Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.
Content-based image retrieval; Medical images; Multimodality data; Multidimensional data; Review
The objective of this study was to make a systematic review on the impact of voxel size in cone beam computed tomography (CBCT)-based image acquisition, retrieving evidence regarding the diagnostic outcome of those images. The MEDLINE bibliographic database was searched from 1950 to June 2012 for reports comparing diverse CBCT voxel sizes. The search strategy was limited to English-language publications using the following combined terms in the search strategy: (voxel or FOV or field of view or resolution) and (CBCT or cone beam CT). The results from the review identified 20 publications that qualitatively or quantitatively assessed the influence of voxel size on CBCT-based diagnostic outcome, and in which the methodology/results comprised at least one of the expected parameters (image acquisition, reconstruction protocols, type of diagnostic task, and presence of a gold standard). The diagnostic task assessed in the studies was diverse, including the detection of root fractures, the detection of caries lesions, and accuracy of 3D surface reconstruction and of bony measurements, among others. From the studies assessed, it is clear that no general protocol can be yet defined for CBCT examination of specific diagnostic tasks in dentistry. Rationale in this direction is an important step to define the utility of CBCT imaging.
Dentistry; Cone beam CT; Voxel size; Diagnostic outcome; Image quality
This article summarizes the consensus reached at the Summit on Color in Medical Imaging held at the Food and Drug Administration (FDA) on May 8–9, 2013, co-sponsored by the FDA and ICC (International Color Consortium). The purpose of the meeting was to gather information on how color is currently handled by medical imaging systems to identify areas where there is a need for improvement, to define objective requirements, and to facilitate consensus development of best practices. Participants were asked to identify areas of concern and unmet needs. This summary documents the topics that were discussed at the meeting and recommendations that were made by the participants. Key areas identified where improvements in color would provide immediate tangible benefits were those of digital microscopy, telemedicine, medical photography (particularly ophthalmic and dental photography), and display calibration. Work in these and other related areas has been started within several professional groups, including the creation of the ICC Medical Imaging Working Group.
Color imaging; Medical imaging; Color calibration; Color management
This report summarizes the performance of the Imaging Informatics Professional (IIP) examination from 2009 to 2011 (six exam administrations). Results show that the IIP exam is a reliable measuring instrument that is functioning well to consistently classify candidates as passing or failing. An analysis of the section scores revealed that content in the Image Management, Systems Management, and Clinical Engineering sections of the exam were somewhat more difficult than the content in the other sections. The authors discuss how future candidates may use this information to help hone their study strategies. By all indications, the IIP examination appears to be statistically functioning as a high-quality certification measuring instrument.
ABII; IIP; Certification; Examination; Statistics
Teleradiology allows medical images to be transmitted over electronic networks for clinical interpretation and for improved healthcare access, delivery, and standards. Although such remote transmission of the images is raising various new and complex legal and ethical issues, including image retention and fraud, privacy, malpractice liability, etc., considerations of the security measures used in teleradiology remain unchanged. Addressing this problem naturally warrants investigations on the security measures for their relative functional limitations and for the scope of considering them further. In this paper, starting with various security and privacy standards, the security requirements of medical images as well as expected threats in teleradiology are reviewed. This will make it possible to determine the limitations of the conventional measures used against the expected threats. Furthermore, we thoroughly study the utilization of digital watermarking for teleradiology. Following the key attributes and roles of various watermarking parameters, justification for watermarking over conventional security measures is made in terms of their various objectives, properties, and requirements. We also outline the main objectives of medical image watermarking for teleradiology and provide recommendations on suitable watermarking techniques and their characterization. Finally, concluding remarks and directions for future research are presented.
Digital watermark; Teleradiology; Security
While occupational stress and fatigue have been well described throughout medicine, the radiology community is particularly susceptible due to declining reimbursements, heightened demands for service deliverables, and increasing exam volume and complexity. The resulting occupational stress can be variable in nature and dependent upon a number of intrinsic and extrinsic stressors. Intrinsic stressors largely account for inter-radiologist stress variability and relate to unique attributes of the radiologist such as personality, emotional state, education/training, and experience. Extrinsic stressors may account for intra-radiologist stress variability and include cumulative workload and task complexity. The creation of personalized stress profiles creates a mechanism for accounting for both inter- and intra-radiologist stress variability, which is essential in creating customizable stress intervention strategies. One viable option for real-time occupational stress measurement is voice stress analysis, which can be directly implemented through existing speech recognition technology and has been proven to be effective in stress measurement and analysis outside of medicine. This technology operates by detecting stress in the acoustic properties of speech through a number of different variables including duration, glottis source factors, pitch distribution, spectral structure, and intensity. The correlation of these speech derived stress measures with outcomes data can be used to determine the user-specific inflection point at which stress becomes detrimental to clinical performance.
