will be concentrated on the processing of the x-ray and ultrasound images corresponding to different oncology (or closed to oncology) pathologies. Additionally we will consider new structure of the neural network for solution of the problem of differential diagnostics of breast cancer.

[Development of a Text-Data Based Learning Tool That Integrates Image Processing and Displaying].

PubMed

Shinohara, Hiroyuki; Hashimoto, Takeyuki

2015-01-01

We developed a text-data based learning tool that integrates image processing and displaying by Excel. Knowledge required for programing this tool is limited to using absolute, relative, and composite cell references and learning approximately 20 mathematical functions available in Excel. The new tool is capable of resolution translation, geometric transformation, spatial-filter processing, Radon transform, Fourier transform, convolutions, correlations, deconvolutions, wavelet transform, mutual information, and simulation of proton density-, T1-, and T2-weighted MR images. The processed images of 128 x 128 pixels or 256 x 256 pixels are observed directly within Excel worksheets without using any particular image display software. The results of image processing using this tool were compared with those using C language and the new tool was judged to have sufficient accuracy to be practically useful. The images displayed on Excel worksheets were compared with images using binary-data display software. This comparison indicated that the image quality of the Excel worksheets was nearly equal to the latter in visual impressions. Since image processing is performed by using text-data, the process is visible and facilitates making contrasts by using mathematical equations within the program. We concluded that the newly developed tool is adequate as a computer-assisted learning tool for use in medical image processing.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

PubMed

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

A new method of cardiographic image segmentation based on grammar

NASA Astrophysics Data System (ADS)

Hamdi, Salah; Ben Abdallah, Asma; Bedoui, Mohamed H.; Alimi, Adel M.

2011-10-01

The measurement of the most common ultrasound parameters, such as aortic area, mitral area and left ventricle (LV) volume, requires the delineation of the organ in order to estimate the area. In terms of medical image processing this translates into the need to segment the image and define the contours as accurately as possible. The aim of this work is to segment an image and make an automated area estimation based on grammar. The entity "language" will be projected to the entity "image" to perform structural analysis and parsing of the image. We will show how the idea of segmentation and grammar-based area estimation is applied to real problems of cardio-graphic image processing.

MO-DE-BRA-05: Developing Effective Medical Physics Knowledge Structures: Models and Methods

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

Sprawls, P

Purpose: Develop a method and supporting online resources to be used by medical physics educators for teaching medical imaging professionals and trainees so they develop highly-effective physics knowledge structures that can contribute to improved diagnostic image quality on a global basis. Methods: The different types of mental knowledge structures were analyzed and modeled with respect to both the learning and teaching process for their development and the functions or tasks that can be performed with the knowledge. While symbolic verbal and mathematical knowledge structures are very important in medical physics for many purposes, the tasks of applying physics in clinicalmore » imaging--especially to optimize image quality and diagnostic accuracy--requires a sensory conceptual knowledge structure, specifically, an interconnected network of visually based concepts. This type of knowledge supports tasks such as analysis, evaluation, problem solving, interacting, and creating solutions. Traditional educational methods including lectures, online modules, and many texts are serial procedures and limited with respect to developing interconnected conceptual networks. A method consisting of the synergistic combination of on-site medical physics teachers and the online resource, CONET (Concept network developer), has been developed and made available for the topic Radiographic Image Quality. This was selected as the inaugural topic, others to follow, because it can be used by medical physicists teaching the large population of medical imaging professionals, such as radiology residents, who can apply the knowledge. Results: Tutorials for medical physics educators on developing effective knowledge structures are being presented and published and CONET is available with open access for all to use. Conclusion: An adjunct to traditional medical physics educational methods with the added focus on sensory concept development provides opportunities for medical physics teachers to share their knowledge and experience at a higher cognitive level and produce medical professionals with the enhanced ability to apply physics to clinical procedures.« less

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

Photogrammetry in 3d Modelling of Human Bone Structures from Radiographs

NASA Astrophysics Data System (ADS)

Hosseinian, S.; Arefi, H.

2017-05-01

Photogrammetry can have great impact on the success of medical processes for diagnosis, treatment and surgeries. Precise 3D models which can be achieved by photogrammetry improve considerably the results of orthopedic surgeries and processes. Usual 3D imaging techniques, computed tomography (CT) and magnetic resonance imaging (MRI), have some limitations such as being used only in non-weight-bearing positions, costs and high radiation dose(for CT) and limitations of MRI for patients with ferromagnetic implants or objects in their bodies. 3D reconstruction of bony structures from biplanar X-ray images is a reliable and accepted alternative for achieving accurate 3D information with low dose radiation in weight-bearing positions. The information can be obtained from multi-view radiographs by using photogrammetry. The primary step for 3D reconstruction of human bone structure from medical X-ray images is calibration which is done by applying principles of photogrammetry. After the calibration step, 3D reconstruction can be done using efficient methods with different levels of automation. Because of the different nature of X-ray images from optical images, there are distinct challenges in medical applications for calibration step of stereoradiography. In this paper, after demonstrating the general steps and principles of 3D reconstruction from X-ray images, a comparison will be done on calibration methods for 3D reconstruction from radiographs and they are assessed from photogrammetry point of view by considering various metrics such as their camera models, calibration objects, accuracy, availability, patient-friendly and cost.

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.

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

Cardiac imaging modalities with ionizing radiation: the role of informed consent.

PubMed

Paterick, Timothy E; Jan, M Fuad; Paterick, Zachary R; Tajik, A Jamil; Gerber, Thomas C

2012-06-01

Informed consent ideally results in patient autonomy and rational health care decisions. Frequently, patients face complex medical decisions that require a delicate balancing of anticipated benefits and potential risks, which is the concept of informed consent. This balancing process requires an understanding of available medical evidence and alternative medical options, and input from experienced physicians. The informed consent doctrine places a positive obligation on physicians to partner with patients as they try to make the best decision for their specific medical situation. The high prevalence and mortality related to heart disease in our society has led to increased cardiac imaging with modalities that use ionizing radiation. This paper reviews how physicians can meet the ideals of informed consent when considering cardiac imaging with ionizing radiation, given the limited evidence for risks and benefits. The goal is an informed patient making rational choices based on available medical information. Copyright © 2012 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.

PubMed

Ganasala, Padma; Kumar, Vinod

2016-02-01

Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.

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.

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

A RESTful image gateway for multiple medical image repositories.

PubMed

Valente, Frederico; Viana-Ferreira, Carlos; Costa, Carlos; Oliveira, José Luis

2012-05-01

Mobile technologies are increasingly important components in telemedicine systems and are becoming powerful decision support tools. Universal access to data may already be achieved by resorting to the latest generation of tablet devices and smartphones. However, the protocols employed for communicating with image repositories are not suited to exchange data with mobile devices. In this paper, we present an extensible approach to solving the problem of querying and delivering data in a format that is suitable for the bandwidth and graphic capacities of mobile devices. We describe a three-tiered component-based gateway that acts as an intermediary between medical applications and a number of Picture Archiving and Communication Systems (PACS). The interface with the gateway is accomplished using Hypertext Transfer Protocol (HTTP) requests following a Representational State Transfer (REST) methodology, which relieves developers from dealing with complex medical imaging protocols and allows the processing of data on the server side.

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.

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.

Web tools for effective retrieval, visualization, and evaluation of cardiology medical images and records

NASA Astrophysics Data System (ADS)

Masseroli, Marco; Pinciroli, Francesco

2000-12-01

To provide easy retrieval, integration and evaluation of multimodal cardiology images and data in a web browser environment, distributed application technologies and java programming were used to implement a client-server architecture based on software agents. The server side manages secure connections and queries to heterogeneous remote databases and file systems containing patient personal and clinical data. The client side is a Java applet running in a web browser and providing a friendly medical user interface to perform queries on patient and medical test dat and integrate and visualize properly the various query results. A set of tools based on Java Advanced Imaging API enables to process and analyze the retrieved cardiology images, and quantify their features in different regions of interest. The platform-independence Java technology makes the developed prototype easy to be managed in a centralized form and provided in each site where an intranet or internet connection can be located. Giving the healthcare providers effective tools for querying, visualizing and evaluating comprehensively cardiology medical images and records in all locations where they can need them- i.e. emergency, operating theaters, ward, or even outpatient clinics- the developed prototype represents an important aid in providing more efficient diagnoses and medical treatments.

A soft kinetic data structure for lesion border detection.

PubMed

Kockara, Sinan; Mete, Mutlu; Yip, Vincent; Lee, Brendan; Aydin, Kemal

2010-06-15

The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach-graph spanner-for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented. Graph spanner approach is examined on a set of 100 dermoscopic images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates, false positives and false negatives along with true positives and true negatives are quantified by digitally comparing results with manually determined borders from a dermatologist. The results show that the highest precision and recall rates obtained to determine lesion boundaries are 100%. However, accuracy of assessment averages out at 97.72% and borders errors' mean is 2.28% for whole dataset.

Standardization efforts of digital pathology in Europe.

PubMed

Rojo, Marcial García; Daniel, Christel; Schrader, Thomas

2012-01-01

EURO-TELEPATH is a European COST Action IC0604. It started in 2007 and will end in November 2011. Its main objectives are evaluating and validating the common technological framework and communication standards required to access, transmit, and manage digital medical records by pathologists and other medical specialties in a networked environment. Working Group 1, "Business Modelling in Pathology," has designed main pathology processes - Frozen Study, Formalin Fixed Specimen Study, Telepathology, Cytology, and Autopsy - using Business Process Modelling Notation (BPMN). Working Group 2 has been dedicated to promoting the application of informatics standards in pathology, collaborating with Integrating Healthcare Enterprise (IHE), Digital Imaging and Communications in Medicine (DICOM), Health Level Seven (HL7), and other standardization bodies. Health terminology standardization research has become a topic of great interest. Future research work should focus on standardizing automatic image analysis and tissue microarrays imaging.

Enhanced ultrasound for advanced diagnostics, ultrasound tomography for volume limb imaging and prosthetic fitting

NASA Astrophysics Data System (ADS)

Anthony, Brian W.

2016-04-01

Ultrasound imaging methods hold the potential to deliver low-cost, high-resolution, operator-independent and nonionizing imaging systems - such systems couple appropriate algorithms with imaging devices and techniques. The increasing demands on general practitioners motivate us to develop more usable and productive diagnostic imaging equipment. Ultrasound, specifically freehand ultrasound, is a low cost and safe medical imaging technique. It doesn't expose a patient to ionizing radiation. Its safety and versatility make it very well suited for the increasing demands on general practitioners, or for providing improved medical care in rural regions or the developing world. However it typically suffers from sonographer variability; we will discuss techniques to address user variability. We also discuss our work to combine cylindrical scanning systems with state of the art inversion algorithms to deliver ultrasound systems for imaging and quantifying limbs in 3-D in vivo. Such systems have the potential to track the progression of limb health at a low cost and without radiation exposure, as well as, improve prosthetic socket fitting. Current methods of prosthetic socket fabrication remain subjective and ineffective at creating an interface to the human body that is both comfortable and functional. Though there has been recent success using methods like magnetic resonance imaging and biomechanical modeling, a low-cost, streamlined, and quantitative process for prosthetic cup design and fabrication has not been fully demonstrated. Medical ultrasonography may inform the design process of prosthetic sockets in a more objective manner. This keynote talk presents the results of progress in this area.

TU-C-18C-01: Medical Physics 1.0 to 2.0: Introduction and Panel Discussion

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

Samei, E; Pfeiffer, D; Frey, G

2014-06-15

Medical Physics 2.0, a new frontier in clinical imaging physics: Diagnostic imaging has always been a technological highlight of modern medicine. Imaging systems, with their ever-expanding advancement in terms of technology and application, increasingly require skilled expertise to understand the delicacy of their operation, monitor their performance, design their effective use, and ensure their overall quality and safety, scientifically and in quantitative terms. Physicists can play a crucial role in that process. But that role has largely remained a severely untapped resource. Many imaging centers fail to appreciate this potential, with medical physics groups either nonexistent or highly understaffed andmore » their services poorly integrated into the patient care process. As a field, we have yet to define and enact how the clinical physicist can engage as an active, effective, and integral member of the clinical team, and how the services that she/he provides can be financially accounted for. Physicists do and will always contribute to research and development. However, their indispensible contribution to clinical imaging operations is something that has not been adequately established. That, in conjunction with new realities of healthcare practice, indicates a growing need to establish an updated approach to clinical medical imaging physics. This presentation aims to describe a vision as how clinical imaging physics can expand beyond traditional insular models of inspection and acceptance testing, oriented toward compliance, towards team-based models of operational engagement addressing topics such as new non-classical challenges of new technologies, quantitative imaging, and operational optimization. The Medical Physics 2.0 paradigm extends clinical medical physics from isolated characterization of inherent properties of the equipment to effective use of the equipment and to retrospective evaluation of clinical performance. This is an existential transition of the field that speaks to the new paradigms of value-based and evidence-based medicine, comparative effectiveness, and meaningful use. The panel discussion that follows includes prominent practitioners, thinkers, and leaders that would lead the discussion on how Medical Physics 2.0 can be actualized. Topics of discussion will include the administrative, financial, regulatory, and accreditation requirements of the new paradigm, effective models of practice, and the steps that we need to take to make MP 2.0 a reality. Learning Objectives: To understand the new paradigm of clinical medical physics practice extending from traditional insular models of compliance towards teambased models of operational engagement. To understand how clinical physics can most effectively contribute to clinical care. Learn to identify strengths and weaknesses in studies designed to measure the effect of low doses of ionizing radiation To recognize the impediments to Medical Physics 2.0 paradigm.« less

42 CFR 37.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., Morgantown, WV 26505. (j) Preemployment physical examination means any medical examination which includes a... image acquisition systems that detect X-ray signals using a cassette-based photostimulable storage... radiographic image to electronic signals which are then processed and stored so they can be displayed. (2...

[Image processing applying in analysis of motion features of cultured cardiac myocyte in rat].

PubMed

Teng, Qizhi; He, Xiaohai; Luo, Daisheng; Wang, Zhengrong; Zhou, Beiyi; Yuan, Zhirun; Tao, Dachang

2007-02-01

Study of mechanism of medicine actions, by quantitative analysis of cultured cardiac myocyte, is one of the cutting edge researches in myocyte dynamics and molecular biology. The characteristics of cardiac myocyte auto-beating without external stimulation make the research sense. Research of the morphology and cardiac myocyte motion using image analysis can reveal the fundamental mechanism of medical actions, increase the accuracy of medicine filtering, and design the optimal formula of medicine for best medical treatments. A system of hardware and software has been built with complete sets of functions including living cardiac myocyte image acquisition, image processing, motion image analysis, and image recognition. In this paper, theories and approaches are introduced for analysis of living cardiac myocyte motion images and implementing quantitative analysis of cardiac myocyte features. A motion estimation algorithm is used for motion vector detection of particular points and amplitude and frequency detection of a cardiac myocyte. Beatings of cardiac myocytes are sometimes very small. In such case, it is difficult to detect the motion vectors from the particular points in a time sequence of images. For this reason, an image correlation theory is employed to detect the beating frequencies. Active contour algorithm in terms of energy function is proposed to approximate the boundary and detect the changes of edge of myocyte.

Applying industrial engineering practices to radiology.

PubMed

Rosen, Len

2004-01-01

Seven hospitals in Oregon and Washington have successfully adopted the Toyota Production System (TPS). Developed by Taiichi Ohno, TPS focuses on finding efficiencies and cost savings in manufacturing processes. A similar effort has occurred in Canada, where Toronto's Hospital for Sick Children has developed a database for its diagnostic imaging department built on the principles of TPS applied to patient encounters. Developed over the last 5 years, the database currently manages all interventional patient procedures for quality assurance, inventory, equipment, and labor. By applying industrial engineering methodology to manufacturing processes, it is possible to manage these constraints, eliminate the obstacles to achieving streamlined processes, and keep the cost of delivering products and services under control. Industrial engineering methodology has encouraged all stakeholders in manufacturing plants to become participants in dealing with constraints. It has empowered those on the shop floor as well as management to become partners in the change process. Using a manufacturing process model to organize patient procedures enables imaging department and imaging centers to generate reports that can help them understand utilization of labor, materials, equipment, and rooms. Administrators can determine the cost of individual procedures as well as the total and average cost of specific procedure types. When Toronto's Hospital for Sick Children first implemented industrial engineering methodology to medical imaging interventional radiology patient encounters, it focused on materials management. Early in the process, the return on investment became apparent as the department improved its management of more than 500,000 dollars of inventory. The calculated accumulated savings over 4 years for 10,000 interventional procedures alone amounted to more than 140,000 dollars. The medical imaging department in this hospital is only now beginning to apply what it has learned to other factors contributing to case cost. It has started to analyze its service contracts with equipment vendors. The department also is accumulating data to measure room, equipment, and labor utilization. The hospital now has a true picture of the real cost associated with each patient encounter in medical imaging. It can now begin to manage case costs, perform better capacity planning, create more effective relationships with its material suppliers, and optimize scheduling of patients and staff.

Secure annotation for medical images based on reversible watermarking in the Integer Fibonacci-Haar transform domain

NASA Astrophysics Data System (ADS)

Battisti, F.; Carli, M.; Neri, A.

2011-03-01

The increasing use of digital image-based applications is resulting in huge databases that are often difficult to use and prone to misuse and privacy concerns. These issues are especially crucial in medical applications. The most commonly adopted solution is the encryption of both the image and the patient data in separate files that are then linked. This practice results to be inefficient since, in order to retrieve patient data or analysis details, it is necessary to decrypt both files. In this contribution, an alternative solution for secure medical image annotation is presented. The proposed framework is based on the joint use of a key-dependent wavelet transform, the Integer Fibonacci-Haar transform, of a secure cryptographic scheme, and of a reversible watermarking scheme. The system allows: i) the insertion of the patient data into the encrypted image without requiring the knowledge of the original image, ii) the encryption of annotated images without causing loss in the embedded information, and iii) due to the complete reversibility of the process, it allows recovering the original image after the mark removal. Experimental results show the effectiveness of the proposed scheme.

An Ibm PC/AT-Based Image Acquisition And Processing System For Quantitative Image Analysis

NASA Astrophysics Data System (ADS)

Kim, Yongmin; Alexander, Thomas

1986-06-01

In recent years, a large number of applications have been developed for image processing systems in the area of biological imaging. We have already finished the development of a dedicated microcomputer-based image processing and analysis system for quantitative microscopy. The system's primary function has been to facilitate and ultimately automate quantitative image analysis tasks such as the measurement of cellular DNA contents. We have recognized from this development experience, and interaction with system users, biologists and technicians, that the increasingly widespread use of image processing systems, and the development and application of new techniques for utilizing the capabilities of such systems, would generate a need for some kind of inexpensive general purpose image acquisition and processing system specially tailored for the needs of the medical community. We are currently engaged in the development and testing of hardware and software for a fairly high-performance image processing computer system based on a popular personal computer. In this paper, we describe the design and development of this system. Biological image processing computer systems have now reached a level of hardware and software refinement where they could become convenient image analysis tools for biologists. The development of a general purpose image processing system for quantitative image analysis that is inexpensive, flexible, and easy-to-use represents a significant step towards making the microscopic digital image processing techniques more widely applicable not only in a research environment as a biologist's workstation, but also in clinical environments as a diagnostic tool.

The Medical Imaging Interaction Toolkit: challenges and advances : 10 years of open-source development.

PubMed

Nolden, Marco; Zelzer, Sascha; Seitel, Alexander; Wald, Diana; Müller, Michael; Franz, Alfred M; Maleike, Daniel; Fangerau, Markus; Baumhauer, Matthias; Maier-Hein, Lena; Maier-Hein, Klaus H; Meinzer, Hans-Peter; Wolf, Ivo

2013-07-01

The Medical Imaging Interaction Toolkit (MITK) has been available as open-source software for almost 10 years now. In this period the requirements of software systems in the medical image processing domain have become increasingly complex. The aim of this paper is to show how MITK evolved into a software system that is able to cover all steps of a clinical workflow including data retrieval, image analysis, diagnosis, treatment planning, intervention support, and treatment control. MITK provides modularization and extensibility on different levels. In addition to the original toolkit, a module system, micro services for small, system-wide features, a service-oriented architecture based on the Open Services Gateway initiative (OSGi) standard, and an extensible and configurable application framework allow MITK to be used, extended and deployed as needed. A refined software process was implemented to deliver high-quality software, ease the fulfillment of regulatory requirements, and enable teamwork in mixed-competence teams. MITK has been applied by a worldwide community and integrated into a variety of solutions, either at the toolkit level or as an application framework with custom extensions. The MITK Workbench has been released as a highly extensible and customizable end-user application. Optional support for tool tracking, image-guided therapy, diffusion imaging as well as various external packages (e.g. CTK, DCMTK, OpenCV, SOFA, Python) is available. MITK has also been used in several FDA/CE-certified applications, which demonstrates the high-quality software and rigorous development process. MITK provides a versatile platform with a high degree of modularization and interoperability and is well suited to meet the challenging tasks of today's and tomorrow's clinically motivated research.

Capturing and displaying microscopic images used in medical diagnostics and forensic science using 4K video resolution - an application in higher education.

PubMed

Maier, Hans; de Heer, Gert; Ortac, Ajda; Kuijten, Jan

2015-11-01

To analyze, interpret and evaluate microscopic images, used in medical diagnostics and forensic science, video images for educational purposes were made with a very high resolution of 4096 × 2160 pixels (4K), which is four times as many pixels as High-Definition Video (1920 × 1080 pixels). The unprecedented high resolution makes it possible to see details that remain invisible to any other video format. The images of the specimens (blood cells, tissue sections, hair, fibre, etc.) are recorded using a 4K video camera which is attached to a light microscope. After processing, this resulted in very sharp and highly detailed images. This material was then used in education for classroom discussion. Spoken explanation by experts in the field of medical diagnostics and forensic science was also added to the high-resolution video images to make it suitable for self-study. © 2015 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.

Standing on the shoulders of giants: improving medical image segmentation via bias correction.

PubMed

Wang, Hongzhi; Das, Sandhitsu; Pluta, John; Craige, Caryne; Altinay, Murat; Avants, Brian; Weiner, Michael; Mueller, Susanne; Yushkevich, Paul

2010-01-01

We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.

Noise reduction and image enhancement using a hardware implementation of artificial neural networks

NASA Astrophysics Data System (ADS)

David, Robert; Williams, Erin; de Tremiolles, Ghislain; Tannhof, Pascal

1999-03-01

In this paper, we present a neural based solution developed for noise reduction and image enhancement using the ZISC, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Artificial neural networks present the advantages of processing time reduction in comparison with classical models, adaptability, and the weighted property of pattern learning. The goal of the developed application is image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). Image results show a quantitative improvement over the noisy image as well as the efficiency of this system. Further enhancements are being examined to improve the output of the system.

Research of processes of reception and analysis of dynamic digital medical images in hardware/software complexes used for diagnostics and treatment of cardiovascular diseases

NASA Astrophysics Data System (ADS)

Karmazikov, Y. V.; Fainberg, E. M.

2005-06-01

Work with DICOM compatible equipment integrated into hardware and software systems for medical purposes has been considered. Structures of process of reception and translormation of the data are resulted by the example of digital rentgenography and angiography systems, included in hardware-software complex DIMOL-IK. Algorithms of reception and the analysis of the data are offered. Questions of the further processing and storage of the received data are considered.

A Method for Automatic Extracting Intracranial Region in MR Brain Image

NASA Astrophysics Data System (ADS)

Kurokawa, Keiji; Miura, Shin; Nishida, Makoto; Kageyama, Yoichi; Namura, Ikuro

It is well known that temporal lobe in MR brain image is in use for estimating the grade of Alzheimer-type dementia. It is difficult to use only region of temporal lobe for estimating the grade of Alzheimer-type dementia. From the standpoint for supporting the medical specialists, this paper proposes a data processing approach on the automatic extraction of the intracranial region from the MR brain image. The method is able to eliminate the cranium region with the laplacian histogram method and the brainstem with the feature points which are related to the observations given by a medical specialist. In order to examine the usefulness of the proposed approach, the percentage of the temporal lobe in the intracranial region was calculated. As a result, the percentage of temporal lobe in the intracranial region on the process of the grade was in agreement with the visual sense standards of temporal lobe atrophy given by the medical specialist. It became clear that intracranial region extracted by the proposed method was good for estimating the grade of Alzheimer-type dementia.

Local area networks in an imaging environment.

PubMed

Noz, M E; Maguire, G Q; Erdman, W A

1986-01-01

There is great interest at present in incorporating image-management systems popularly referred to as picture archiving and communication systems (PACS) into imaging departments. This paper will describe various aspects of local area networks (LANs) for medical images and will give a definition of terms and classification of devices by describing a possible system which links various digital image sources through a high-speed data link and a common image format, allows for viewing and processing of all images produced within the complex, and eliminates the transport of films. The status of standards governing LAN and particularly PACS systems along with a proposed image exchange format will be given. Prototype systems, particularly a system for nuclear medicine images, will be presented, as well as the prospects for the immediate future in terms of installations started and commercial products available. A survey of the many questions that arise in the development of a PACS for medical images and also a survey of the presently suggested/adopted answers will be given.

Medical revolution in Argentina.

PubMed

Ballarin, V L; Isoardi, R A

2010-01-01

The paper discusses the major Argentineans contributors, medical physicists and scientists, in medical imaging and the development of medical imaging in Argentina. The following are presented: history of medical imaging in Argentina: the pioneers; medical imaging and medical revolution; nuclear medicine imaging; ultrasound imaging; and mathematics, physics, and electronics in medical image research: a multidisciplinary endeavor.

32 CFR 161.6 - Procedures.

Code of Federal Regulations, 2014 CFR

2014-07-01

... photocopying of DoD ID cards to facilitate medical care processing, check cashing, voting, tax matters... support CAC issuance, which includes fingerprints and facial images specified in FIPS Publication 201-1... the Office of the USD(AT&L), implement the capability to obtain two segmented images (primary and...

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.

Achieving consistent color and grayscale presentation on medial color displays

NASA Astrophysics Data System (ADS)

Fan, Jiahua; Roehrig, Hans; Dallas, William; Krupinski, Elizabeth A.

2008-03-01

Color displays are increasingly used for medical imaging, replacing the traditional monochrome displays in radiology for multi-modality applications, 3D representation applications, etc. Color displays are also used increasingly because of wide spread application of Tele-Medicine, Tele-Dermatology and Digital Pathology. At this time, there is no concerted effort for calibration procedures for this diverse range of color displays in Telemedicine and in other areas of the medical field. Using a colorimeter to measure the display luminance and chrominance properties as well as some processing software we developed a first attempt to a color calibration protocol for the medical imaging field.

[Ethical reflection on multidisciplinarity and confidentiality of information in medical imaging through new information and communication technologies].

PubMed

Béranger, J; Le Coz, P

2012-05-01

Technological advances in medical imaging has resulted in the exponential increase of the number of images per examination, caused the irreversible decline of the silver film and imposed digital imaging. This digitization is a concept whose levels of development are multiple, reflecting the complexity of this process of technological change. Under these conditions, the use of medical information via new information and communication technologies is at the crossroads of several scientific approaches and several disciplines (medicine, ethics, law, economics, psychology, etc.) surrounding the information systems in health, doctor-patient relationship and concepts that are associated. Each day, these new information and communication technologies open up new horizons and the space of possibilities, spectacularly developing access to information and knowledge. In this perspective of digital technology emergence impacting the multidisciplinary use of health information systems, the ethical questions are numerous, especially on the preservation of privacy, confidentiality and security of medical data, and their accessibility and integrity. Copyright © 2012 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

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.

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.

Corpus callosum segmentation using deep neural networks with prior information from multi-atlas images

NASA Astrophysics Data System (ADS)

Park, Gilsoon; Hong, Jinwoo; Lee, Jong-Min

2018-03-01

In human brain, Corpus Callosum (CC) is the largest white matter structure, connecting between right and left hemispheres. Structural features such as shape and size of CC in midsagittal plane are of great significance for analyzing various neurological diseases, for example Alzheimer's disease, autism and epilepsy. For quantitative and qualitative studies of CC in brain MR images, robust segmentation of CC is important. In this paper, we present a novel method for CC segmentation. Our approach is based on deep neural networks and the prior information generated from multi-atlas images. Deep neural networks have recently shown good performance in various image processing field. Convolutional neural networks (CNN) have shown outstanding performance for classification and segmentation in medical image fields. We used convolutional neural networks for CC segmentation. Multi-atlas based segmentation model have been widely used in medical image segmentation because atlas has powerful information about the target structure we want to segment, consisting of MR images and corresponding manual segmentation of the target structure. We combined the prior information, such as location and intensity distribution of target structure (i.e. CC), made from multi-atlas images in CNN training process for more improving training. The CNN with prior information showed better segmentation performance than without.

[Security specifications for electronic medical records on the Internet].

PubMed

Mocanu, Mihai; Mocanu, Carmen

2007-01-01

The extension for the Web applications of the Electronic Medical Record seems both interesting and promising. Correlated with the expansion of Internet in our country, it allows the interconnection of physicians of different specialties and their collaboration for better treatment of patients. In this respect, the ophthalmologic medical applications consider the increased possibilities for monitoring chronic ocular diseases and for the identification of some elements for early diagnosis and risk factors supervision. We emphasize in this survey some possible solutions to the problems of interconnecting medical information systems to the Internet: the achievement of interoperability within medical organizations through the use of open standards, the automated input and processing for ocular imaging, the use of data reduction techniques in order to increase the speed of image retrieval in large databases, and, last but not least, the resolution of security and confidentiality problems in medical databases.

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.

Speckle noise removal applied to ultrasound image of carotid artery based on total least squares model.

PubMed

Yang, Lei; Lu, Jun; Dai, Ming; Ren, Li-Jie; Liu, Wei-Zong; Li, Zhen-Zhou; Gong, Xue-Hao

2016-10-06

An ultrasonic image speckle noise removal method by using total least squares model is proposed and applied onto images of cardiovascular structures such as the carotid artery. On the basis of the least squares principle, the related principle of minimum square method is applied to cardiac ultrasound image speckle noise removal process to establish the model of total least squares, orthogonal projection transformation processing is utilized for the output of the model, and the denoising processing for the cardiac ultrasound image speckle noise is realized. Experimental results show that the improved algorithm can greatly improve the resolution of the image, and meet the needs of clinical medical diagnosis and treatment of the cardiovascular system for the head and neck. Furthermore, the success in imaging of carotid arteries has strong implications in neurological complications such as stroke.

MR imaging of spinal infection.

PubMed

Tins, Bernhard J; Cassar-Pullicino, Victor N

2004-09-01

Magnetic resonance (MR) imaging plays a pivotal role in the diagnosis and management of spinal infection, enjoying a high sensitivity and specificity. A thorough understanding of spinal anatomy and the physicochemical pathological processes associated with infection is a desirable prerequisite allowing accurate interpretation of the disease process. Apart from confirmation of the disease, MR imaging is also best suited to excluding multifocal spinal involvement and the detection/exclusion of complications. It plays an essential role in the decision-making process concerning conservative versus surgical treatment and is also the best imaging method to monitor the effect of treatment. The MR features of infection confidently exclude tumor, degeneration, and so forth as the underlying process; differentiate pyogenic from granulomatous infections in most cases; and can suggest the rarer specific infective organisms. Copyright 2004 Thieme Medical Publishers, Inc.

A Methodology and Implementation for Annotating Digital Images for Context-appropriate Use in an Academic Health Care Environment

PubMed Central

Goede, Patricia A.; Lauman, Jason R.; Cochella, Christopher; Katzman, Gregory L.; Morton, David A.; Albertine, Kurt H.

2004-01-01

Use of digital medical images has become common over the last several years, coincident with the release of inexpensive, mega-pixel quality digital cameras and the transition to digital radiology operation by hospitals. One problem that clinicians, medical educators, and basic scientists encounter when handling images is the difficulty of using business and graphic arts commercial-off-the-shelf (COTS) software in multicontext authoring and interactive teaching environments. The authors investigated and developed software-supported methodologies to help clinicians, medical educators, and basic scientists become more efficient and effective in their digital imaging environments. The software that the authors developed provides the ability to annotate images based on a multispecialty methodology for annotation and visual knowledge representation. This annotation methodology is designed by consensus, with contributions from the authors and physicians, medical educators, and basic scientists in the Departments of Radiology, Neurobiology and Anatomy, Dermatology, and Ophthalmology at the University of Utah. The annotation methodology functions as a foundation for creating, using, reusing, and extending dynamic annotations in a context-appropriate, interactive digital environment. The annotation methodology supports the authoring process as well as output and presentation mechanisms. The annotation methodology is the foundation for a Windows implementation that allows annotated elements to be represented as structured eXtensible Markup Language and stored separate from the image(s). PMID:14527971

Design of a dataway processor for a parallel image signal processing system

NASA Astrophysics Data System (ADS)

Nomura, Mitsuru; Fujii, Tetsuro; Ono, Sadayasu

1995-04-01

Recently, demands for high-speed signal processing have been increasing especially in the field of image data compression, computer graphics, and medical imaging. To achieve sufficient power for real-time image processing, we have been developing parallel signal-processing systems. This paper describes a communication processor called 'dataway processor' designed for a new scalable parallel signal-processing system. The processor has six high-speed communication links (Dataways), a data-packet routing controller, a RISC CORE, and a DMA controller. Each communication link operates at 8-bit parallel in a full duplex mode at 50 MHz. Moreover, data routing, DMA, and CORE operations are processed in parallel. Therefore, sufficient throughput is available for high-speed digital video signals. The processor is designed in a top- down fashion using a CAD system called 'PARTHENON.' The hardware is fabricated using 0.5-micrometers CMOS technology, and its hardware is about 200 K gates.

MRI brain tumor segmentation based on improved fuzzy c-means method

NASA Astrophysics Data System (ADS)

Deng, Wankai; Xiao, Wei; Pan, Chao; Liu, Jianguo

2009-10-01

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

Thin client (web browser)-based collaboration for medical imaging and web-enabled data.

PubMed

Le, Tuong Huu; Malhi, Nadeem

2002-01-01

Utilizing thin client software and open source server technology, a collaborative architecture was implemented allowing for sharing of Digital Imaging and Communications in Medicine (DICOM) and non-DICOM images with real-time markup. Using the Web browser as a thin client integrated with standards-based components, such as DHTML (dynamic hypertext markup language), JavaScript, and Java, collaboration was achieved through a Web server/proxy server combination utilizing Java Servlets and Java Server Pages. A typical collaborative session involved the driver, who directed the navigation of the other collaborators, the passengers, and provided collaborative markups of medical and nonmedical images. The majority of processing was performed on the server side, allowing for the client to remain thin and more accessible.

Combined semantic and similarity search in medical image databases

NASA Astrophysics Data System (ADS)

Seifert, Sascha; Thoma, Marisa; Stegmaier, Florian; Hammon, Matthias; Kramer, Martin; Huber, Martin; Kriegel, Hans-Peter; Cavallaro, Alexander; Comaniciu, Dorin

2011-03-01

The current diagnostic process at hospitals is mainly based on reviewing and comparing images coming from multiple time points and modalities in order to monitor disease progression over a period of time. However, for ambiguous cases the radiologist deeply relies on reference literature or second opinion. Although there is a vast amount of acquired images stored in PACS systems which could be reused for decision support, these data sets suffer from weak search capabilities. Thus, we present a search methodology which enables the physician to fulfill intelligent search scenarios on medical image databases combining ontology-based semantic and appearance-based similarity search. It enabled the elimination of 12% of the top ten hits which would arise without taking the semantic context into account.

Color image enhancement of medical images using alpha-rooting and zonal alpha-rooting methods on 2D QDFT

NASA Astrophysics Data System (ADS)

Grigoryan, Artyom M.; John, Aparna; Agaian, Sos S.

2017-03-01

2-D quaternion discrete Fourier transform (2-D QDFT) is the Fourier transform applied to color images when the color images are considered in the quaternion space. The quaternion numbers are four dimensional hyper-complex numbers. Quaternion representation of color image allows us to see the color of the image as a single unit. In quaternion approach of color image enhancement, each color is seen as a vector. This permits us to see the merging effect of the color due to the combination of the primary colors. The color images are used to be processed by applying the respective algorithm onto each channels separately, and then, composing the color image from the processed channels. In this article, the alpha-rooting and zonal alpha-rooting methods are used with the 2-D QDFT. In the alpha-rooting method, the alpha-root of the transformed frequency values of the 2-D QDFT are determined before taking the inverse transform. In the zonal alpha-rooting method, the frequency spectrum of the 2-D QDFT is divided by different zones and the alpha-rooting is applied with different alpha values for different zones. The optimization of the choice of alpha values is done with the genetic algorithm. The visual perception of 3-D medical images is increased by changing the reference gray line.

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

PubMed Central

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-01-01

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists’ goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists—as opposed to a completely automatic computer interpretation—focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous—from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects—collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more—from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis. PMID:19175137

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

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

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-12-15

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities thatmore » are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists--as opposed to a completely automatic computer interpretation--focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous--from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects--collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more--from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.« less

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.

Level set segmentation of medical images based on local region statistics and maximum a posteriori probability.

PubMed

Cui, Wenchao; Wang, Yi; Lei, Tao; Fan, Yangyu; Feng, Yan

2013-01-01

This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

Chromosome Analysis

NASA Technical Reports Server (NTRS)

1998-01-01

Perceptive Scientific Instruments, Inc., provides the foundation for the Powergene line of chromosome analysis and molecular genetic instrumentation. This product employs image processing technology from NASA's Jet Propulsion Laboratory and image enhancement techniques from Johnson Space Center. Originally developed to send pictures back to earth from space probes, digital imaging techniques have been developed and refined for use in a variety of medical applications, including diagnosis of disease.

Evaluation of hybrids algorithms for mass detection in digitalized mammograms

NASA Astrophysics Data System (ADS)

Cordero, José; Garzón Reyes, Johnson

2011-01-01

The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.

Full image-processing pipeline in field-programmable gate array for a small endoscopic camera

NASA Astrophysics Data System (ADS)

Mostafa, Sheikh Shanawaz; Sousa, L. Natércia; Ferreira, Nuno Fábio; Sousa, Ricardo M.; Santos, Joao; Wäny, Martin; Morgado-Dias, F.

2017-01-01

Endoscopy is an imaging procedure used for diagnosis as well as for some surgical purposes. The camera used for the endoscopy should be small and able to produce a good quality image or video, to reduce discomfort of the patients, and to increase the efficiency of the medical team. To achieve these fundamental goals, a small endoscopy camera with a footprint of 1 mm×1 mm×1.65 mm is used. Due to the physical properties of the sensors and human vision system limitations, different image-processing algorithms, such as noise reduction, demosaicking, and gamma correction, among others, are needed to faithfully reproduce the image or video. A full image-processing pipeline is implemented using a field-programmable gate array (FPGA) to accomplish a high frame rate of 60 fps with minimum processing delay. Along with this, a viewer has also been developed to display and control the image-processing pipeline. The control and data transfer are done by a USB 3.0 end point in the computer. The full developed system achieves real-time processing of the image and fits in a Xilinx Spartan-6LX150 FPGA.

Research on interpolation methods in medical image processing.

PubMed

Pan, Mei-Sen; Yang, Xiao-Li; Tang, Jing-Tian

2012-04-01

Image interpolation is widely used for the field of medical image processing. In this paper, interpolation methods are divided into three groups: filter interpolation, ordinary interpolation and general partial volume interpolation. Some commonly-used filter methods for image interpolation are pioneered, but the interpolation effects need to be further improved. When analyzing and discussing ordinary interpolation, many asymmetrical kernel interpolation methods are proposed. Compared with symmetrical kernel ones, the former are have some advantages. After analyzing the partial volume and generalized partial volume estimation interpolations, the new concept and constraint conditions of the general partial volume interpolation are defined, and several new partial volume interpolation functions are derived. By performing the experiments of image scaling, rotation and self-registration, the interpolation methods mentioned in this paper are compared in the entropy, peak signal-to-noise ratio, cross entropy, normalized cross-correlation coefficient and running time. Among the filter interpolation methods, the median and B-spline filter interpolations have a relatively better interpolating performance. Among the ordinary interpolation methods, on the whole, the symmetrical cubic kernel interpolations demonstrate a strong advantage, especially the symmetrical cubic B-spline interpolation. However, we have to mention that they are very time-consuming and have lower time efficiency. As for the general partial volume interpolation methods, from the total error of image self-registration, the symmetrical interpolations provide certain superiority; but considering the processing efficiency, the asymmetrical interpolations are better.