Stress; Voice analysis; Speech recognition
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.
Image analysis; Image processing; Decision making; Quantitative imaging biomarkers
Efficient workflow is essential for a successful business. However, there is relatively little literature on analytical tools and standards for defining workflow and measuring workflow efficiency. Here, we describe an effort to define a workflow lexicon for medical imaging departments, including the rationale, the process, and the resulting lexicon.
Workflow; Cost-effectiveness; Controlled vocabulary; Data mining; Radiology workflow; Workflow re-engineering
Recent information technology literature, in general, and radiology trade journals, in particular, are rife with allusions to the “cloud” suggesting that moving one’s compute and storage assets into someone else’s data center magically solves cost, performance, and elasticity problems. More likely, one is only trading one set of problems for another, including greater latency (aka slower turnaround times) since the image data must now leave the local area network and travel longer paths via encrypted tunnels. To offset this, an imaging system design is needed that reduces the number of high-latency image transmissions, yet can still leverage cloud strengths. This work explores the requirements for such a design.
DICOM; IHE; Cloud; WADO
The digital imaging and communications in medicine (DICOM) 3.0 standard was first officially ratified by the national electrical manufacturers association in 1993. The success of the DICOM open standard cannot be overstated in its ability to enable an explosion of innovation in the best of breed picture archiving and communication systems (PACS) industry. At the heart of the success of allowing interoperability between disparate systems have been three fundamental DICOM operations: C-MOVE, C-FIND, and C-STORE. DICOM C-MOVE oversees the transfer of DICOM Objects between two systems using C-STORE. DICOM C-FIND negotiates the ability to discover DICOM objects on another node. This paper will discuss the efforts within the DICOM standard to adapt this core functionality to Internet standards. These newer DICOM standards look to address the next generation of PACS challenges including highly distributed mobile acquisition systems and viewing platforms.
Web technology; Wide area network (WAN); Systems integration; PACS integration; Image distribution; Integrating healthcare enterprise (IHE); Internet technology; Enterprise PACS; Digital imaging and communications in medicine (DICOM)
Previous studies suggests that cone beam computerized tomography (CBCT) images could provide reliable information regarding the fate of bone grafts in the maxillofacial region, but no systematic information regarding the standardization of CBCT settings and properties is available, i.e., there is a lack of information on how the images were generated, exported, and analyzed when bone grafts were evaluated. The aim of this study was to (1) do a systematic review on which type of CBCT-based DICOM images have been used for the evaluation of the fate of bone grafts in humans and (2) use a software suggested in the literature to test DICOM-based data sets, exemplifying the effect of variation in selected parameters (windowing/contrast control, plane definition, slice thickness, and number of measured slices) on the final image characteristics. The results from review identified three publications that used CBCT to evaluate maxillofacial bone grafts in humans, and in which the methodology/results comprised at least one of the expected outcomes (image acquisition protocol, image reconstruction, and image generation information). The experimental shows how the influence of information that was missing in the retrieved papers, can influence the reproducibility and the validity of image measurements. Although the use of CBCT-based images for the evaluation of bone grafts in humans has become more common, this does not reflect on a better standardization of the developed studies. Parameters regarding image acquisition and reconstruction, while important, are not addressed in the proper way in the literature, compromising the reproducibility and scientific impact of the studies.
CBCT; Bone graft; Windowing; Plane definition; Slice thickness
The objective of this work is to develop and implement a medical decision-making system for an automated diagnosis and classification of ultrasound carotid artery images. The proposed method categorizes the subjects into normal, cerebrovascular, and cardiovascular diseases. Two contours are extracted for each and every preprocessed ultrasound carotid artery image. Two types of contour extraction techniques and multilayer back propagation network (MBPN) system have been developed for classifying carotid artery categories. The results obtained show that MBPN system provides higher classification efficiency, with minimum training and testing time. The outputs of decision support system are validated with medical expert to measure the actual efficiency. MBPN system with contour extraction algorithms and preprocessing scheme helps in developing medical decision-making system for ultrasound carotid artery images. It can be used as secondary observer in clinical decision making.