The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning.

PubMed

Huff, Trevor J; Ludwig, Parker E; Zuniga, Jorge M

2018-05-01

3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition. The current challenges, potential solutions, and future directions for ML and 3D anatomical modeling in healthcare are discussed. Areas covered: This review covers research articles in the field of machine learning as related to 3D anatomical modeling. Topics discussed include automated image segmentation, cost reduction, and related time constraints. Expert commentary: ML-based segmentation of medical images could potentially improve the process of 3D anatomical modeling. However, until more research is done to validate these technologies in clinical practice, their impact on patient outcomes will remain unknown. We have the necessary computational tools to tackle the problems discussed. The difficulty now lies in our ability to collect sufficient data.

Quasi-real-time telemedical checkup system for x-ray examination of UGI tract based on high-speed network

NASA Astrophysics Data System (ADS)

Sakano, Toshikazu; Yamaguchi, Takahiro; Fujii, Tatsuya; Okumura, Akira; Furukawa, Isao; Ono, Sadayasu; Suzuki, Junji; Ando, Yutaka; Kohda, Ehiichi; Sugino, Yoshinori; Okada, Yoshiyuki; Amaki, Sachi

2000-05-01

We constructed a high-speed medical information network testbed, which is one of the largest testbeds in Japan, and applied it to practical medical checkups for the first time. The constructed testbed, which we call IMPACT, consists of a Super-High Definition Imaging system, a video conferencing system, a remote database system, and a 6 - 135 Mbps ATM network. The interconnected facilities include the School of Medicine in Keio University, a company's clinic, and an NTT R&D center, all in and around Tokyo. We applied IMPACT to the mass screening of the upper gastrointestinal (UGI) tract at the clinic. All 5419 radiographic images acquired at them clinic for 523 employees were digitized (2048 X 1698 X 12 bits) and transferred to a remote database in NTT. We then picked up about 50 images from five patients and sent them to nine radiological specialists at Keio University. The processing, which includes film digitization, image data transfer, and database registration, took 574 seconds per patient in average. The average reading time at Keio Univ. was 207 seconds. The overall processing time was estimated to be 781 seconds per patient. From these experimental results, we conclude that quasi-real time tele-medical checkups are possible with our prototype system.

Using a high-definition stereoscopic video system to teach microscopic surgery

NASA Astrophysics Data System (ADS)

Ilgner, Justus; Park, Jonas Jae-Hyun; Labbé, Daniel; Westhofen, Martin

2007-02-01

Introduction: While there is an increasing demand for minimally invasive operative techniques in Ear, Nose and Throat surgery, these operations are difficult to learn for junior doctors and demanding to supervise for experienced surgeons. The motivation for this study was to integrate high-definition (HD) stereoscopic video monitoring in microscopic surgery in order to facilitate teaching interaction between senior and junior surgeon. Material and methods: We attached a 1280x1024 HD stereo camera (TrueVisionSystems TM Inc., Santa Barbara, CA, USA) to an operating microscope (Zeiss ProMagis, Zeiss Co., Oberkochen, Germany), whose images were processed online by a PC workstation consisting of a dual Intel® Xeon® CPU (Intel Co., Santa Clara, CA). The live image was displayed by two LCD projectors @ 1280x768 pixels on a 1,25m rear-projection screen by polarized filters. While the junior surgeon performed the surgical procedure based on the displayed stereoscopic image, all other participants (senior surgeon, nurse and medical students) shared the same stereoscopic image from the screen. Results: With the basic setup being performed only once on the day before surgery, fine adjustments required about 10 minutes extra during the operation schedule, which fitted into the time interval between patients and thus did not prolong operation times. As all relevant features of the operative field were demonstrated on one large screen, four major effects were obtained: A) Stereoscopy facilitated orientation for the junior surgeon as well as for medical students. B) The stereoscopic image served as an unequivocal guide for the senior surgeon to demonstrate the next surgical steps to the junior colleague. C) The theatre nurse shared the same image, anticipating the next instruments which were needed. D) Medical students instantly share the information given by all staff and the image, thus avoiding the need for an extra teaching session. Conclusion: High definition stereoscopy bears the potential to compress the learning curve for undergraduate as well as postgraduate medical professionals in minimally invasive surgery. Further studies will focus on the long term effect for operative training as well as on post-processing of HD stereoscopy video content for off-line interactive medical education.

Medical Image Retrieval Using Multi-Texton Assignment.

PubMed

Tang, Qiling; Yang, Jirong; Xia, Xianfu

2018-02-01

In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.

A new method for the automatic retrieval of medical cases based on the RadLex ontology.

PubMed

Spanier, A B; Cohen, D; Joskowicz, L

2017-03-01

The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems. We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy-Pathology List. It uses a new case relatedness metric [Formula: see text] that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case. An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods. The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.

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.

From plastic to gold: a unified classification scheme for reference standards in medical image processing

NASA Astrophysics Data System (ADS)

Lehmann, Thomas M.

2002-05-01

Reliable evaluation of medical image processing is of major importance for routine applications. Nonetheless, evaluation is often omitted or methodically defective when novel approaches or algorithms are introduced. Adopted from medical diagnosis, we define the following criteria to classify reference standards: 1. Reliance, if the generation or capturing of test images for evaluation follows an exactly determined and reproducible protocol. 2. Equivalence, if the image material or relationships considered within an algorithmic reference standard equal real-life data with respect to structure, noise, or other parameters of importance. 3. Independence, if any reference standard relies on a different procedure than that to be evaluated, or on other images or image modalities than that used routinely. This criterion bans the simultaneous use of one image for both, training and test phase. 4. Relevance, if the algorithm to be evaluated is self-reproducible. If random parameters or optimization strategies are applied, reliability of the algorithm must be shown before the reference standard is applied for evaluation. 5. Significance, if the number of reference standard images that are used for evaluation is sufficient large to enable statistically founded analysis. We demand that a true gold standard must satisfy the Criteria 1 to 3. Any standard only satisfying two criteria, i.e., Criterion 1 and Criterion 2 or Criterion 1 and Criterion 3, is referred to as silver standard. Other standards are termed to be from plastic. Before exhaustive evaluation based on gold or silver standards is performed, its relevance must be shown (Criterion 4) and sufficient tests must be carried out to found statistical analysis (Criterion 5). In this paper, examples are given for each class of reference standards.

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.

Evalution of a DE-Identification Process for Ocular Imaging

NASA Technical Reports Server (NTRS)

LaPelusa, Michael B.; Mason, Sara S.; Taiym, Wafa F.; Sargsyan, Ashot; Lee, Lesley R.; Wear, Mary L.; Van Baalen, Mary

2015-01-01

Medical privacy of NASA astronauts requires an organized and comprehensive approach when data are being made available outside NASA systems. A combination of factors, including the uniquely small patient population, the extensive medical testing done on these individuals, and the relative cultural popularity of the astronauts puts them at a far greater risk to potential exposure of personal information than the general public. Therefore, care must be taken to ensure that the astronauts' identities are concealed. Magnetic Resonance Imaging (MRI) medical data is a recent source of interest to researchers concerned with the development of Visual Impairment due to Intracranial Pressure (VIIP) in the astronaut population. Each vision MRI scan of an astronaut includes 176 separate sagittal images that are saved as an "image series" for clinical use. In addition to the medical information these image sets provide, they also inherently contain a substantial amount of non-medical personally identifiable information (PII) such as-name, date of birth, and date of exam. We have shown that an image set of this type can be rendered, using free software, to give an accurate representation of the patient's face. This currently restricts NASA from dispensing MRI data to researchers in a deidentified format. Automated software programs, such as the Brain Extraction Tool, are available to researchers who wish to de-identify MRI sagittal brain images by "erasing" identifying characteristics such as the nose and jaw on the image sets. However, this software is not useful to NASA for vision research because it removes the portion of the images around the eye orbits, which is the main area of interest to researchers studying the VIIP syndrome. The Lifetime Surveillance of Astronaut Health program has resolved this issue by developing a protocol to de-identify MRI sagittal brain images using Showcase Premier, a DICOM (Digital Imaging and Communications in Medicine) software package. The software allows manual editing of one image from a patient's image set to be automatically applied to the entire image series. This new approach would allow a new level of access to untapped medical imaging data relating to VIIP that can be utilized by researchers while protecting the privacy of the astronauts. In the next step toward finalizing this technique, NASA clinical radiology consultants will test the images to verify removal of all metadata and PII.

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 ).

Systematic procedures to promote U.S. HIV medication adherence via Photovoice.

PubMed

Teti, Michelle; Shaffer, Victoria; Majee, Wilson; Farnan, Rose; Gerkovich, Mary

2017-06-21

Medication adherence is essential to promote the health of people living with HIV (PL-HIV) and prevent HIV transmission in the U.S. Novel medication health promotion interventions are needed that address patient-centeredness, understandability, and communication with providers. The aims of this article are to define the systematic stages we used to develop an effective health promotion intervention via the products (e.g. images and stories) of Photovoice. We designed an intervention to improve HIV adherence knowledge, attitudes, and communication with providers through Photovoice. 16 PL-HIV used Photovoice strategies to describe their experiences with medication via images and captions and create an intervention (10 adherence promotion posters) that integrated photo-stories of their adherence motivators, journeys from sickness to health, and how they manage and counter HIV stigma. We outline the systematic process we used to adapt Photovoice to create the effective intervention for replication. The process included six stages: (i) identify scope of the project; (ii) create collaborative project team; (iii) design project materials; (iv) review and revise materials with team members; (v) disseminate materials; and (vi) evaluate materials. Photovoice is used traditionally as a social action research method. In this project, it was adapted to create patient-driven images and stories for health promotion posters. Poster viewers experienced improved self-efficacy for HIV medication adherence. Describing the adaptation of the Photovoice process in a deliberate and transparent way can support fidelity to the essence of the participant-driven method, while also allowing researchers and practitioners to replicate Photovoice as a successful health promotion intervention. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices.

PubMed

Filho, Mercedes; Ma, Zhen; Tavares, João Manuel R S

2015-11-01

In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

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.

Acquisition and review of diagnostic images for use in medical research and medical testing examinations via the Internet

NASA Astrophysics Data System (ADS)

Pauley, Mark A.; Dalrymple, Glenn V.; Zhu, Quiming; Chu, Wei-Kom

2000-12-01

With the continued centralization of medical care into large, regional centers, there is a growing need for a flexible, inexpensive, and secure system to rapidly provide referring physicians in the field with the results of the sophisticated medical tests performed at these facilities. Furthermore, the medical community has long recognized the need for a system with similar characteristics to maintain and upgrade patient case sets for oral and written student examinations. With the move toward filmless radiographic instrumentation, the widespread and growing use of digital methods and the Internet, both of these processes can now be realized. This article describes the conceptual development and testing of a protocol that allow users to transmit, modify, remotely store and display the images and textual information of medical cases via the Internet. We also discuss some of the legal issues we encountered regarding the transmission of medical information; these issues have had a direct impact on the implementation of the results of this project.

Cardiovascular imaging and image processing: Theory and practice - 1975; Proceedings of the Conference, Stanford University, Stanford, Calif., July 10-12, 1975

NASA Technical Reports Server (NTRS)

Harrison, D. C.; Sandler, H.; Miller, H. A.

1975-01-01

The present collection of papers outlines advances in ultrasonography, scintigraphy, and commercialization of medical technology as applied to cardiovascular diagnosis in research and clinical practice. Particular attention is given to instrumentation, image processing and display. As necessary concomitants to mathematical analysis, recently improved magnetic recording methods using tape or disks and high-speed computers of large capacity are coming into use. Major topics include Doppler ultrasonic techniques, high-speed cineradiography, three-dimensional imaging of the myocardium with isotopes, sector-scanning echocardiography, and commercialization of the echocardioscope. Individual items are announced in this issue.

Region of interest and windowing-based progressive medical image delivery using JPEG2000

NASA Astrophysics Data System (ADS)

Nagaraj, Nithin; Mukhopadhyay, Sudipta; Wheeler, Frederick W.; Avila, Ricardo S.

2003-05-01

An important telemedicine application is the perusal of CT scans (digital format) from a central server housed in a healthcare enterprise across a bandwidth constrained network by radiologists situated at remote locations for medical diagnostic purposes. It is generally expected that a viewing station respond to an image request by displaying the image within 1-2 seconds. Owing to limited bandwidth, it may not be possible to deliver the complete image in such a short period of time with traditional techniques. In this paper, we investigate progressive image delivery solutions by using JPEG 2000. An estimate of the time taken in different network bandwidths is performed to compare their relative merits. We further make use of the fact that most medical images are 12-16 bits, but would ultimately be converted to an 8-bit image via windowing for display on the monitor. We propose a windowing progressive RoI technique to exploit this and investigate JPEG 2000 RoI based compression after applying a favorite or a default window setting on the original image. Subsequent requests for different RoIs and window settings would then be processed at the server. For the windowing progressive RoI mode, we report a 50% reduction in transmission time.

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.

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.

The Handbook of Medical Image Perception and Techniques

NASA Astrophysics Data System (ADS)

Samei, Ehsan; Krupinski, Elizabeth

2014-07-01

1. Medical image perception Ehsan Samei and Elizabeth Krupinski; Part I. Historical Reflections and Theoretical Foundations: 2. A short history of image perception in medical radiology Harold Kundel and Calvin Nodine; 3. Spatial vision research without noise Arthur Burgess; 4. Signal detection theory, a brief history Arthur Burgess; 5. Signal detection in radiology Arthur Burgess; 6. Lessons from dinners with the giants of modern image science Robert Wagner; Part II. Science of Image Perception: 7. Perceptual factors in reading medical images Elizabeth Krupinski; 8. Cognitive factors in reading medical images David Manning; 9. Satisfaction of search in traditional radiographic imaging Kevin Berbaum, Edmund Franken, Robert Caldwell and Kevin Schartz; 10. The role of expertise in radiologic image interpretation Calvin Nodine and Claudia Mello-Thoms; 11. A primer of image quality and its perceptual relevance Robert Saunders and Ehsan Samei; 12. Beyond the limitations of human vision Maria Petrou; Part III. Perception Metrology: 13. Logistical issues in designing perception experiments Ehsan Samei and Xiang Li; 14. ROC analysis: basic concepts and practical applications Georgia Tourassi; 15. Multi-reader ROC Steve Hillis; 16. Recent developments in FROC methodology Dev Chakraborty; 17. Observer models as a surrogate to perception experiments Craig Abbey and Miguel Eckstein; 18. Implementation of observer models Matthew Kupinski; Part IV. Decision Support and Computer Aided Detection: 19. CAD: an image perception perspective Maryellen Giger and Weijie Chen; 20. Common designs of CAD studies Yulei Jiang; 21. Perceptual effect of CAD in reading chest images Matthew Freedman and Teresa Osicka; 22. Perceptual issues in mammography and CAD Michael Ulissey; 23. How perceptual factors affect the use and accuracy of CAD for interpretation of CT images Ronald Summers; 24. CAD: risks and benefits for radiologists' decisions Eugenio Alberdi, Andrey Povyakalo, Lorenzo Strigini and Peter Ayton; Part V. Optimization and Practical Issues: 25. Optimization of 2D and 3D radiographic systems Jeff Siewerdson; 26. Applications of AFC methodology in optimization of CT imaging systems Kent Ogden and Walter Huda; 27. Perceptual issues in reading mammograms Margarita Zuley; 28. Perceptual optimization of display processing techniques Richard Van Metter; 29. Optimization of display systems Elizabeth Krupinski and Hans Roehrig; 30. Ergonomic radiologist workplaces in the PACS environment Carl Zylack; Part VI. Epilogue: 31. Future prospects of medical image perception Ehsan Samei and Elizabeth Krupinski; Index.

The Handbook of Medical Image Perception and Techniques

NASA Astrophysics Data System (ADS)

Samei, Ehsan; Krupinski, Elizabeth

2009-12-01

1. Medical image perception Ehsan Samei and Elizabeth Krupinski; Part I. Historical Reflections and Theoretical Foundations: 2. A short history of image perception in medical radiology Harold Kundel and Calvin Nodine; 3. Spatial vision research without noise Arthur Burgess; 4. Signal detection theory, a brief history Arthur Burgess; 5. Signal detection in radiology Arthur Burgess; 6. Lessons from dinners with the giants of modern image science Robert Wagner; Part II. Science of Image Perception: 7. Perceptual factors in reading medical images Elizabeth Krupinski; 8. Cognitive factors in reading medical images David Manning; 9. Satisfaction of search in traditional radiographic imaging Kevin Berbaum, Edmund Franken, Robert Caldwell and Kevin Schartz; 10. The role of expertise in radiologic image interpretation Calvin Nodine and Claudia Mello-Thoms; 11. A primer of image quality and its perceptual relevance Robert Saunders and Ehsan Samei; 12. Beyond the limitations of human vision Maria Petrou; Part III. Perception Metrology: 13. Logistical issues in designing perception experiments Ehsan Samei and Xiang Li; 14. ROC analysis: basic concepts and practical applications Georgia Tourassi; 15. Multi-reader ROC Steve Hillis; 16. Recent developments in FROC methodology Dev Chakraborty; 17. Observer models as a surrogate to perception experiments Craig Abbey and Miguel Eckstein; 18. Implementation of observer models Matthew Kupinski; Part IV. Decision Support and Computer Aided Detection: 19. CAD: an image perception perspective Maryellen Giger and Weijie Chen; 20. Common designs of CAD studies Yulei Jiang; 21. Perceptual effect of CAD in reading chest images Matthew Freedman and Teresa Osicka; 22. Perceptual issues in mammography and CAD Michael Ulissey; 23. How perceptual factors affect the use and accuracy of CAD for interpretation of CT images Ronald Summers; 24. CAD: risks and benefits for radiologists' decisions Eugenio Alberdi, Andrey Povyakalo, Lorenzo Strigini and Peter Ayton; Part V. Optimization and Practical Issues: 25. Optimization of 2D and 3D radiographic systems Jeff Siewerdson; 26. Applications of AFC methodology in optimization of CT imaging systems Kent Ogden and Walter Huda; 27. Perceptual issues in reading mammograms Margarita Zuley; 28. Perceptual optimization of display processing techniques Richard Van Metter; 29. Optimization of display systems Elizabeth Krupinski and Hans Roehrig; 30. Ergonomic radiologist workplaces in the PACS environment Carl Zylack; Part VI. Epilogue: 31. Future prospects of medical image perception Ehsan Samei and Elizabeth Krupinski; Index.

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.

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.

Medical technology integration: CT, angiography, imaging-capable OR-table, navigation and robotics in a multifunctional sterile suite.

PubMed

Jacob, A L; Regazzoni, P; Bilecen, D; Rasmus, M; Huegli, R W; Messmer, P

2007-01-01

Technology integration is an enabling technological prerequisite to achieve a major breakthrough in sophisticated intra-operative imaging, navigation and robotics in minimally invasive and/or emergency diagnosis and therapy. Without a high degree of integration and reliability comparable to that achieved in the aircraft industry image guidance in its different facets will not ultimately succeed. As of today technology integration in the field of image-guidance is close to nonexistent. Technology integration requires inter-departmental integration of human and financial resources and of medical processes in a dialectic way. This expanded techno-socio-economic integration has profound consequences for the administration and working conditions in hospitals. At the university hospital of Basel, Switzerland, a multimodality multifunction sterile suite was put into operation after a substantial pre-run. We report the lessons learned during our venture into the world of medical technology integration and describe new possibilities for similar integration projects in the future.

[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.

Hyperspectral venous image quality assessment for optimum illumination range selection based on skin tone characteristics

PubMed Central

2014-01-01

Background Subcutaneous veins localization is usually performed manually by medical staff to find suitable vein to insert catheter for medication delivery or blood sample function. The rule of thumb is to find large and straight enough vein for the medication to flow inside of the selected blood vessel without any obstruction. The problem of peripheral difficult venous access arises when patient’s veins are not visible due to any reason like dark skin tone, presence of hair, high body fat or dehydrated condition, etc. Methods To enhance the visibility of veins, near infrared imaging systems is used to assist medical staff in veins localization process. Optimum illumination is crucial to obtain a better image contrast and quality, taking into consideration the limited power and space on portable imaging systems. In this work a hyperspectral image quality assessment is done to get the optimum range of illumination for venous imaging system. A database of hyperspectral images from 80 subjects has been created and subjects were divided in to four different classes on the basis of their skin tone. In this paper the results of hyper spectral image analyses are presented in function of the skin tone of patients. For each patient, four mean images were constructed by taking mean with a spectral span of 50 nm within near infrared range, i.e. 750–950 nm. Statistical quality measures were used to analyse these images. Conclusion It is concluded that the wavelength range of 800 to 850 nm serve as the optimum illumination range to get best near infrared venous image quality for each type of skin tone. PMID:25087016

Cultivating Medical Intentionality: The Phenomenology of Diagnostic Virtuosity in East Asian Medicine.

PubMed

Kim, Taewoo

2017-03-01

This study examines the perceptual basis of diagnostic virtuosity in East Asian medicine, combining Merleau-Ponty's phenomenology and an ethnographic investigation of Korean medicine in South Korea. A novice, being exposed to numerous clinical transactions during apprenticeship, organizes perceptual experience that occurs between him or herself and patients. In the process, the fledgling practitioner's body begins to set up a medically-tinged "intentionality" interconnecting his or her consciousness and medically significant qualities in patients. Diagnostic virtuosity is gained when the practitioner embodies a cultivated medical intentionality. In the process of becoming a practitioner imbued with virtuosity, this study focuses on the East Asian notion of "Image" that maximizes the body's perceptual capacity, and minimizes possible reductions by linguistic re-presentation. "Image" enables the practitioner to somatically conceptualize the core notions of East Asian medicine, such as Yin-Yang, and to use them as an embodied litmus as the practitioner's cultivated body instinctively conjures up medical notions at clinical encounters. In line with anthropological critiques of reductionist frameworks that congeal human existential and perceptual vitality within a "scientific" explanatory model, this article attempts to provide an example of various knowing and caring practices, institutionalized external to the culture of science.

Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Choi, Sunghoon; Kim, Hee-Joung

2018-03-01

When processing medical images, image denoising is an important pre-processing step. Various image denoising algorithms have been developed in the past few decades. Recently, image denoising using the deep learning method has shown excellent performance compared to conventional image denoising algorithms. In this study, we introduce an image denoising technique based on a convolutional denoising autoencoder (CDAE) and evaluate clinical applications by comparing existing image denoising algorithms. We train the proposed CDAE model using 3000 chest radiograms training data. To evaluate the performance of the developed CDAE model, we compare it with conventional denoising algorithms including median filter, total variation (TV) minimization, and non-local mean (NLM) algorithms. Furthermore, to verify the clinical effectiveness of the developed denoising model with CDAE, we investigate the performance of the developed denoising algorithm on chest radiograms acquired from real patients. The results demonstrate that the proposed denoising algorithm developed using CDAE achieves a superior noise-reduction effect in chest radiograms compared to TV minimization and NLM algorithms, which are state-of-the-art algorithms for image noise reduction. For example, the peak signal-to-noise ratio and structure similarity index measure of CDAE were at least 10% higher compared to conventional denoising algorithms. In conclusion, the image denoising algorithm developed using CDAE effectively eliminated noise without loss of information on anatomical structures in chest radiograms. It is expected that the proposed denoising algorithm developed using CDAE will be effective for medical images with microscopic anatomical structures, such as terminal bronchioles.

Meeting the challenges of the digital medical enterprise of the future by reusing enterprise software components

NASA Astrophysics Data System (ADS)

Shani, Uri; Kol, Tomer; Shachor, Gal

2004-04-01

Managing medical digital information objects, and in particular medical images is an enterprise-grade problem. Firstly, there is the sheer amount of digital data that is generated in the proliferation of digital (and film-free) medical imaging. Secondly, the managing software ought to enjoy high availability, recoverability and manageability that are found only in the most business-critical systems. Indeed, such requirements are borrowed from the business enterprise world. Moreover, the solution for the medical information management problem should too employ the same software tools, middlewares and architectures. It is safe to say that all first-line medical PACS products strive to provide a solution for all these challenging requirements. The DICOM standard has been a prime enabler of such solutions. DICOM created the interconnectivity, which made it possible for a PACS service to manage millions of exams consisting of trillions of images. With the more comprehensive IHE architecture, the enterprise is expanded into a multi-facility regional conglomerate, which presents extreme demands from the data management system. HIPPA legislations add considerable challenges per security, privacy and other legal issues, which aggravate the situation. In this paper, we firstly present what in our view should be the general requirements for a first-line medical PACS, taken from an enterprise medical imaging storage and management solution perspective. While these requirements can be met by homegrown implementations, we suggest looking at the existing technologies, which have emerged in the recent years to meet exactly these challenges in the business world. We present an evolutionary process, which led to the design and implementation of a medical object management subsystem. This is indeed an enterprise medical imaging solution that is built upon respective technological components. The system answers all these challenges simply by not reinventing wheels, but rather reusing the best "wheels" for the job. Relying on such middleware components allowed us to concentrate on added value for this specific problem domain.

Exploring the Validity of Assessment in Anatomy: Do Images Influence Cognitive Processes Used in Answering Extended Matching Questions?

ERIC Educational Resources Information Center

Vorstenbosch, Marc A. T. M.; Bouter, Shifra T.; van den Hurk, Marianne M.; Kooloos, Jan G. M.; Bolhuis, Sanneke M.; Laan, Roland F. J. M.

2014-01-01

Assessment is an important aspect of medical education because it tests students' competence and motivates them to study. Various assessment methods, with and without images, are used in the study of anatomy. In this study, we investigated the use of extended matching questions (EMQs). To gain insight into the influence of images on the…

Referral criteria and clinical decision support: radiological protection aspects for justification.

PubMed

Pérez, M del Rosario

2015-06-01

Advanced imaging technology has opened new horizons for medical diagnostics and improved patient care. However, many procedures are unjustified and do not provide a net benefit. An area of particular concern is the unnecessary use of radiation when clinical evaluation or other imaging modalities could provide an accurate diagnosis. Referral criteria for medical imaging are consensus statements based on the best-available evidence to assist the decision-making process when choosing the best imaging procedure for a given patient. Although they are advisory rather than compulsory, physicians should have good reasons for deviation from these criteria. Voluntary use of referral criteria has shown limited success compared with integration into clinical decision support systems. These systems support good medical practice, can improve health service delivery, and foster safer, more efficient, fair, cost-effective care, thus contributing to the strengthening of health systems. Justification of procedures and optimisation of protection, the two pillars of radiological protection in health care, are implicit in the notion of good medical practice. However, some health professionals are not familiar with these principles, and have low awareness of radiological protection aspects of justification. A stronger collaboration between radiation protection and healthcare communities could contribute to improve the radiation protection culture in medical practice. © The Chartered Institution of Building Services Engineers 2014.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

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

NONE

2011-02-15

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.more » Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (''nodule{>=}3 mm,''''nodule<3 mm,'' and ''non-nodule{>=}3 mm''). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked ''nodule'' by at least one radiologist. 2669 of these lesions were marked ''nodule{>=}3 mm'' by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.« less

The Imaging and Medical Beam Line at the Australian Synchrotron

NASA Astrophysics Data System (ADS)

Hausermann, Daniel; Hall, Chris; Maksimenko, Anton; Campbell, Colin

2010-07-01

As a result of the enthusiastic support from the Australian biomedical, medical and clinical communities, the Australian Synchrotron is constructing a world-class facility for medical research, the `Imaging and Medical Beamline'. The IMBL began phased commissioning in late 2008 and is scheduled to commence the first clinical research programs with patients in 2011. It will provide unrivalled x-ray facilities for imaging and radiotherapy for a wide range of research applications in diseases, treatments and understanding of physiological processes. The main clinical research drivers are currently high resolution and sensitivity cardiac and breast imaging, cell tracking applied to regenerative and stem cell medicine and cancer therapies. The beam line has a maximum source to sample distance of 136 m and will deliver a 60 cm by 4 cm x-ray beam1—monochromatic and white—to a three storey satellite building fully equipped for pre-clinical and clinical research. Currently operating with a 1.4 Tesla multi-pole wiggler, it will upgrade to a 4.2 Tesla device which requires the ability to handle up to 21 kW of x-ray power at any point along the beam line. The applications envisaged for this facility include imaging thick objects encompassing materials, humans and animals. Imaging can be performed in the range 15-150 keV. Radiotherapy research typically requires energies between 30 and 120 keV, for both monochromatic and broad beam.

A Neuroimaging Web Services Interface as a Cyber Physical System for Medical Imaging and Data Management in Brain Research: Design Study

PubMed Central

2018-01-01

Background Structural and functional brain images are essential imaging modalities for medical experts to study brain anatomy. These images are typically visually inspected by experts. To analyze images without any bias, they must be first converted to numeric values. Many software packages are available to process the images, but they are complex and difficult to use. The software packages are also hardware intensive. The results obtained after processing vary depending on the native operating system used and its associated software libraries; data processed in one system cannot typically be combined with data on another system. Objective The aim of this study was to fulfill the neuroimaging community’s need for a common platform to store, process, explore, and visualize their neuroimaging data and results using Neuroimaging Web Services Interface: a series of processing pipelines designed as a cyber physical system for neuroimaging and clinical data in brain research. Methods Neuroimaging Web Services Interface accepts magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and functional magnetic resonance imaging. These images are processed using existing and custom software packages. The output is then stored as image files, tabulated files, and MySQL tables. The system, made up of a series of interconnected servers, is password-protected and is securely accessible through a Web interface and allows (1) visualization of results and (2) downloading of tabulated data. Results All results were obtained using our processing servers in order to maintain data validity and consistency. The design is responsive and scalable. The processing pipeline started from a FreeSurfer reconstruction of Structural magnetic resonance imaging images. The FreeSurfer and regional standardized uptake value ratio calculations were validated using Alzheimer’s Disease Neuroimaging Initiative input images, and the results were posted at the Laboratory of Neuro Imaging data archive. Notable leading researchers in the field of Alzheimer’s Disease and epilepsy have used the interface to access and process the data and visualize the results. Tabulated results with unique visualization mechanisms help guide more informed diagnosis and expert rating, providing a truly unique multimodal imaging platform that combines magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and resting state functional magnetic resonance imaging. A quality control component was reinforced through expert visual rating involving at least 2 experts. Conclusions To our knowledge, there is no validated Web-based system offering all the services that Neuroimaging Web Services Interface offers. The intent of Neuroimaging Web Services Interface is to create a tool for clinicians and researchers with keen interest on multimodal neuroimaging. More importantly, Neuroimaging Web Services Interface significantly augments the Alzheimer’s Disease Neuroimaging Initiative data, especially since our data contain a large cohort of Hispanic normal controls and Alzheimer’s Disease patients. The obtained results could be scrutinized visually or through the tabulated forms, informing researchers on subtle changes that characterize the different stages of the disease. PMID:29699962

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.

Development of national competency-based learning objectives "Medical Informatics" for undergraduate medical education.

PubMed

Röhrig, R; Stausberg, J; Dugas, M

2013-01-01

The aim of this project is to develop a catalogue of competency-based learning objectives "Medical Informatics" for undergraduate medical education (abbreviated NKLM-MI in German). The development followed a multi-level annotation and consensus process. For each learning objective a reason why a physician needs this competence was required. In addition, each objective was categorized according to the competence context (A = covered by medical informatics, B = core subject of medical informatics, C = optional subject of medical informatics), the competence level (1 = referenced knowledge, 2 = applied knowledge, 3 = routine knowledge) and a CanMEDS competence role (medical expert, communicator, collaborator, manager, health advocate, professional, scholar). Overall 42 objectives in seven areas (medical documentation and information processing, medical classifications and terminologies, information systems in healthcare, health telematics and telemedicine, data protection and security, access to medical knowledge and medical signal-/image processing) were identified, defined and consented. With the NKLM-MI the competences in the field of medical informatics vital to a first year resident physician are identified, defined and operationalized. These competencies are consistent with the recommendations of the International Medical Informatics Association (IMIA). The NKLM-MI will be submitted to the National Competence-Based Learning Objectives for Undergraduate Medical Education. The next step is implementation of these objectives by the faculties.

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

PubMed

Kohli, Marc D; Summers, Ronald M; Geis, J Raymond

2017-08-01

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.

Remote sensing, imaging, and signal engineering

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

Brase, J.M.

1993-03-01

This report discusses the Remote Sensing, Imaging, and Signal Engineering (RISE) trust area which has been very active in working to define new directions. Signal and image processing have always been important support for existing programs at Lawrence Livermore National Laboratory (LLNL), but now these technologies are becoming central to the formation of new programs. Exciting new applications such as high-resolution telescopes, radar remote sensing, and advanced medical imaging are allowing us to participate in the development of new programs.

Myositis and Fasciitis: Role of Imaging.

PubMed

Endo, Yoshimi; Miller, Theodore T

2018-07-01

Imaging plays an important role in the evaluation of patients presenting with possible myositis, with magnetic resonance imaging the most appropriate modality but ultrasound also playing a complementary role. This article reviews the imaging appearance of the inflammatory myopathies, other forms of myositis, and mimickers of myositis, with a discussion of distinguishing features for each entity. The fascia and disease processes that preferentially involve the fascia are also reviewed. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

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.

The challenges of studying visual expertise in medical image diagnosis.

PubMed

Gegenfurtner, Andreas; Kok, Ellen; van Geel, Koos; de Bruin, Anique; Jarodzka, Halszka; Szulewski, Adam; van Merriënboer, Jeroen Jg

2017-01-01

Visual expertise is the superior visual skill shown when executing domain-specific visual tasks. Understanding visual expertise is important in order to understand how the interpretation of medical images may be best learned and taught. In the context of this article, we focus on the visual skill of medical image diagnosis and, more specifically, on the methodological set-ups routinely used in visual expertise research. We offer a critique of commonly used methods and propose three challenges for future research to open up new avenues for studying characteristics of visual expertise in medical image diagnosis. The first challenge addresses theory development. Novel prospects in modelling visual expertise can emerge when we reflect on cognitive and socio-cultural epistemologies in visual expertise research, when we engage in statistical validations of existing theoretical assumptions and when we include social and socio-cultural processes in expertise development. The second challenge addresses the recording and analysis of longitudinal data. If we assume that the development of expertise is a long-term phenomenon, then it follows that future research can engage in advanced statistical modelling of longitudinal expertise data that extends the routine use of cross-sectional material through, for example, animations and dynamic visualisations of developmental data. The third challenge addresses the combination of methods. Alternatives to current practices can integrate qualitative and quantitative approaches in mixed-method designs, embrace relevant yet underused data sources and understand the need for multidisciplinary research teams. Embracing alternative epistemological and methodological approaches for studying visual expertise can lead to a more balanced and robust future for understanding superior visual skills in medical image diagnosis as well as other medical fields. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

US hospital-based direct access with radiology referral: an administrative case report.

PubMed

Keil, Aaron; Brown, Suzanne Robben

2015-01-01

Legislative gains in the US allow physical therapists to function in expanded scopes of practice including direct access and referral to specialists. The combination of direct access with privileges to order imaging studies directly offers a desirable practice status for many physical therapists, especially in musculoskeletal focused settings. Although direct access is legal in all US jurisdictions, institutional-based physical therapy settings have not embraced these practices. Barriers cited to implementing direct access with advanced practice are concerns over medical and administrative opposition, institutional policies, provider qualifications and reimbursement. This administrative case report describes the process taken to allow therapists to see patients without a referral and to order diagnostic imaging studies at an academic medical center. Nine-month implementation results show 66 patients seen via direct access with 15% referred for imaging studies. Claims submitted to 20 different insurance providers were reimbursed at 100%. While institutional regulations and reimbursement are reported as barriers to direct access, this report highlights the process one academic medical center used to implement direct access and advanced practice radiology referral by updating policies and procedures, identifying advanced competencies and communicating with necessary stakeholder groups. Favorable reimbursement for services is documented.

Medicalization of women's third age.

PubMed

Kaufert, P A; Lock, M

1997-06-01

Medicalization usually refers to the process whereby the normal processes of pregnancy, childbirth, menstruation and menopause have been claimed and redefined by medicine. Rather than discussing medicalization and menopause in terms of the number of women taking hormones, or the percentage of physicians convinced they should prescribe them, this paper looks at the visual image of the menopausal woman as portrayed in the pharmaceutical literature and in the mass media. Unlike the depressed and sickly looking women shown in the pharmaceutical advertisements in the 1970s, this 1990s version of the menopausal woman is shown glowing with fitness, with well-maintained teeth, hair and skin, far too fit to break a hip, have a heart attack, or witness the slow destruction of their minds by Alzheimer's disease. This image is not to be confused with the reality of being a menopausal woman, yet the two are intimately intertwined, for the image determines how menopausal women see themselves and how they are seen in the wider society. The final section of the paper discusses how health is the new virtue for women as they age as each individual is held responsible for what happens to her body, particularly in terms of the decisions made at the time of menopause.

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.

EduGATE - basic examples for educative purpose using the GATE simulation platform.

PubMed

Pietrzyk, Uwe; Zakhnini, Abdelhamid; Axer, Markus; Sauerzapf, Sophie; Benoit, Didier; Gaens, Michaela

2013-02-01

EduGATE is a collection of basic examples to introduce students to the fundamental physical aspects of medical imaging devices. It is based on the GATE platform, which has received a wide acceptance in the field of simulating medical imaging devices including SPECT, PET, CT and also applications in radiation therapy. GATE can be configured by commands, which are, for the sake of simplicity, listed in a collection of one or more macro files to set up phantoms, multiple types of sources, detection device, and acquisition parameters. The aim of the EduGATE is to use all these helpful features of GATE to provide insights into the physics of medical imaging by means of a collection of very basic and simple GATE macros in connection with analysis programs based on ROOT, a framework for data processing. A graphical user interface to define a configuration is also included. Copyright © 2012. Published by Elsevier GmbH.

3D printing from diagnostic images: a radiologist's primer with an emphasis on musculoskeletal imaging-putting the 3D printing of pathology into the hands of every physician.

PubMed

Friedman, Tamir; Michalski, Mark; Goodman, T Rob; Brown, J Elliott

2016-03-01

Three-dimensional (3D) printing has recently erupted into the medical arena due to decreased costs and increased availability of printers and software tools. Due to lack of detailed information in the medical literature on the methods for 3D printing, we have reviewed the medical and engineering literature on the various methods for 3D printing and compiled them into a practical "how to" format, thereby enabling the novice to start 3D printing with very limited funds. We describe (1) background knowledge, (2) imaging parameters, (3) software, (4) hardware, (5) post-processing, and (6) financial aspects required to cost-effectively reproduce a patient's disease ex vivo so that the patient, engineer and surgeon may hold the anatomy and associated pathology in their hands.

Smartphones as multimodal communication devices to facilitate clinical knowledge processes: randomized controlled trial.