US carotid artery image analysis; Contour extraction; Multilayer back propagation network; Neural network classifier; Carotid artery classification; Medical decision-making system; Digital image processing; Image segmentation; Decision support techniques; Neural networks; Carotid artery
Online social networking is an immature, but rapidly evolving industry of web-based technologies that allow individuals to develop online relationships. News stories populate the headlines about various websites which can facilitate patient and doctor interaction. There remain questions about protecting patient confidentiality and defining etiquette in order to preserve the doctor/patient relationship and protect physicians. How much social networking-based communication or other forms of E-communication is effective? What are the potential benefits and pitfalls of this form of communication? Physicians are exploring how social networking might provide a forum for interacting with their patients, and advance collaborative patient care. Several organizations and institutions have set forth policies to address these questions and more. Though still in its infancy, this form of media has the power to revolutionize the way physicians interact with their patients and fellow health care workers. In the end, physicians must ask what value is added by engaging patients or other health care providers in a social networking format. Social networks may flourish in health care as a means of distributing information to patients or serve mainly as support groups among patients. Physicians must tread a narrow path to bring value to interactions in these networks while limiting their exposure to unwanted liability.
E-communication; Doctor patient relationship; Facebook; Sermo
With the increasing availability of high-resolution isotropic three- or four-dimensional medical datasets from sources such as magnetic resonance imaging, computed tomography, and ultrasound, volumetric image visualization techniques have increased in importance. Over the past two decades, a number of new algorithms and improvements have been developed for practical clinical image display. More recently, further efficiencies have been attained by designing and implementing volume-rendering algorithms on graphics processing units (GPUs). In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency. Within the outlined literature review, we have integrated our research results relating to new visualization, classification, enhancement, and multimodal data dynamic rendering. Finally, we illustrate issues related to modern GPU working pipelines, and their applications in volume visualization domain.
Visualization; medical imaging; image processing; volume rendering; raycasting; splatting; shear-warp; shell rendering; texture mapping; Fourier transformation; shading; transfer function; classification; graphics processing unit (GPU)
Commoditization pressures in medicine have risked transforming service provider selection from “survival of the fittest” to “survival of the cheapest.” Quality- and safety-oriented mandates by the Institute of Medicine have led to the creation of a number of data-driven quality-centric initiatives including Pay for Performance and Evidence-Based Medicine. A synergistic approach to creating quantitative accountability in medical service delivery is through the creation of consumer-oriented performance metrics which provide patients with objective data related to individual service provider quality, safety, cost-efficacy, efficiency, and customer service. These performance metrics could in turn be customized to the individual preferences and health care needs of each individual patient, thereby providing an objective methodology for service provider selection while empowering health care consumers.
Patient empowerment; Quality performance; Data mining
Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.
Content-based image retrieval; imaging informatics; information storage and retrieval; digital image management; decision support
Structured reporting offers a number of theoretical advantages, perhaps the most important of which is creation of standardized report databases. The standardized data created can in turn be used to customize data display, report content, historical data retrieval, interpretation analysis, and results communication in both a context and user-specific manner. In addition, these referenceable report databases can be used to facilitate the practice of evidence based medicine, through data-driven meta-analysis and determination of best practice guidelines. This concept will only be realized if the customized data delivery technology provides real and tangible value to end users, accentuates workflow, can be seamlessly integrated into existing information system technologies, and be shown to yield reproducibility of the evidence domain. The time is here for the medical imaging and clinical communities to embrace this vision in order to improve clinical outcomes and patient safety.