PubMed

Pimmer, Christoph; Mateescu, Magdalena; Zahn, Carmen; Genewein, Urs

2013-11-27

Despite the widespread use and advancements of mobile technology that facilitate rich communication modes, there is little evidence demonstrating the value of smartphones for effective interclinician communication and knowledge processes. The objective of this study was to determine the effects of different synchronous smartphone-based modes of communication, such as (1) speech only, (2) speech and images, and (3) speech, images, and image annotation (guided noticing) on the recall and transfer of visually and verbally represented medical knowledge. The experiment was conducted from November 2011 to May 2012 at the University Hospital Basel (Switzerland) with 42 medical students in a master's program. All participants analyzed a standardized case (a patient with a subcapital fracture of the fifth metacarpal bone) based on a radiological image, photographs of the hand, and textual descriptions, and were asked to consult a remote surgical specialist via a smartphone. Participants were randomly assigned to 3 experimental conditions/groups. In group 1, the specialist provided verbal explanations (speech only). In group 2, the specialist provided verbal explanations and displayed the radiological image and the photographs to the participants (speech and images). In group 3, the specialist provided verbal explanations, displayed the radiological image and the photographs, and annotated the radiological image by drawing structures/angle elements (speech, images, and image annotation). To assess knowledge recall, participants were asked to write brief summaries of the case (verbally represented knowledge) after the consultation and to re-analyze the diagnostic images (visually represented knowledge). To assess knowledge transfer, participants analyzed a similar case without specialist support. Data analysis by ANOVA found that participants in groups 2 and 3 (images used) evaluated the support provided by the specialist as significantly more positive than group 1, the speech-only group (group 1: mean 4.08, SD 0.90; group 2: mean 4.73, SD 0.59; group 3: mean 4.93, SD 0.25; F2,39=6.76, P=.003; partial η(2)=0.26, 1-β=.90). However, significant positive effects on the recall and transfer of visually represented medical knowledge were only observed when the smartphone-based communication involved the combination of speech, images, and image annotation (group 3). There were no significant positive effects on the recall and transfer of visually represented knowledge between group 1 (speech only) and group 2 (speech and images). No significant differences were observed between the groups regarding verbally represented medical knowledge. The results show (1) the value of annotation functions for digital and mobile technology for interclinician communication and medical informatics, and (2) the use of guided noticing (the integration of speech, images, and image annotation) leads to significantly improved knowledge gains for visually represented knowledge. This is particularly valuable in situations involving complex visual subject matters, typical in clinical practice.

Smartphones as Multimodal Communication Devices to Facilitate Clinical Knowledge Processes: Randomized Controlled Trial

PubMed Central

Mateescu, Magdalena; Zahn, Carmen; Genewein, Urs

2013-01-01

Background Despite the widespread use and advancements of mobile technology that facilitate rich communication modes, there is little evidence demonstrating the value of smartphones for effective interclinician communication and knowledge processes. Objective The objective of this study was to determine the effects of different synchronous smartphone-based modes of communication, such as (1) speech only, (2) speech and images, and (3) speech, images, and image annotation (guided noticing) on the recall and transfer of visually and verbally represented medical knowledge. Methods The experiment was conducted from November 2011 to May 2012 at the University Hospital Basel (Switzerland) with 42 medical students in a master’s program. All participants analyzed a standardized case (a patient with a subcapital fracture of the fifth metacarpal bone) based on a radiological image, photographs of the hand, and textual descriptions, and were asked to consult a remote surgical specialist via a smartphone. Participants were randomly assigned to 3 experimental conditions/groups. In group 1, the specialist provided verbal explanations (speech only). In group 2, the specialist provided verbal explanations and displayed the radiological image and the photographs to the participants (speech and images). In group 3, the specialist provided verbal explanations, displayed the radiological image and the photographs, and annotated the radiological image by drawing structures/angle elements (speech, images, and image annotation). To assess knowledge recall, participants were asked to write brief summaries of the case (verbally represented knowledge) after the consultation and to re-analyze the diagnostic images (visually represented knowledge). To assess knowledge transfer, participants analyzed a similar case without specialist support. Results Data analysis by ANOVA found that participants in groups 2 and 3 (images used) evaluated the support provided by the specialist as significantly more positive than group 1, the speech-only group (group 1: mean 4.08, SD 0.90; group 2: mean 4.73, SD 0.59; group 3: mean 4.93, SD 0.25; F 2,39=6.76, P=.003; partial η2=0.26, 1–β=.90). However, significant positive effects on the recall and transfer of visually represented medical knowledge were only observed when the smartphone-based communication involved the combination of speech, images, and image annotation (group 3). There were no significant positive effects on the recall and transfer of visually represented knowledge between group 1 (speech only) and group 2 (speech and images). No significant differences were observed between the groups regarding verbally represented medical knowledge. Conclusions The results show (1) the value of annotation functions for digital and mobile technology for interclinician communication and medical informatics, and (2) the use of guided noticing (the integration of speech, images, and image annotation) leads to significantly improved knowledge gains for visually represented knowledge. This is particularly valuable in situations involving complex visual subject matters, typical in clinical practice. PMID:24284080

Enterprise-wide PACS: beyond radiology, an architecture to manage all medical images.

PubMed

Bandon, David; Lovis, Christian; Geissbühler, Antoine; Vallée, Jean-Paul

2005-08-01

Picture archiving and communication systems (PACS) have the vocation to manage all medical images acquired within the hospital. To address the various situations encountered in the imaging specialties, the traditional architecture used for the radiology department has to evolve. We present our preliminarily results toward an enterprise-wide PACS intended to support all kind of image production in medicine, from biomolecular images to whole-body pictures. Our solution is based on an existing radiologic PACS system from which images are distributed through an electronic patient record to all care facilities. This platform is enriched with a flexible integration framework supporting digital image communication in medicine (DICOM) and DICOM-XML formats. In addition, a generic workflow engine highly customizable is used to drive work processes. Echocardiology; hematology; ear, nose, and throat; and dermatology, including wounds, follow-up is the first implemented extensions outside of radiology. We also propose a global strategy for further developments based on three possible architectures for an enterprise-wide PACS.

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.

New secure communication-layer standard for medical image management (ISCL)

NASA Astrophysics Data System (ADS)

Kita, Kouichi; Nohara, Takashi; Hosoba, Minoru; Yachida, Masuyoshi; Yamaguchi, Masahiro; Ohyama, Nagaaki

1999-07-01

This paper introduces a summary of the standard draft of ISCL 1.00 which will be published by MEDIS-DC officially. ISCL is abbreviation of Integrated Secure Communication Layer Protocols for Secure Medical Image Management Systems. ISCL is a security layer which manages security function between presentation layer and TCP/IP layer. ISCL mechanism depends on basic function of a smart IC card and symmetric secret key mechanism. A symmetry key for each session is made by internal authentication function of a smart IC card with a random number. ISCL has three functions which assure authentication, confidently and integrity. Entity authentication process is done through 3 path 4 way method using functions of internal authentication and external authentication of a smart iC card. Confidentially algorithm and MAC algorithm for integrity are able to be selected. ISCL protocols are communicating through Message Block which consists of Message Header and Message Data. ISCL protocols are evaluating by applying to regional collaboration system for image diagnosis, and On-line Secure Electronic Storage system for medical images. These projects are supported by Medical Information System Development Center. These project shows ISCL is useful to keep security.

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.

[Evaluating the maturity of IT-supported clinical imaging and diagnosis using the Digital Imaging Adoption Model : Are your clinical imaging processes ready for the digital era?

PubMed

Studzinski, J

2017-06-01

The Digital Imaging Adoption Model (DIAM) has been jointly developed by HIMSS Analytics and the European Society of Radiology (ESR). It helps evaluate the maturity of IT-supported processes in medical imaging, particularly in radiology. This eight-stage maturity model drives your organisational, strategic and tactical alignment towards imaging-IT planning. The key audience for the model comprises hospitals with imaging centers, as well as external imaging centers that collaborate with hospitals. The assessment focuses on different dimensions relevant to digital imaging, such as software infrastructure and usage, workflow security, clinical documentation and decision support, data exchange and analytical capabilities. With its standardised approach, it enables regional, national and international benchmarking. All DIAM participants receive a structured report that can be used as a basis for presenting, e.g. budget planning and investment decisions at management level.

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

Re-engineering the process of medical imaging physics and technology education and training.

PubMed

Sprawls, Perry

2005-09-01

The extensive availability of digital technology provides an opportunity for enhancing both the effectiveness and efficiency of virtually all functions in the process of medical imaging physics and technology education and training. This includes degree granting academic programs within institutions and a wide spectrum of continuing education lifelong learning activities. Full achievement of the advantages of technology-enhanced education (e-learning, etc.) requires an analysis of specific educational activities with respect to desired outcomes and learning objectives. This is followed by the development of strategies and resources that are based on established educational principles. The impact of contemporary technology comes from its ability to place learners into enriched learning environments. The full advantage of a re-engineered and implemented educational process involves changing attitudes and functions of learning facilitators (teachers) and resource allocation and sharing both within and among institutions.

Cognition-based development and evaluation of ergonomic user interfaces for medical image processing and archiving systems.

PubMed

Demiris, A M; Meinzer, H P

1997-01-01

Whether or not a computerized system enhances the conditions of work in the application domain, very much demands on the user interface. Graphical user interfaces seem to attract the interest of the users but mostly ignore some basic rules of visual information processing thus leading to systems which are difficult to use, lowering productivity and increasing working stress (cognitive and work load). In this work we present some fundamental ergonomic considerations and their application to the medical image processing and archiving domain. We introduce the extensions to an existing concept needed to control and guide the development of GUIs with respect to domain specific ergonomics. The suggested concept, called Model-View-Controller Constraints (MVCC), can be used to programmatically implement ergonomic constraints, and thus has some advantages over written style guides. We conclude with the presentation of existing norms and methods to evaluate user interfaces.

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.

Grid-Independent Compressive Imaging and Fourier Phase Retrieval

ERIC Educational Resources Information Center

Liao, Wenjing

2013-01-01

This dissertation is composed of two parts. In the first part techniques of band exclusion(BE) and local optimization(LO) are proposed to solve linear continuum inverse problems independently of the grid spacing. The second part is devoted to the Fourier phase retrieval problem. Many situations in optics, medical imaging and signal processing call…

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

Unified modeling language and design of a case-based retrieval system in medical imaging.

PubMed Central

LeBozec, C.; Jaulent, M. C.; Zapletal, E.; Degoulet, P.

1998-01-01

One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users. Images Figure 6 Figure 7 PMID:9929346

Understanding Physiological and Degenerative Natural Vision Mechanisms to Define Contrast and Contour Operators

PubMed Central

Demongeot, Jacques; Fouquet, Yannick; Tayyab, Muhammad; Vuillerme, Nicolas

2009-01-01

Background Dynamical systems like neural networks based on lateral inhibition have a large field of applications in image processing, robotics and morphogenesis modeling. In this paper, we will propose some examples of dynamical flows used in image contrasting and contouring. Methodology First we present the physiological basis of the retina function by showing the role of the lateral inhibition in the optical illusions and pathologic processes generation. Then, based on these biological considerations about the real vision mechanisms, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a non-isotropic diffusion reaction partial differential system. Following this, we introduce other continuous operators based on similar biomimetic approaches: a chemotactic contrasting method, a viability contouring algorithm and an attentional focus operator. Then, we introduce the new notion of mixed potential Hamiltonian flows; we compare it with the watershed method and we use it for contouring. Conclusions We conclude by showing the utility of these biomimetic methods with some examples of application in medical imaging and computed assisted surgery. PMID:19547712

[Applicability of non-invasive imaging methods in forensic medicine and forensic anthropology in particular].

PubMed

Marcinková, Mária; Straka, Ľubomír; Novomeský, František; Janík, Martin; Štuller, František; Krajčovič, Jozef

2018-01-01

Massive progress in developing even more precise imaging modalities influenced all medical branches including the forensic medicine. In forensic anthropology, an inevitable part of forensic medicine itself, the use of all imaging modalities becomes even more important. Despite of acquiring more accurate informations about the deceased, all of them can be used in the process of identification and/or age estimation. X - ray imaging is most commonly used in detecting foreign bodies or various pathological changes of the deceased. Computed tomography, on the other hand, can be very helpful in the process of identification, whereas outcomes of this examination can be used for virtual reconstruction of living objects. Magnetic resonance imaging offers new opportunities in detecting cardiovascular pathological processes or develompental anomalies. Ultrasonography provides promising results in age estimation of living subjects without excessive doses of radiation. Processing the latest information sources available, authors introduce the application examples of X - ray imaging, computed tomography, magnetic resonance imaging and ultrasonography in everyday forensic medicine routine, with particular focusing on forensic anthropology.

Optical Fourier diffractometry applied to degraded bone structure recognition

NASA Astrophysics Data System (ADS)

Galas, Jacek; Godwod, Krzysztof; Szawdyn, Jacek; Sawicki, Andrzej

1993-09-01

Image processing and recognition methods are useful in many fields. This paper presents the hybrid optical and digital method applied to recognition of pathological changes in bones involved by metabolic bone diseases. The trabecular bone structure, registered by x ray on the photographic film, is analyzed in the new type of computer controlled diffractometer. The set of image parameters, extracted from diffractogram, is evaluated by statistical analysis. The synthetic image descriptors in discriminant space, constructed on the base of 3 training groups of images (control, osteoporosis, and osteomalacia groups) by discriminant analysis, allow us to recognize bone samples with degraded bone structure and to recognize the disease. About 89% of the images were classified correctly. This method after optimization process will be verified in medical investigations.

Digital Pathology: Data-Intensive Frontier in Medical Imaging

PubMed Central

Cooper, Lee A. D.; Carter, Alexis B.; Farris, Alton B.; Wang, Fusheng; Kong, Jun; Gutman, David A.; Widener, Patrick; Pan, Tony C.; Cholleti, Sharath R.; Sharma, Ashish; Kurc, Tahsin M.; Brat, Daniel J.; Saltz, Joel H.

2013-01-01

Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses. PMID:25328166

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

PubMed

Boix, Macarena; Cantó, Begoña

2013-04-01

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

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.

Hyperpolarized Porous Silicon Nanoparticles: Potential Theragnostic Material for ²⁹Si Magnetic Resonance Imaging.

PubMed

Seo, Hyeonglim; Choi, Ikjang; Whiting, Nicholas; Hu, Jingzhe; Luu, Quy Son; Pudakalakatti, Shivanand; McCowan, Caitlin; Kim, Yaewon; Zacharias, Niki; Lee, Seunghyun; Bhattacharya, Pratip; Lee, Youngbok

2018-05-20

Porous silicon nanoparticles have recently garnered attention as potentially-promising biomedical platforms for drug delivery and medical diagnostics. Here, we demonstrate porous silicon nanoparticles as contrast agents for ²⁹Si magnetic resonance imaging. Size-controlled porous silicon nanoparticles were synthesized by magnesiothermic reduction of silica nanoparticles and were surface activated for further functionalization. Particles were hyperpolarized via dynamic nuclear polarization to enhance their ²⁹Si MR signals; the particles demonstrated long ²⁹Si spin-lattice relaxation (T₁) times (~ 25 mins), which suggests potential applicability for medical imaging. Furthermore, ²⁹Si hyperpolarization levels were sufficient to allow ²⁹Si MRI in phantoms. These results underscore the potential of porous silicon nanoparticles that, when combined with hyperpolarized magnetic resonance imaging, can be a powerful theragnostic deep tissue imaging platform to interrogate various biomolecular processes in vivo. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ultrasound image edge detection based on a novel multiplicative gradient and Canny operator.

PubMed

Zheng, Yinfei; Zhou, Yali; Zhou, Hao; Gong, Xiaohong

2015-07-01

To achieve the fast and accurate segmentation of ultrasound image, a novel edge detection method for speckle noised ultrasound images was proposed, which was based on the traditional Canny and a novel multiplicative gradient operator. The proposed technique combines a new multiplicative gradient operator of non-Newtonian type with the traditional Canny operator to generate the initial edge map, which is subsequently optimized by the following edge tracing step. To verify the proposed method, we compared it with several other edge detection methods that had good robustness to noise, with experiments on the simulated and in vivo medical ultrasound image. Experimental results showed that the proposed algorithm has higher speed for real-time processing, and the edge detection accuracy could be 75% or more. Thus, the proposed method is very suitable for fast and accurate edge detection of medical ultrasound images. © The Author(s) 2014.

MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection

PubMed Central

Ou, Yangming; Resnick, Susan M.; Gur, Ruben C.; Gur, Raquel E.; Satterthwaite, Theodore D.; Furth, Susan; Davatzikos, Christos

2016-01-01

Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images. PMID:26679328

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

A., Javadpour; A., Mohammadi

2016-01-01

Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

WE-B-BRC-03: Risk in the Context of Medical Imaging

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

Samei, E.

Prospective quality management techniques, long used by engineering and industry, have become a growing aspect of efforts to improve quality management and safety in healthcare. These techniques are of particular interest to medical physics as scope and complexity of clinical practice continue to grow, thus making the prescriptive methods we have used harder to apply and potentially less effective for our interconnected and highly complex healthcare enterprise, especially in imaging and radiation oncology. An essential part of most prospective methods is the need to assess the various risks associated with problems, failures, errors, and design flaws in our systems. Wemore » therefore begin with an overview of risk assessment methodologies used in healthcare and industry and discuss their strengths and weaknesses. The rationale for use of process mapping, failure modes and effects analysis (FMEA) and fault tree analysis (FTA) by TG-100 will be described, as well as suggestions for the way forward. This is followed by discussion of radiation oncology specific risk assessment strategies and issues, including the TG-100 effort to evaluate IMRT and other ways to think about risk in the context of radiotherapy. Incident learning systems, local as well as the ASTRO/AAPM ROILS system, can also be useful in the risk assessment process. Finally, risk in the context of medical imaging will be discussed. Radiation (and other) safety considerations, as well as lack of quality and certainty all contribute to the potential risks associated with suboptimal imaging. The goal of this session is to summarize a wide variety of risk analysis methods and issues to give the medical physicist access to tools which can better define risks (and their importance) which we work to mitigate with both prescriptive and prospective risk-based quality management methods. Learning Objectives: Description of risk assessment methodologies used in healthcare and industry Discussion of radiation oncology-specific risk assessment strategies and issues Evaluation of risk in the context of medical imaging and image quality E. Samei: Research grants from Siemens and GE.« less

Dual function seal: visualized digital signature for electronic medical record systems.

PubMed

Yu, Yao-Chang; Hou, Ting-Wei; Chiang, Tzu-Chiang

2012-10-01

Digital signature is an important cryptography technology to be used to provide integrity and non-repudiation in electronic medical record systems (EMRS) and it is required by law. However, digital signatures normally appear in forms unrecognizable to medical staff, this may reduce the trust from medical staff that is used to the handwritten signatures or seals. Therefore, in this paper we propose a dual function seal to extend user trust from a traditional seal to a digital signature. The proposed dual function seal is a prototype that combines the traditional seal and digital seal. With this prototype, medical personnel are not just can put a seal on paper but also generate a visualized digital signature for electronic medical records. Medical Personnel can then look at the visualized digital signature and directly know which medical personnel generated it, just like with a traditional seal. Discrete wavelet transform (DWT) is used as an image processing method to generate a visualized digital signature, and the peak signal to noise ratio (PSNR) is calculated to verify that distortions of all converted images are beyond human recognition, and the results of our converted images are from 70 dB to 80 dB. The signature recoverability is also tested in this proposed paper to ensure that the visualized digital signature is verifiable. A simulated EMRS is implemented to show how the visualized digital signature can be integrity into EMRS.

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method.

PubMed

Haddadpour, Mozhdeh; Daneshvar, Sabalan; Seyedarabi, Hadi

2017-08-01

The process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time. We used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (D k ) as an assessing spectral features and Average Gradient (AG k ) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method. In this paper we used three common evaluation metrics like Average Gradient (AG k ) and the lowest Discrepancy (D k ) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method. Since that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AG k ), Discrepancy (D k ) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images. Copyright © 2017 Chang Gung University. Published by Elsevier B.V. All rights reserved.

Volumetric brain tumour detection from MRI using visual saliency.

PubMed

Mitra, Somosmita; Banerjee, Subhashis; Hayashi, Yoichi

2017-01-01

Medical image processing has become a major player in the world of automatic tumour region detection and is tantamount to the incipient stages of computer aided design. Saliency detection is a crucial application of medical image processing, and serves in its potential aid to medical practitioners by making the affected area stand out in the foreground from the rest of the background image. The algorithm developed here is a new approach to the detection of saliency in a three dimensional multi channel MR image sequence for the glioblastoma multiforme (a form of malignant brain tumour). First we enhance the three channels, FLAIR (Fluid Attenuated Inversion Recovery), T2 and T1C (contrast enhanced with gadolinium) to generate a pseudo coloured RGB image. This is then converted to the CIE L*a*b* color space. Processing on cubes of sizes k = 4, 8, 16, the L*a*b* 3D image is then compressed into volumetric units; each representing the neighbourhood information of the surrounding 64 voxels for k = 4, 512 voxels for k = 8 and 4096 voxels for k = 16, respectively. The spatial distance of these voxels are then compared along the three major axes to generate the novel 3D saliency map of a 3D image, which unambiguously highlights the tumour region. The algorithm operates along the three major axes to maximise the computation efficiency while minimising loss of valuable 3D information. Thus the 3D multichannel MR image saliency detection algorithm is useful in generating a uniform and logistically correct 3D saliency map with pragmatic applicability in Computer Aided Detection (CADe). Assignment of uniform importance to all three axes proves to be an important factor in volumetric processing, which helps in noise reduction and reduces the possibility of compromising essential information. The effectiveness of the algorithm was evaluated over the BRATS MICCAI 2015 dataset having 274 glioma cases, consisting both of high grade and low grade GBM. The results were compared with that of the 2D saliency detection algorithm taken over the entire sequence of brain data. For all comparisons, the Area Under the receiver operator characteristic (ROC) Curve (AUC) has been found to be more than 0.99 ± 0.01 over various tumour types, structures and locations.

Real-time imaging through strongly scattering media: seeing through turbid media, instantly

PubMed Central

Sudarsanam, Sriram; Mathew, James; Panigrahi, Swapnesh; Fade, Julien; Alouini, Mehdi; Ramachandran, Hema

2016-01-01

Numerous everyday situations like navigation, medical imaging and rescue operations require viewing through optically inhomogeneous media. This is a challenging task as photons propagate predominantly diffusively (rather than ballistically) due to random multiple scattering off the inhomogenieties. Real-time imaging with ballistic light under continuous-wave illumination is even more challenging due to the extremely weak signal, necessitating voluminous data-processing. Here we report imaging through strongly scattering media in real-time and at rates several times the critical flicker frequency of the eye, so that motion is perceived as continuous. Two factors contributed to the speedup of more than three orders of magnitude over conventional techniques - the use of a simplified algorithm enabling processing of data on the fly, and the utilisation of task and data parallelization capabilities of typical desktop computers. The extreme simplicity of the technique, and its implementation with present day low-cost technology promises its utility in a variety of devices in maritime, aerospace, rail and road transport, in medical imaging and defence. It is of equal interest to the common man and adventure sportsperson like hikers, divers, mountaineers, who frequently encounter situations requiring realtime imaging through obscuring media. As a specific example, navigation under poor visibility is examined. PMID:27114106

Statistical ultrasonics: the influence of Robert F. Wagner

NASA Astrophysics Data System (ADS)

Insana, Michael F.

2009-02-01

An important ongoing question for higher education is how to successfully mentor the next generation of scientists and engineers. It has been my privilege to have been mentored by one of the best, Dr Robert F. Wagner and his colleagues at the CDRH/FDA during the mid 1980s. Bob introduced many of us in medical ultrasonics to statistical imaging techniques. These ideas continue to broadly influence studies on adaptive aperture management (beamforming, speckle suppression, compounding), tissue characterization (texture features, Rayleigh/Rician statistics, scatterer size and number density estimators), and fundamental questions about how limitations of the human eye-brain system for extracting information from textured images can motivate image processing. He adapted the classical techniques of signal detection theory to coherent imaging systems that, for the first time in ultrasonics, related common engineering metrics for image quality to task-based clinical performance. This talk summarizes my wonderfully-exciting three years with Bob as I watched him explore topics in statistical image analysis that formed a rational basis for many of the signal processing techniques used in commercial systems today. It is a story of an exciting time in medical ultrasonics, and of how a sparkling personality guided and motivated the development of junior scientists who flocked around him in admiration and amazement.

A new method of content based medical image retrieval and its applications to CT imaging sign retrieval.

PubMed

Ma, Ling; Liu, Xiabi; Gao, Yan; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2017-02-01

This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency. Copyright © 2017 Elsevier Inc. All rights reserved.

Implementation of Enterprise Imaging Strategy at a Chinese Tertiary Hospital.

PubMed

Li, Shanshan; Liu, Yao; Yuan, Yifang; Li, Jia; Wei, Lan; Wang, Yuelong; Fei, Xiaolu

2018-01-04

Medical images have become increasingly important in clinical practice and medical research, and the need to manage images at the hospital level has become urgent in China. To unify patient identification in examinations from different medical specialties, increase convenient access to medical images under authentication, and make medical images suitable for further artificial intelligence investigations, we implemented an enterprise imaging strategy by adopting an image integration platform as the main tool at Xuanwu Hospital. Workflow re-engineering and business system transformation was also performed to ensure the quality and content of the imaging data. More than 54 million medical images and approximately 1 million medical reports were integrated, and uniform patient identification, images, and report integration were made available to the medical staff and were accessible via a mobile application, which were achieved by implementing the enterprise imaging strategy. However, to integrate all medical images of different specialties at a hospital and ensure that the images and reports are qualified for data mining, some further policy and management measures are still needed.

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

Multimodal medical information retrieval with unsupervised rank fusion.

PubMed

Mourão, André; Martins, Flávio; Magalhães, João

2015-01-01

Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical. Copyright © 2014 Elsevier Ltd. All rights reserved.

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Towards intelligent diagnostic system employing integration of mathematical and engineering model

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

Isa, Nor Ashidi Mat

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability ofmore » the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.« less

Towards intelligent diagnostic system employing integration of mathematical and engineering model

NASA Astrophysics Data System (ADS)

Isa, Nor Ashidi Mat

2015-05-01

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

Social process and the assessment of a new imaging technique.

PubMed

Blume, S S

1993-01-01

Each group involved in the development of a new medical technology constantly assesses the value of the emergent technique in terms of the group's own specific goals and conventions. The history of infrared thermography demonstrates the social nature of this assessment process.

Advanced medical imaging protocol workflow-a flexible electronic solution to optimize process efficiency, care quality and patient safety in the National VA Enterprise.

PubMed

Medverd, Jonathan R; Cross, Nathan M; Font, Frank; Casertano, Andrew

2013-08-01

Radiologists routinely make decisions with only limited information when assigning protocol instructions for the performance of advanced medical imaging examinations. Opportunity exists to simultaneously improve the safety, quality and efficiency of this workflow through the application of an electronic solution leveraging health system resources to provide concise, tailored information and decision support in real-time. Such a system has been developed using an open source, open standards design for use within the Veterans Health Administration. The Radiology Protocol Tool Recorder (RAPTOR) project identified key process attributes as well as inherent weaknesses of paper processes and electronic emulators of paper processes to guide the development of its optimized electronic solution. The design provides a kernel that can be expanded to create an integrated radiology environment. RAPTOR has implications relevant to the greater health care community, and serves as a case model for modernization of legacy government health information systems.

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

PubMed Central

Sechopoulos, Ioannis

2013-01-01

Many important post-acquisition aspects of breast tomosynthesis imaging can impact its clinical performance. Chief among them is the reconstruction algorithm that generates the representation of the three-dimensional breast volume from the acquired projections. But even after reconstruction, additional processes, such as artifact reduction algorithms, computer aided detection and diagnosis, among others, can also impact the performance of breast tomosynthesis in the clinical realm. In this two part paper, a review of breast tomosynthesis research is performed, with an emphasis on its medical physics aspects. In the companion paper, the first part of this review, the research performed relevant to the image acquisition process is examined. This second part will review the research on the post-acquisition aspects, including reconstruction, image processing, and analysis, as well as the advanced applications being investigated for breast tomosynthesis. PMID:23298127

MO-F-204-00: Preparing for the ABR Diagnostic and Nuclear Medical Physics Exams

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

NONE

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of allmore » aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

TU-AB-204-04: Partnerships

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

Ochs, R.

The responsibilities of the Food and Drug Administration (FDA) have increased since the inception of the Food and Drugs Act in 1906. Medical devices first came under comprehensive regulation with the passage of the 1938 Food, Drug, and Cosmetic Act. In 1971 FDA also took on the responsibility for consumer protection against unnecessary exposure to radiation-emitting devices for home and occupational use. However it was not until 1976, under the Medical Device Regulation Act, that the FDA was responsible for the safety and effectiveness of medical devices. This session will be presented by the Division of Radiological Health (DRH) andmore » the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) from the Center for Devices and Radiological Health (CDRH) at the FDA. The symposium will discuss on how we protect and promote public health with a focus on medical physics applications organized into four areas: pre-market device review, post-market surveillance, device compliance, current regulatory research efforts and partnerships with other organizations. The pre-market session will summarize the pathways FDA uses to regulate the investigational use and commercialization of diagnostic imaging and radiation therapy medical devices in the US, highlighting resources available to assist investigators and manufacturers. The post-market session will explain the post-market surveillance and compliance activities FDA performs to monitor the safety and effectiveness of devices on the market. The third session will describe research efforts that support the regulatory mission of the Agency. An overview of our regulatory research portfolio to advance our understanding of medical physics and imaging technologies and approaches to their evaluation will be discussed. Lastly, mechanisms that FDA uses to seek public input and promote collaborations with professional, government, and international organizations, such as AAPM, International Electrotechnical Commission (IEC), Image Gently, and the Quantitative Imaging Biomarkers Alliance (QIBA) among others, to fulfill FDA’s mission will be discussed. Learning Objectives: Understand FDA’s pre-market and post-market review processes for medical devices Understand FDA’s current regulatory research activities in the areas of medical physics and imaging products Understand how being involved with AAPM and other organizations can also help to promote innovative, safe and effective medical devices J. Delfino, nothing to disclose.« less

The production of digital and printed resources from multiple modalities using visualization and three-dimensional printing techniques.

PubMed

Shui, Wuyang; Zhou, Mingquan; Chen, Shi; Pan, Zhouxian; Deng, Qingqiong; Yao, Yong; Pan, Hui; He, Taiping; Wang, Xingce

2017-01-01

Virtual digital resources and printed models have become indispensable tools for medical training and surgical planning. Nevertheless, printed models of soft tissue organs are still challenging to reproduce. This study adopts open source packages and a low-cost desktop 3D printer to convert multiple modalities of medical images to digital resources (volume rendering images and digital models) and lifelike printed models, which are useful to enhance our understanding of the geometric structure and complex spatial nature of anatomical organs. Neuroimaging technologies such as CT, CTA, MRI, and TOF-MRA collect serial medical images. The procedures for producing digital resources can be divided into volume rendering and medical image reconstruction. To verify the accuracy of reconstruction, this study presents qualitative and quantitative assessments. Subsequently, digital models are archived as stereolithography format files and imported to the bundled software of the 3D printer. The printed models are produced using polylactide filament materials. We have successfully converted multiple modalities of medical images to digital resources and printed models for both hard organs (cranial base and tooth) and soft tissue organs (brain, blood vessels of the brain, the heart chambers and vessel lumen, and pituitary tumor). Multiple digital resources and printed models were provided to illustrate the anatomical relationship between organs and complicated surrounding structures. Three-dimensional printing (3DP) is a powerful tool to produce lifelike and tangible models. We present an available and cost-effective method for producing both digital resources and printed models. The choice of modality in medical images and the processing approach is important when reproducing soft tissue organs models. The accuracy of the printed model is determined by the quality of organ models and 3DP. With the ongoing improvement of printing techniques and the variety of materials available, 3DP will become an indispensable tool in medical training and surgical planning.

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.

Radiography for intensive care: participatory process analysis in a PACS-equipped and film/screen environment

NASA Astrophysics Data System (ADS)

Peer, Regina; Peer, Siegfried; Sander, Heike; Marsolek, Ingo; Koller, Wolfgang; Pappert, Dirk; Hierholzer, Johannes

2002-05-01

If new technology is introduced into medical practice it must prove to make a difference. However traditional approaches of outcome analysis failed to show a direct benefit of PACS on patient care and economical benefits are still in debate. A participatory process analysis was performed to compare workflow in a film based hospital and a PACS environment. This included direct observation of work processes, interview of involved staff, structural analysis and discussion of observations with staff members. After definition of common structures strong and weak workflow steps were evaluated. With a common workflow structure in both hospitals, benefits of PACS were revealed in workflow steps related to image reporting with simultaneous image access for ICU-physicians and radiologists, archiving of images as well as image and report distribution. However PACS alone is not able to cover the complete process of 'radiography for intensive care' from ordering of an image till provision of the final product equals image + report. Interference of electronic workflow with analogue process steps such as paper based ordering reduces the potential benefits of PACS. In this regard workflow modeling proved to be very helpful for the evaluation of complex work processes linking radiology and the ICU.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

New Developments in Observer Performance Methodology in Medical Imaging

PubMed Central

Chakraborty, Dev P.

2011-01-01

A common task in medical imaging is assessing whether a new imaging system, or a variant of an existing one, is an improvement over an existing imaging technology. Imaging systems are generally quite complex, consisting of several components – e.g., image acquisition hardware, image processing and display hardware and software, and image interpretation by radiologists– each of which can affect performance. While it may appear odd to include the radiologist as a “component” of the imaging chain, since the radiologist’s decision determines subsequent patient care, the effect of the human interpretation has to be included. Physical measurements like modulation transfer function, signal to noise ratio, etc., are useful for characterizing the non-human parts of the imaging chain under idealized and often unrealistic conditions, such as uniform background phantoms, target objects with sharp edges, etc. Measuring the effect on performance of the entire imaging chain, including the radiologist, and using real clinical images, requires different methods that fall under the rubric of observer performance methods or “ROC analysis”. The purpose of this paper is to review recent developments in this field, particularly with respect to the free-response method. PMID:21978444

Digital radiography: spatial and contrast resolution

NASA Astrophysics Data System (ADS)

Bjorkholm, Paul; Annis, M.; Frederick, E.; Stein, J.; Swift, R.

1981-07-01

The addition of digital image collection and storage to standard and newly developed x-ray imaging techniques has allowed spectacular improvements in some diagnostic procedures. There is no reason to expect that the developments in this area are yet complete. But no matter what further developments occur in this field, all the techniques will share a common element, digital image storage and processing. This common element alone determines some of the important imaging characteristics. These will be discussed using one system, the Medical MICRODOSE System as an example.

The COST Action IC0604 "Telepathology Network in Europe" (EURO-TELEPATH).

PubMed

García-Rojo, Marcial; Gonçalves, Luís; Blobel, Bernd

2012-01-01

The COST Action IC0604 "Telepathology Network in Europe" (EURO-TELEPATH) is a European COST Action that has been running from 2007 to 2011. COST Actions are funded by the COST (European Cooperation in the field of Scientific and Technical Research) Agency, supported by the Seventh Framework Programme for Research and Technological Development (FP7), of the European Union. EURO-TELEPATH's main objectives were evaluating and validating the common technological framework and communication standards required to access, transmit and manage digital medical records by pathologists and other medical professionals in a networked environment. The project was organized in four working groups. orking Group 1 "Business modeling in pathology" has designed main pathology processes - Frozen Study, Formalin Fixed Specimen Study, Telepathology, Cytology, and Autopsy -using Business Process Modeling Notation (BPMN). orking Group 2 "Informatics standards in pathology" has been dedicated to promoting the development and application of informatics standards in pathology, collaborating with Integrating the Healthcare Enterprise (IHE), Digital Imaging and Communications in Medicine (DICOM), Health Level Seven (HL7), and other standardization bodies. Working Group 3 "Images: Analysis, Processing, Retrieval and Management" worked on the use of virtual or digital slides that are fostering the use of image processing and analysis in pathology not only for research purposes, but also in daily practice. Working Group 4 "Technology and Automation in Pathology" was focused on studying the adequacy of current existing technical solutions, including, e.g., the quality of images obtained by slide scanners, or the efficiency of image analysis applications. Major outcome of this action are the collaboration with international health informatics standardization bodies to foster the development of standards for digital pathology, offering a new approach for workflow analysis, based in business process modeling. Health terminology standardization research has become a topic of high interest. Future research work should focus on standardization of automatic image analysis and tissue microarrays imaging.

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.

Optical design and testing: introduction.

PubMed

Liang, Chao-Wen; Koshel, John; Sasian, Jose; Breault, Robert; Wang, Yongtian; Fang, Yi Chin

2014-10-10

Optical design and testing has numerous applications in industrial, military, consumer, and medical settings. Assembling a complete imaging or nonimage optical system may require the integration of optics, mechatronics, lighting technology, optimization, ray tracing, aberration analysis, image processing, tolerance compensation, and display rendering. This issue features original research ranging from the optical design of image and nonimage optical stimuli for human perception, optics applications, bio-optics applications, 3D display, solar energy system, opto-mechatronics to novel imaging or nonimage modalities in visible and infrared spectral imaging, modulation transfer function measurement, and innovative interferometry.

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.

An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images.

PubMed

Guo, Yanen; Xu, Xiaoyin; Wang, Yuanyuan; Wang, Yaming; Xia, Shunren; Yang, Zhong

2014-08-01

Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images. © 2014 Wiley Periodicals, Inc.

Gold nanoparticle contrast agents in advanced X-ray imaging technologies.

PubMed

Ahn, Sungsook; Jung, Sung Yong; Lee, Sang Joon

2013-05-17

Recently, there has been significant progress in the field of soft- and hard-X-ray imaging for a wide range of applications, both technically and scientifically, via developments in sources, optics and imaging methodologies. While one community is pursuing extensive applications of available X-ray tools, others are investigating improvements in techniques, including new optics, higher spatial resolutions and brighter compact sources. For increased image quality and more exquisite investigation on characteristic biological phenomena, contrast agents have been employed extensively in imaging technologies. Heavy metal nanoparticles are excellent absorbers of X-rays and can offer excellent improvements in medical diagnosis and X-ray imaging. In this context, the role of gold (Au) is important for advanced X-ray imaging applications. Au has a long-history in a wide range of medical applications and exhibits characteristic interactions with X-rays. Therefore, Au can offer a particular advantage as a tracer and a contrast enhancer in X-ray imaging technologies by sensing the variation in X-ray attenuation in a given sample volume. This review summarizes basic understanding on X-ray imaging from device set-up to technologies. Then this review covers recent studies in the development of X-ray imaging techniques utilizing gold nanoparticles (AuNPs) and their relevant applications, including two- and three-dimensional biological imaging, dynamical processes in a living system, single cell-based imaging and quantitative analysis of circulatory systems and so on. In addition to conventional medical applications, various novel research areas have been developed and are expected to be further developed through AuNP-based X-ray imaging technologies.

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.

A quality-refinement process for medical imaging applications.

PubMed

Neuhaus, J; Maleike, D; Nolden, M; Kenngott, H-G; Meinzer, H-P; Wolf, I

2009-01-01

To introduce and evaluate a process for refinement of software quality that is suitable to research groups. In order to avoid constraining researchers too much, the quality improvement process has to be designed carefully. The scope of this paper is to present and evaluate a process to advance quality aspects of existing research prototypes in order to make them ready for initial clinical studies. The proposed process is tailored for research environments and therefore more lightweight than traditional quality management processes. Focus on quality criteria that are important at the given stage of the software life cycle. Usage of tools that automate aspects of the process is emphasized. To evaluate the additional effort that comes along with the process, it was exemplarily applied for eight prototypical software modules for medical image processing. The introduced process has been applied to improve the quality of all prototypes so that they could be successfully used in clinical studies. The quality refinement yielded an average of 13 person days of additional effort per project. Overall, 107 bugs were found and resolved by applying the process. Careful selection of quality criteria and the usage of automated process tools lead to a lightweight quality refinement process suitable for scientific research groups that can be applied to ensure a successful transfer of technical software prototypes into clinical research workflows.

The patient experience of high technology medical imaging: a systematic review of the qualitative evidence.