Structured reporting; data customization; evidence-based medicine; data mining; decision support
The accurate estimation of point correspondences is often required in a wide variety of medical image-processing applications. Numerous point correspondence methods have been proposed in this field, each exhibiting its own characteristics, strengths, and weaknesses. This paper presents a comprehensive comparison of four automatic methods for allocating corresponding points, namely the template-matching technique, the iterative closest points approach, the correspondence by sensitivity to movement scheme, and the self-organizing maps algorithm. Initially, the four correspondence methods are described focusing on their distinct characteristics and their parameter selection for common comparisons. The performance of the four methods is then qualitatively and quantitatively compared over a total of 132 two-dimensional image pairs divided into eight sets. The sets comprise of pairs of images obtained using controlled geometry protocols (affine and sinusoidal transforms) and pairs of images subject to unknown transformations. The four methods are statistically evaluated pairwise on all image pairs and individually in terms of specific features of merit based on the correspondence accuracy as well as the registration accuracy. After assessing these evaluation criteria for each method, it was deduced that the self-organizing maps approach outperformed in most cases the other three methods in comparison.
Point extraction; automatic point correspondence; iterative closest points; template matching; correspondence by sensitivity to movement; self-organizing maps; features of merit; registration accuracy
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.
Content-based image retrieval (CBIR); pattern recognition; picture archiving and communication systems (PACS); information system integration; data mining; information retrieval; semantic gap
Breast masses due to benign disease and malignant tumors related to breast cancer differ in terms of shape, edge-sharpness, and texture characteristics. In this study, we evaluate a set of 22 features including 5 shape factors, 3 edge-sharpness measures, and 14 texture features computed from 111 regions in mammograms, with 46 regions related to malignant tumors and 65 to benign masses. Feature selection is performed by a genetic algorithm based on several criteria, such as alignment of the kernel with the target function, class separability, and normalized distance. Fisher’s linear discriminant analysis, the support vector machine (SVM), and our strict two-surface proximal (S2SP) classifier, as well as their corresponding kernel-based nonlinear versions, are used in the classification task with the selected features. The nonlinear classification performance of kernel Fisher’s discriminant analysis, SVM, and S2SP, with the Gaussian kernel, reached 0.95 in terms of the area under the receiver operating characteristics curve. The results indicate that improvement in classification accuracy may be gained by using selected combinations of shape, edge-sharpness, and texture features.
Breast masses; breast tumors; mammography; computer-aided diagnosis; feature selection; pattern classification; kernel-based classifiers; shape analysis; edge-sharpness analysis; texture analysis
Despite the increasing use of diagnostic workstations, film reading is still commonplace in most radiology departments all over the world. The purpose of this work is to assess the adoption of image review workstations in a radiology department where the usual primary diagnosis is film-based and cannot be replaced with diagnostic workstations. At our institution, a tertiary care center specialized in diagnostic imaging, a pair of PC-based review workstations running a Digital Imaging and Communications in Medicine (DICOM)-conformant public domain software for image display and analysis were installed in two reading rooms. Studies are automatically routed after acquisition from the picture archiving and communication system (PACS) server to the workstations and remain available for visualization for approximately 15 to 20 days. Data from two radiologists and two technologists collected over a 3-month period were analyzed, including purpose of use, time savings as compared to traditional manual methods, and overall user satisfaction. The results from the analysis presented in this work indicate a high degree of approval from the users, who report significant timesavings in numerous circumstances, in particular when it comes to discussing findings with referring physicians whenever films are not available. It also enriches communication between radiologists, facilitating peer review on the telephone when one of them has questions at the outcome of any given study. One of the main advantages associated with the system is the possibility of using it as a powerful tool for teaching and research. In conclusion, even when primary diagnosis is performed on film, the availability of a PACS for review can be helpful to enhance communication with referring physicians, as well as technologists and radiologists’ efficiency. Our experience shows that it is possible to implement such a system using low-cost or freely available components without compromising ease of use while keeping costs down, which is a major concern in developing countries.
The purpose of this review is to further inform radiologists, physicists, technologists, and engineers working with digital image display devices of issues related to human perception. This article will briefly review the effects of several factors in human perception that are specifically relevant to a digital display environment. These factors include the following: the spatial and contrast resolution of the display device; back-ground luminance level and luminance range of the display system; brightness uniformity; extraneous light in the reading room; displayed field size; viewing distance; image motion and monitor flickering; signal to noise ratio of the displayed image; magnification functions; and the user interface. After reviewing the perception study results, a checklist of desirable features and quality assurance issues for a digital display workstation are presented as an appendix.
picture archiving and communications systems (PACS); quality assurance/control; visual perception; evaluation