PubMed

Munn, Zachary; Jordan, Zoe

When presenting to an imaging department, the person who is to be imaged is often in a vulnerable state, and out of their comfort zone. It is the role of the medical imaging technician to produce a high quality image and facilitate patient care throughout the imaging process. Qualitative research is necessary to better inform the medical imaging technician and to help them to understand the experience of the person being imaged. Some issues that have been identified in the literature include fear, claustrophobia, dehumanisation, and an uncomfortable or unusual experience. There is now a small but worthwhile qualitative literature base focusing on the patient experience in high technology imaging. There is no current qualitative synthesis of the literature on the patient experience in high technology imaging. It is therefore timely and worthwhile to produce a systematic review to identify and summarise the existent literature exploring the patient experience of high technology imaging. To identify the patient experience of high technology medical imaging. Studies that were of a qualitative design that explored the phenomenon of interest, the patient experience of high technology medical imaging. Participants included anyone who had undergone one of these procedures. The search strategy aimed to find both published and unpublished studies, and was conducted over a period from June - September 2010. No time limits were imposed on this search strategy. A three-step search strategy was utilised in this review. All studies that met the criteria were selected for retrieval. They were then assessed by two independent reviewers for methodological validity prior to inclusion in the review using standardised critical appraisal instruments from the Joanna Briggs Institute Qualitative Assessment and Review Instrument. Data was extracted from papers included in the review using the standardised data extraction tool from the Joanna Briggs Institute Qualitative Assessment and Review Instrument. Research findings were pooled using the Qualitative Assessment and Review Instrument. Following the search and critical appraisal processes, 15 studies were identified that were deemed of suitable quality to be included in the review. From these 15 studies, 127 findings were extracted, forming 33 categories and 11 synthesised findings. These synthesised findings related to the patient experience, the emotions they felt (whether negative or positive), the need for support and information, and highlighted the importance of imaging to the patient. The synthesised findings in this review highlight the diverse, unique and challenging ways in which people experience imaging with MRI and CT scanners. All health professionals involved in imaging need to be aware of the different ways each patient may experience imaging, and provide them with ongoing support and information. The implications for practice are derived directly from the results of the meta-synthesis, and each of the 11 synthesised findings. There is still scope for further high methodological qualitative studies to be conducted in this field, particularly in the field of nuclear medicine imaging and Positron Emission Tomography. Further studies may be conducted in certain patient groups, and in certain age ranges. No studies were found assessing the experience of children undergoing high technology imaging.

Design and implementation of non-linear image processing functions for CMOS image sensor

NASA Astrophysics Data System (ADS)

Musa, Purnawarman; Sudiro, Sunny A.; Wibowo, Eri P.; Harmanto, Suryadi; Paindavoine, Michel

2012-11-01

Today, solid state image sensors are used in many applications like in mobile phones, video surveillance systems, embedded medical imaging and industrial vision systems. These image sensors require the integration in the focal plane (or near the focal plane) of complex image processing algorithms. Such devices must meet the constraints related to the quality of acquired images, speed and performance of embedded processing, as well as low power consumption. To achieve these objectives, low-level analog processing allows extracting the useful information in the scene directly. For example, edge detection step followed by a local maxima extraction will facilitate the high-level processing like objects pattern recognition in a visual scene. Our goal was to design an intelligent image sensor prototype achieving high-speed image acquisition and non-linear image processing (like local minima and maxima calculations). For this purpose, we present in this article the design and test of a 64×64 pixels image sensor built in a standard CMOS Technology 0.35 μm including non-linear image processing. The architecture of our sensor, named nLiRIC (non-Linear Rapid Image Capture), is based on the implementation of an analog Minima/Maxima Unit. This MMU calculates the minimum and maximum values (non-linear functions), in real time, in a 2×2 pixels neighbourhood. Each MMU needs 52 transistors and the pitch of one pixel is 40×40 mu m. The total area of the 64×64 pixels is 12.5mm2. Our tests have shown the validity of the main functions of our new image sensor like fast image acquisition (10K frames per second), minima/maxima calculations in less then one ms.

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

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.

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

PubMed

Rahim, Sarni Suhaila; Palade, Vasile; Shuttleworth, James; Jayne, Chrisina

2016-12-01

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

Clinical evaluation of watermarked medical images.

PubMed

Zain, Jasni M; Fauzi, Abdul M; Aziz, Azian A

2006-01-01

Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.

[Research Progress of Multi-Model Medical Image Fusion at Feature Level].

PubMed

Zhang, Junjie; Zhou, Tao; Lu, Huiling; Wang, Huiqun

2016-04-01

Medical image fusion realizes advantage integration of functional images and anatomical images.This article discusses the research progress of multi-model medical image fusion at feature level.We firstly describe the principle of medical image fusion at feature level.Then we analyze and summarize fuzzy sets,rough sets,D-S evidence theory,artificial neural network,principal component analysis and other fusion methods’ applications in medical image fusion and get summery.Lastly,we in this article indicate present problems and the research direction of multi-model medical images in the future.

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).

A Dependable Massive Storage Service for Medical Imaging.

PubMed

Núñez-Gaona, Marco Antonio; Marcelín-Jiménez, Ricardo; Gutiérrez-Martínez, Josefina; Aguirre-Meneses, Heriberto; Gonzalez-Compean, José Luis

2018-05-18

We present the construction of Babel, a distributed storage system that meets stringent requirements on dependability, availability, and scalability. Together with Babel, we developed an application that uses our system to store medical images. Accordingly, we show the feasibility of our proposal to provide an alternative solution for massive scientific storage and describe the software architecture style that manages the DICOM images life cycle, utilizing Babel like a virtual local storage component for a picture archiving and communication system (PACS-Babel Interface). Furthermore, we describe the communication interface in the Unified Modeling Language (UML) and show how it can be extended to manage the hard work associated with data migration processes on PACS in case of updates or disaster recovery.

A novel scatter-matrix eigenvalues-based total variation (SMETV) regularization for medical image restoration

NASA Astrophysics Data System (ADS)

Huang, Zhenghua; Zhang, Tianxu; Deng, Lihua; Fang, Hao; Li, Qian

2015-12-01

Total variation(TV) based on regularization has been proven as a popular and effective model for image restoration, because of its ability of edge preserved. However, as the TV favors a piece-wise constant solution, the processing results in the flat regions of the image are easily produced "staircase effects", and the amplitude of the edges will be underestimated; the underlying cause of the problem is that the regularization parameter can not be changeable with spatial local information of image. In this paper, we propose a novel Scatter-matrix eigenvalues-based TV(SMETV) regularization with image blind restoration algorithm for deblurring medical images. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish edges from flat areas. The proposed algorithm can effectively reduce the noise in flat regions as well as preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Extensive experiments demonstrate that the proposed approach produces results superior to most methods in both visual image quality and quantitative measures.

Ontology of gaps in content-based image retrieval.

PubMed

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

2009-04-01

Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the "semantic gap." The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of "gaps" in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven 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, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.

1984 European Conference on Optics, Optical Systems and Applications, Amsterdam, Netherlands, October 9-12, 1984, Proceedings

NASA Astrophysics Data System (ADS)

Boelger, B.; Ferwerda, H. A.

Various papers on optics, optical systems, and their applications are presented. The general topics addressed include: laser systems, optical and electrooptical materials and devices; novel spectroscopic techniques and applications; inspection, remote sensing, velocimetry, and gauging; optical design and image formation; holography, image processing, and storage; and integrated and fiber optics. Also discussed are: nonlinear optics; nonlinear photorefractive materials; scattering and diffractions applications in materials processing, deposition, and machining; medical and biological applications; and focus on industry.

The Imaging and Medical Beam Line at the Australian Synchrotron

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

Hausermann, Daniel; Hall, Chris; Maksimenko, Anton

2010-07-23

As a result of the enthusiastic support from the Australian biomedical, medical and clinical communities, the Australian Synchrotron is constructing a world-class facility for medical research, the 'Imaging and Medical Beamline'. The IMBL began phased commissioning in late 2008 and is scheduled to commence the first clinical research programs with patients in 2011. It will provide unrivalled x-ray facilities for imaging and radiotherapy for a wide range of research applications in diseases, treatments and understanding of physiological processes. The main clinical research drivers are currently high resolution and sensitivity cardiac and breast imaging, cell tracking applied to regenerative and stemmore » cell medicine and cancer therapies. The beam line has a maximum source to sample distance of 136 m and will deliver a 60 cm by 4 cm x-ray beam1 - monochromatic and white - to a three storey satellite building fully equipped for pre-clinical and clinical research. Currently operating with a 1.4 Tesla multi-pole wiggler, it will upgrade to a 4.2 Tesla device which requires the ability to handle up to 21 kW of x-ray power at any point along the beam line. The applications envisaged for this facility include imaging thick objects encompassing materials, humans and animals. Imaging can be performed in the range 15-150 keV. Radiotherapy research typically requires energies between 30 and 120 keV, for both monochromatic and broad beam.« less

High performance 3D adaptive filtering for DSP based portable medical imaging systems

NASA Astrophysics Data System (ADS)

Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark

2015-03-01

Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.

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

NASA Astrophysics Data System (ADS)

Zielinski, Jerzy S.

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

Classification of brain MRI with big data and deep 3D convolutional neural networks

NASA Astrophysics Data System (ADS)

Wegmayr, Viktor; Aitharaju, Sai; Buhmann, Joachim

2018-02-01

Our ever-aging society faces the growing problem of neurodegenerative diseases, in particular dementia. Magnetic Resonance Imaging provides a unique tool for non-invasive investigation of these brain diseases. However, it is extremely difficult for neurologists to identify complex disease patterns from large amounts of three-dimensional images. In contrast, machine learning excels at automatic pattern recognition from large amounts of data. In particular, deep learning has achieved impressive results in image classification. Unfortunately, its application to medical image classification remains difficult. We consider two reasons for this difficulty: First, volumetric medical image data is considerably scarcer than natural images. Second, the complexity of 3D medical images is much higher compared to common 2D images. To address the problem of small data set size, we assemble the largest dataset ever used for training a deep 3D convolutional neural network to classify brain images as healthy (HC), mild cognitive impairment (MCI) or Alzheimers disease (AD). We use more than 20.000 images from subjects of these three classes, which is almost 9x the size of the previously largest data set. The problem of high dimensionality is addressed by using a deep 3D convolutional neural network, which is state-of-the-art in large-scale image classification. We exploit its ability to process the images directly, only with standard preprocessing, but without the need for elaborate feature engineering. Compared to other work, our workflow is considerably simpler, which increases clinical applicability. Accuracy is measured on the ADNI+AIBL data sets, and the independent CADDementia benchmark.

Evaluation of aortic contractility based on analysis of CT images of the heart

NASA Astrophysics Data System (ADS)

DzierŻak, RóŻa; Maciejewski, Ryszard; Uhlig, Sebastian

2017-08-01

The paper presents a method to assess the aortic contractility based on the analysis of CT images of the heart. This is an alternative method that can be used for patients who cannot be examined by using echocardiography. Usage of medical imaging application for DICOM file processing allows to evaluate the aortic cross section during systole and diastole. It makes possible to assess the level of aortic contractility.

Validation of a computational knee joint model using an alignment method for the knee laxity test and computed tomography.

PubMed

Kang, Kyoung-Tak; Kim, Sung-Hwan; Son, Juhyun; Lee, Young Han; Koh, Yong-Gon

2017-01-01

Computational models have been identified as efficient techniques in the clinical decision-making process. However, computational model was validated using published data in most previous studies, and the kinematic validation of such models still remains a challenge. Recently, studies using medical imaging have provided a more accurate visualization of knee joint kinematics. The purpose of the present study was to perform kinematic validation for the subject-specific computational knee joint model by comparison with subject's medical imaging under identical laxity condition. The laxity test was applied to the anterior-posterior drawer under 90° flexion and the varus-valgus under 20° flexion with a series of stress radiographs, a Telos device, and computed tomography. The loading condition in the computational subject-specific knee joint model was identical to the laxity test condition in the medical image. Our computational model showed knee laxity kinematic trends that were consistent with the computed tomography images, except for negligible differences because of the indirect application of the subject's in vivo material properties. Medical imaging based on computed tomography with the laxity test allowed us to measure not only the precise translation but also the rotation of the knee joint. This methodology will be beneficial in the validation of laxity tests for subject- or patient-specific computational models.

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

A Q-Ising model application for linear-time image segmentation

NASA Astrophysics Data System (ADS)

Bentrem, Frank W.

2010-10-01

A computational method is presented which efficiently segments digital grayscale images by directly applying the Q-state Ising (or Potts) model. Since the Potts model was first proposed in 1952, physicists have studied lattice models to gain deep insights into magnetism and other disordered systems. For some time, researchers have realized that digital images may be modeled in much the same way as these physical systems ( i.e., as a square lattice of numerical values). A major drawback in using Potts model methods for image segmentation is that, with conventional methods, it processes in exponential time. Advances have been made via certain approximations to reduce the segmentation process to power-law time. However, in many applications (such as for sonar imagery), real-time processing requires much greater efficiency. This article contains a description of an energy minimization technique that applies four Potts (Q-Ising) models directly to the image and processes in linear time. The result is analogous to partitioning the system into regions of four classes of magnetism. This direct Potts segmentation technique is demonstrated on photographic, medical, and acoustic images.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-02-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-06-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Better Pictures in a Snap

NASA Technical Reports Server (NTRS)

2002-01-01

Retinex Imaging Processing, winner of NASA's 1999 Space Act Award, is commercially available through TruView Imaging Company. With this technology, amateur photographers use their personal computers to improve the brightness, scene contrast, detail, and overall sharpness of images with increased ease. The process was originally developed for remote sensing of the Earth by researchers at Langley Research Center and Science and Technology Corporation (STC). It automatically enhances a digital image in terms of dynamic range compression, color independence from the spectral distribution of the scene illuminant, and color/lightness rendition. As a result, the enhanced digital image is much closer to the scene perceived by the human visual system, under all kinds and levels of lighting variations. TruView believes there are other applications for the software in medical imaging, forensics, security, recognizance, mining, assembly, and other industrial areas.

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.

Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network

NASA Astrophysics Data System (ADS)

Negahdar, Mohammadreza; Beymer, David; Syeda-Mahmood, Tanveer

2018-02-01

Deep Learning models such as Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in 2D medical image analysis. In clinical practice; however, most analyzed and acquired medical data are formed of 3D volumes. In this paper, we present a fast and efficient 3D lung segmentation method based on V-net: a purely volumetric fully CNN. Our model is trained on chest CT images through volume to volume learning, which palliates overfitting problem on limited number of annotated training data. Adopting a pre-processing step and training an objective function based on Dice coefficient addresses the imbalance between the number of lung voxels against that of background. We have leveraged Vnet model by using batch normalization for training which enables us to use higher learning rate and accelerates the training of the model. To address the inadequacy of training data and obtain better robustness, we augment the data applying random linear and non-linear transformations. Experimental results on two challenging medical image data show that our proposed method achieved competitive result with a much faster speed.

Using deep learning for content-based medical image retrieval

NASA Astrophysics Data System (ADS)

Sun, Qinpei; Yang, Yuanyuan; Sun, Jianyong; Yang, Zhiming; Zhang, Jianguo

2017-03-01

Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging problems in current CBMIR research, which is mainly due to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have witnessed some important advances of new techniques in machine learning. One important breakthrough technique is known as "deep learning". Unlike conventional machine learning methods that are often using "shallow" architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation. This means that we do not need to spend enormous energy to extract features manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to improve the accuracy and speed of a CBIR in integrated RIS/PACS.

Spectral imaging applications: Remote sensing, environmental monitoring, medicine, military operations, factory automation and manufacturing

NASA Technical Reports Server (NTRS)

Gat, N.; Subramanian, S.; Barhen, J.; Toomarian, N.

1996-01-01

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.

Role of Travel Motivations, Perceived Risks and Travel Constraints on Destination Image and Visit Intention in Medical Tourism

PubMed Central

Khan, Mohammad J.; Chelliah, Shankar; Haron, Mahmod S.; Ahmed, Sahrish

2017-01-01

Travel motivations, perceived risks and travel constraints, along with the attributes and characteristics of medical tourism destinations, are important issues in medical tourism. Although the importance of these factors is already known, a comprehensive theoretical model of the decision-making process of medical tourists has yet to be established, analysing the intricate relationships between the different variables involved. This article examines a large body of literature on both medical and conventional tourism in order to propose a comprehensive theoretical framework of medical tourism decision-making. Many facets of this complex phenomenon require further empirical investigation. PMID:28417022

Role of Travel Motivations, Perceived Risks and Travel Constraints on Destination Image and Visit Intention in Medical Tourism: Theoretical model.

PubMed

Khan, Mohammad J; Chelliah, Shankar; Haron, Mahmod S; Ahmed, Sahrish

2017-02-01

Travel motivations, perceived risks and travel constraints, along with the attributes and characteristics of medical tourism destinations, are important issues in medical tourism. Although the importance of these factors is already known, a comprehensive theoretical model of the decision-making process of medical tourists has yet to be established, analysing the intricate relationships between the different variables involved. This article examines a large body of literature on both medical and conventional tourism in order to propose a comprehensive theoretical framework of medical tourism decision-making. Many facets of this complex phenomenon require further empirical investigation.

Quantitative imaging test approval and biomarker qualification: interrelated but distinct activities.

PubMed

Buckler, Andrew J; Bresolin, Linda; Dunnick, N Reed; Sullivan, Daniel C; Aerts, Hugo J W L; Bendriem, Bernard; Bendtsen, Claus; Boellaard, Ronald; Boone, John M; Cole, Patricia E; Conklin, James J; Dorfman, Gary S; Douglas, Pamela S; Eidsaunet, Willy; Elsinger, Cathy; Frank, Richard A; Gatsonis, Constantine; Giger, Maryellen L; Gupta, Sandeep N; Gustafson, David; Hoekstra, Otto S; Jackson, Edward F; Karam, Lisa; Kelloff, Gary J; Kinahan, Paul E; McLennan, Geoffrey; Miller, Colin G; Mozley, P David; Muller, Keith E; Patt, Rick; Raunig, David; Rosen, Mark; Rupani, Haren; Schwartz, Lawrence H; Siegel, Barry A; Sorensen, A Gregory; Wahl, Richard L; Waterton, John C; Wolf, Walter; Zahlmann, Gudrun; Zimmerman, Brian

2011-06-01

Quantitative imaging biomarkers could speed the development of new treatments for unmet medical needs and improve routine clinical care. However, it is not clear how the various regulatory and nonregulatory (eg, reimbursement) processes (often referred to as pathways) relate, nor is it clear which data need to be collected to support these different pathways most efficiently, given the time- and cost-intensive nature of doing so. The purpose of this article is to describe current thinking regarding these pathways emerging from diverse stakeholders interested and active in the definition, validation, and qualification of quantitative imaging biomarkers and to propose processes to facilitate the development and use of quantitative imaging biomarkers. A flexible framework is described that may be adapted for each imaging application, providing mechanisms that can be used to develop, assess, and evaluate relevant biomarkers. From this framework, processes can be mapped that would be applicable to both imaging product development and to quantitative imaging biomarker development aimed at increasing the effectiveness and availability of quantitative imaging. http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100800/-/DC1. RSNA, 2011

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.

Group-sparse representation with dictionary learning for medical image denoising and fusion.

PubMed

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Integrating user profile in medical CBIR systems to answer perceptual similarity queries

NASA Astrophysics Data System (ADS)

Bugatti, Pedro H.; Kaster, Daniel S.; Ponciano-Silva, Marcelo; Traina, Agma J. M.; Traina, Caetano, Jr.

2011-03-01

Techniques for Content-Based Image Retrieval (CBIR) have been intensively explored due to the increase in the amount of captured images and the need of fast retrieval of them. The medical field is a specific example that generates a large flow of information, especially digital images employed for diagnosing. One issue that still remains unsolved deals with how to reach the perceptual similarity. That is, to achieve an effective retrieval, one must characterize and quantify the perceptual similarity regarding the specialist in the field. Therefore, the present paper was conceived to fill in this gap creating a consistent support to perform similarity queries over medical images, maintaining the semantics of a given query desired by the user. CBIR systems relying in relevance feedback techniques usually request the users to label relevant images. In this paper, we present a simple but highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The user profiles maintain the settings desired for each user, allowing tuning the similarity assessment, which encompasses dynamically changing the distance function employed through an interactive process. Experiments using computed tomography lung images show that the proposed approach is effective in capturing the users' perception.

Enhancement of chest radiographs using eigenimage processing

NASA Astrophysics Data System (ADS)

Bones, Philip J.; Butler, Anthony P. H.; Hurrell, Michael

2006-08-01

Frontal chest radiographs ("chest X-rays") are routinely used by medical personnel to assess patients for a wide range of suspected disorders. Often large numbers of images need to be analyzed. Furthermore, at times the images need to analyzed ("reported") when no radiological expert is available. A system which enhances the images in such a way that abnormalities are more obvious is likely to reduce the chance that an abnormality goes unnoticed. The authors previously reported the use of principal components analysis to derive a basis set of eigenimages from a training set made up of images from normal subjects. The work is here extended to investigate how best to emphasize the abnormalities in chest radiographs. Results are also reported for various forms of image normalizing transformations used in performing the eigenimage processing.

[Bone drilling simulation by three-dimensional imaging].

PubMed

Suto, Y; Furuhata, K; Kojima, T; Kurokawa, T; Kobayashi, M

1989-06-01

The three-dimensional display technique has a wide range of medical applications. Pre-operative planning is one typical application: in orthopedic surgery, three-dimensional image processing has been used very successfully. We have employed this technique in pre-operative planning for orthopedic surgery, and have developed a simulation system for bone-drilling. Positive results were obtained by pre-operative rehearsal; when a region of interest is indicated by means of a mouse on the three-dimensional image displayed on the CRT, the corresponding region appears on the slice image which is displayed simultaneously. Consequently, the status of the bone-drilling is constantly monitored. In developing this system, we have placed emphasis on the quality of the reconstructed three-dimensional images, on fast processing, and on the easy operation of the surgical planning simulation.

A new method for fusion, denoising and enhancement of x-ray images retrieved from Talbot-Lau grating interferometry.

PubMed

Scholkmann, Felix; Revol, Vincent; Kaufmann, Rolf; Baronowski, Heidrun; Kottler, Christian

2014-03-21

This paper introduces a new image denoising, fusion and enhancement framework for combining and optimal visualization of x-ray attenuation contrast (AC), differential phase contrast (DPC) and dark-field contrast (DFC) images retrieved from x-ray Talbot-Lau grating interferometry. The new image fusion framework comprises three steps: (i) denoising each input image (AC, DPC and DFC) through adaptive Wiener filtering, (ii) performing a two-step image fusion process based on the shift-invariant wavelet transform, i.e. first fusing the AC with the DPC image and then fusing the resulting image with the DFC image, and finally (iii) enhancing the fused image to obtain a final image using adaptive histogram equalization, adaptive sharpening and contrast optimization. Application examples are presented for two biological objects (a human tooth and a cherry) and the proposed method is compared to two recently published AC/DPC/DFC image processing techniques. In conclusion, the new framework for the processing of AC, DPC and DFC allows the most relevant features of all three images to be combined in one image while reducing the noise and enhancing adaptively the relevant image features. The newly developed framework may be used in technical and medical applications.

Pc-Based Floating Point Imaging Workstation

NASA Astrophysics Data System (ADS)

Guzak, Chris J.; Pier, Richard M.; Chinn, Patty; Kim, Yongmin

1989-07-01

The medical, military, scientific and industrial communities have come to rely on imaging and computer graphics for solutions to many types of problems. Systems based on imaging technology are used to acquire and process images, and analyze and extract data from images that would otherwise be of little use. Images can be transformed and enhanced to reveal detail and meaning that would go undetected without imaging techniques. The success of imaging has increased the demand for faster and less expensive imaging systems and as these systems become available, more and more applications are discovered and more demands are made. From the designer's perspective the challenge to meet these demands forces him to attack the problem of imaging from a different perspective. The computing demands of imaging algorithms must be balanced against the desire for affordability and flexibility. Systems must be flexible and easy to use, ready for current applications but at the same time anticipating new, unthought of uses. Here at the University of Washington Image Processing Systems Lab (IPSL) we are focusing our attention on imaging and graphics systems that implement imaging algorithms for use in an interactive environment. We have developed a PC-based imaging workstation with the goal to provide powerful and flexible, floating point processing capabilities, along with graphics functions in an affordable package suitable for diverse environments and many applications.

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.

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.

Medical Devices; General Hospital and Personal Use Devices; Classification of the Image Processing Device for Estimation of External Blood Loss. Final order.

PubMed

2017-12-20

The Food and Drug Administration (FDA or we) is classifying the image processing device for estimation of external blood loss into class II (special controls). The special controls that apply to the device type are identified in this order and will be part of the codified language for the image processing device for estimation of external blood loss' classification. We are taking this action because we have determined that classifying the device into class II (special controls) will provide a reasonable assurance of safety and effectiveness of the device. We believe this action will also enhance patients' access to beneficial innovative devices, in part by reducing regulatory burdens.

Digital diagnosis of medical images

NASA Astrophysics Data System (ADS)

Heinonen, Tomi; Kuismin, Raimo; Jormalainen, Raimo; Dastidar, Prasun; Frey, Harry; Eskola, Hannu

2001-08-01

The popularity of digital imaging devices and PACS installations has increased during the last years. Still, images are analyzed and diagnosed using conventional techniques. Our research group begun to study the requirements for digital image diagnostic methods to be applied together with PACS systems. The research was focused on various image analysis procedures (e.g., segmentation, volumetry, 3D visualization, image fusion, anatomic atlas, etc.) that could be useful in medical diagnosis. We have developed Image Analysis software (www.medimag.net) to enable several image-processing applications in medical diagnosis, such as volumetry, multimodal visualization, and 3D visualizations. We have also developed a commercial scalable image archive system (ActaServer, supports DICOM) based on component technology (www.acta.fi), and several telemedicine applications. All the software and systems operate in NT environment and are in clinical use in several hospitals. The analysis software have been applied in clinical work and utilized in numerous patient cases (500 patients). This method has been used in the diagnosis, therapy and follow-up in various diseases of the central nervous system (CNS), respiratory system (RS) and human reproductive system (HRS). In many of these diseases e.g. Systemic Lupus Erythematosus (CNS), nasal airways diseases (RS) and ovarian tumors (HRS), these methods have been used for the first time in clinical work. According to our results, digital diagnosis improves diagnostic capabilities, and together with PACS installations it will become standard tool during the next decade by enabling more accurate diagnosis and patient follow-up.

Recent development of radiation measurement instrument for industrial and medical applications

NASA Astrophysics Data System (ADS)

Baba, Sueki; Ohmori, Koichi; Mito, Yoshio; Tanoue, Toshiya; Yano, Shigeki; Tokumori, Kenji; Toyofuku, Fukai; Kanda, Shigenobu

2001-02-01

Recently, computer imaging technology has developed very high-quality image and fast processing time. X-rays have been used for many purposes such as medical diagnosis and analyzing the structure of industrial materials. However, as X-rays are hazardous to the human body, it is desirable to reduce its exposed dose to a minimum. For this purpose, it is necessary to use a semiconductor radiation detector with a high efficiency for X-rays. We have developed photon-counting CdTe array detector system for medical and industrial use. The bone densitometer for Dual Energy X-ray Absorptometry (DEXA) has been developed to make diagnosis of osteoporosis, and it is developed to analyze a material element for industrial use. Recently, we have developed a monochromatic X-ray CT using a 256 ch CdTe array detector. We found that the array detector systems are very useful for medical and industrial applications.

Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML).

PubMed

Yu, Zheng-yang; Zheng, Shu-sen; Chen, Lei-ting; He, Xiao-qian; Wang, Jian-jun

2005-07-01

This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic concision, the VRML browser can offer better windows for man-computer interaction in real-time environment than ever before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and have a promising prospect in the fields of medical imaging.

Building Structured Personal Health Records from Photographs of Printed Medical Records.

PubMed

Li, Xiang; Hu, Gang; Teng, Xiaofei; Xie, Guotong

2015-01-01

Personal health records (PHRs) provide patient-centric healthcare by making health records accessible to patients. In China, it is very difficult for individuals to access electronic health records. Instead, individuals can easily obtain the printed copies of their own medical records, such as prescriptions and lab test reports, from hospitals. In this paper, we propose a practical approach to extract structured data from printed medical records photographed by mobile phones. An optical character recognition (OCR) pipeline is performed to recognize text in a document photo, which addresses the problems of low image quality and content complexity by image pre-processing and multiple OCR engine synthesis. A series of annotation algorithms that support flexible layouts are then used to identify the document type, entities of interest, and entity correlations, from which a structured PHR document is built. The proposed approach was applied to real world medical records to demonstrate the effectiveness and applicability.

Building Structured Personal Health Records from Photographs of Printed Medical Records

PubMed Central

Li, Xiang; Hu, Gang; Teng, Xiaofei; Xie, Guotong

2015-01-01

Personal health records (PHRs) provide patient-centric healthcare by making health records accessible to patients. In China, it is very difficult for individuals to access electronic health records. Instead, individuals can easily obtain the printed copies of their own medical records, such as prescriptions and lab test reports, from hospitals. In this paper, we propose a practical approach to extract structured data from printed medical records photographed by mobile phones. An optical character recognition (OCR) pipeline is performed to recognize text in a document photo, which addresses the problems of low image quality and content complexity by image pre-processing and multiple OCR engine synthesis. A series of annotation algorithms that support flexible layouts are then used to identify the document type, entities of interest, and entity correlations, from which a structured PHR document is built. The proposed approach was applied to real world medical records to demonstrate the effectiveness and applicability. PMID:26958219

Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML)*

PubMed Central

Yu, Zheng-yang; Zheng, Shu-sen; Chen, Lei-ting; He, Xiao-qian; Wang, Jian-jun

2005-01-01

This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic concision, the VRML browser can offer better windows for man-computer interaction in real-time environment than ever before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and have a promising prospect in the fields of medical imaging. PMID:15973760

MO-F-204-02: Preparing for Part 2 of the ABR Diagnostic Physics Exam

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

Szczykutowicz, T.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of allmore » aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

MO-F-204-03: Preparing for Part 3 of the ABR Diagnostic Physics Exam

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

Zambelli, J.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of allmore » aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

MO-F-204-01: Preparing for Part 1 of the ABR Diagnostic Physics Exam

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

McKenney, S.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of allmore » aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

MO-F-204-04: Preparing for Parts 2 & 3 of the ABR Nuclear Medicine Physics Exam

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

MacDougall, R.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of allmore » aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

WE-D-213-04: Preparing for Parts 2 & 3 of the ABR Nuclear Medicine Physics Exam

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

MacDougall, R.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance ofmore » all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

WE-D-213-00: Preparing for the ABR Diagnostic and Nuclear Medicine Physics Exams

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

NONE

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance ofmore » all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

WE-D-213-01: Preparing for Part 1 of the ABR Diagnostic Physics Exam

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

Simiele, S.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance ofmore » all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

WE-D-213-03: Preparing for Part 3 of the ABR Diagnostic Physics Exam

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

Bevins, N.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance ofmore » all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

WE-D-213-02: Preparing for Part 2 of the ABR Diagnostic Physics Exam

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

Zambelli, J.

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance ofmore » all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.« less

Medical Image Retrieval: A Multimodal Approach

PubMed Central

Cao, Yu; Steffey, Shawn; He, Jianbiao; Xiao, Degui; Tao, Cui; Chen, Ping; Müller, Henning

2014-01-01

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system. PMID:26309389

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

TU-AB-204-03: Research Activities in Medical Physics

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

Badano, A.

The responsibilities of the Food and Drug Administration (FDA) have increased since the inception of the Food and Drugs Act in 1906. Medical devices first came under comprehensive regulation with the passage of the 1938 Food, Drug, and Cosmetic Act. In 1971 FDA also took on the responsibility for consumer protection against unnecessary exposure to radiation-emitting devices for home and occupational use. However it was not until 1976, under the Medical Device Regulation Act, that the FDA was responsible for the safety and effectiveness of medical devices. This session will be presented by the Division of Radiological Health (DRH) andmore » the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) from the Center for Devices and Radiological Health (CDRH) at the FDA. The symposium will discuss on how we protect and promote public health with a focus on medical physics applications organized into four areas: pre-market device review, post-market surveillance, device compliance, current regulatory research efforts and partnerships with other organizations. The pre-market session will summarize the pathways FDA uses to regulate the investigational use and commercialization of diagnostic imaging and radiation therapy medical devices in the US, highlighting resources available to assist investigators and manufacturers. The post-market session will explain the post-market surveillance and compliance activities FDA performs to monitor the safety and effectiveness of devices on the market. The third session will describe research efforts that support the regulatory mission of the Agency. An overview of our regulatory research portfolio to advance our understanding of medical physics and imaging technologies and approaches to their evaluation will be discussed. Lastly, mechanisms that FDA uses to seek public input and promote collaborations with professional, government, and international organizations, such as AAPM, International Electrotechnical Commission (IEC), Image Gently, and the Quantitative Imaging Biomarkers Alliance (QIBA) among others, to fulfill FDA’s mission will be discussed. Learning Objectives: Understand FDA’s pre-market and post-market review processes for medical devices Understand FDA’s current regulatory research activities in the areas of medical physics and imaging products Understand how being involved with AAPM and other organizations can also help to promote innovative, safe and effective medical devices J. Delfino, nothing to disclose.« less

SU-F-P-06: Moving From Computed Radiography to Digital Radiography: A Collaborative Approach to Improve Image Quality

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

Sandoval, D; Mlady, G; Selwyn, R

Purpose: To bring together radiologists, technologists, and physicists to utilize post-processing techniques in digital radiography (DR) in order to optimize image acquisition and improve image quality. Methods: Sub-optimal images acquired on a new General Electric (GE) DR system were flagged for follow-up by radiologists and reviewed by technologists and medical physicists. Various exam types from adult musculoskeletal (n=35), adult chest (n=4), and pediatric (n=7) were chosen for review. 673 total images were reviewed. These images were processed using five customized algorithms provided by GE. An image score sheet was created allowing the radiologist to assign a numeric score to eachmore » of the processed images, this allowed for objective comparison to the original images. Each image was scored based on seven properties: 1) overall image look, 2) soft tissue contrast, 3) high contrast, 4) latitude, 5) tissue equalization, 6) edge enhancement, 7) visualization of structures. Additional space allowed for additional comments not captured in scoring categories. Radiologists scored the images from 1 – 10 with 1 being non-diagnostic quality and 10 being superior diagnostic quality. Scores for each custom algorithm for each image set were summed. The algorithm with the highest score for each image set was then set as the default processing. Results: Images placed into the PACS “QC folder” for image processing reasons decreased. Feedback from radiologists was, overall, that image quality for these studies had improved. All default processing for these image types was changed to the new algorithm. Conclusion: This work is an example of the collaboration between radiologists, technologists, and physicists at the University of New Mexico to add value to the radiology department. The significant amount of work required to prepare the processing algorithms, reprocessing and scoring of the images was eagerly taken on by all team members in order to produce better quality images and improve patient care.« less

Rotation covariant image processing for biomedical applications.

PubMed

Skibbe, Henrik; Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Clinical imaging guidelines part 4: challenges in identifying, engaging and collaborating with stakeholders.

PubMed

Bettmann, Michael A; Oikarinen, Helja; Rehani, Madan; Holmberg, Ola; del Rosario Perez, Maria; Naidoo, Anusha; Do, Kyung-Hyun; Dreyer, Keith; Ebdon-Jackson, Steve

2015-04-01

The effective development and use of clinical imaging guidelines requires an understanding of who the stakeholders are, what their interests in the process are, and what roles they should play. If the appropriate stakeholders are not engaged in the right roles, it is unlikely that clinical imaging guidelines will be successfully developed, relied on, and actually used. Some stakeholders are obvious: for the development of clinical imaging guidelines, both imagers and those who request examinations, such as general practitioners, internists, and medical specialists, must be involved. To gain acceptance, other relevant groups are stakeholders, including medical societies, other health care professionals, insurers, health IT experts and vendors, and patients. The role of stakeholders must be dictated by their specific interest. For some, involvement in the creation of guidelines is the right role. For others, such as regulators or insurers, reviews or invitations to comment are required, and for others, such as medical educators, it is probably sufficient to provide information and create awareness. Only through a careful consideration of who the stakeholders are and what are their interests are the successful development, acceptance, and use of clinical imaging guidelines likely to occur. Future efforts must focus on collaboration, particularly among groups that create clinical imaging guidelines and those that can support their use, and on regulatory roles and mandates. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Computer-aided diagnosis (CAD) for colonoscopy

NASA Astrophysics Data System (ADS)

Gu, Jia; Poirson, Allen

2007-03-01

Colorectal cancer is the second leading cause of cancer deaths, and ranks third for new cancer cases and cancer mortality for both men and women. However, its death rate can be dramatically reduced by appropriate treatment when early detection is available. The purpose of colonoscopy is to identify and assess the severity of lesions, which may be flat or protruding. Due to the subjective nature of the examination, colonoscopic proficiency is highly variable and dependent upon the colonoscopist's knowledge and experience. An automated image processing system providing an objective, rapid, and inexpensive analysis of video from a standard colonoscope could provide a valuable tool for screening and diagnosis. In this paper, we present the design, functionality and preliminary results of its Computer-Aided-Diagnosis (CAD) system for colonoscopy - ColonoCAD TM. ColonoCAD is a complex multi-sensor, multi-data and multi-algorithm image processing system, incorporating data management and visualization, video quality assessment and enhancement, calibration, multiple view based reconstruction, feature extraction and classification. As this is a new field in medical image processing, our hope is that this paper will provide the framework to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

Adaptive nonlinear L2 and L3 filters for speckled image processing

NASA Astrophysics Data System (ADS)

Lukin, Vladimir V.; Melnik, Vladimir P.; Chemerovsky, Victor I.; Astola, Jaakko T.

1997-04-01

Here we propose adaptive nonlinear filters based on calculation and analysis of two or three order statistics in a scanning window. They are designed for processing images corrupted by severe speckle noise with non-symmetrical. (Rayleigh or one-side exponential) distribution laws; impulsive noise can be also present. The proposed filtering algorithms provide trade-off between impulsive noise can be also present. The proposed filtering algorithms provide trade-off between efficient speckle noise suppression, robustness, good edge/detail preservation, low computational complexity, preservation of average level for homogeneous regions of images. Quantitative evaluations of the characteristics of the proposed filter are presented as well as the results of the application to real synthetic aperture radar and ultrasound medical images.

Medical Image Tamper Detection Based on Passive Image Authentication.

PubMed

Ulutas, Guzin; Ustubioglu, Arda; Ustubioglu, Beste; V Nabiyev, Vasif; Ulutas, Mustafa

2017-12-01

Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

Deep learning methods to guide CT image reconstruction and reduce metal artifacts

NASA Astrophysics Data System (ADS)

Gjesteby, Lars; Yang, Qingsong; Xi, Yan; Zhou, Ye; Zhang, Junping; Wang, Ge

2017-03-01

The rapidly-rising field of machine learning, including deep learning, has inspired applications across many disciplines. In medical imaging, deep learning has been primarily used for image processing and analysis. In this paper, we integrate a convolutional neural network (CNN) into the computed tomography (CT) image reconstruction process. Our first task is to monitor the quality of CT images during iterative reconstruction and decide when to stop the process according to an intelligent numerical observer instead of using a traditional stopping rule, such as a fixed error threshold or a maximum number of iterations. After training on ground truth images, the CNN was successful in guiding an iterative reconstruction process to yield high-quality images. Our second task is to improve a sinogram to correct for artifacts caused by metal objects. A large number of interpolation and normalization-based schemes were introduced for metal artifact reduction (MAR) over the past four decades. The NMAR algorithm is considered a state-of-the-art method, although residual errors often remain in the reconstructed images, especially in cases of multiple metal objects. Here we merge NMAR with deep learning in the projection domain to achieve additional correction in critical image regions. Our results indicate that deep learning can be a viable tool to address CT reconstruction challenges.