Search tips
Search criteria

Results 26-50 (1458)

Clipboard (0)

Select a Filter Below

Year of Publication
more »
26.  Data-Driven Decision Support for Radiologists: Re-using the National Lung Screening Trial Dataset for Pulmonary Nodule Management 
Journal of Digital Imaging  2014;28(1):18-23.
Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.
PMCID: PMC4305063  PMID: 24965276
Decision support; Data mining; Decision support techniques; Web technology
27.  Integrated Image Data and Medical Record Management for Rare Disease Registries. A General Framework and its Instantiation to the German Calciphylaxis Registry 
Journal of Digital Imaging  2014;27(6):702-713.
Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany. With respect to the study management tool that is already successfully operating at the Clinical Trial Center Aachen, the Google Web Toolkit was chosen with Hibernate and Gilead connecting a MySQL database management system. Image and signal data integration and processing is supported by Apache Commons FileUpload-Library and ImageJ-based Java code, respectively. As a proof of concept, the framework is instantiated to the German Calciphylaxis Registry. The framework is composed of five mandatory core modules: (1) Data Core, (2) EDC, (3) Access Control, (4) Audit Trail, and (5) Terminology as well as six optional modules: (6) Binary Large Object (BLOB), (7) BLOB Analysis, (8) Standard Operation Procedure, (9) Communication, (10) Pseudonymization, and (11) Biorepository. Modules 1–7 are implemented in the German Calciphylaxis Registry. The proposed RDR framework is easily instantiated and directly integrates image management and analysis. As open source software, it may assist improved data collection and analysis of rare diseases in near future.
PMCID: PMC4391063  PMID: 24865858
Clinical trial; Rare disease registry; Electronic data capture; Data management; Image management; Image processing; Image annotation
28.  Secured Telemedicine Using Region-Based Watermarking with Tamper Localization 
Journal of Digital Imaging  2014;27(6):737-750.
Medical images exchanged over public networks require a methodology to provide confidentiality for the image, authenticity of the image ownership and source of origin, and image integrity verification. To provide these three security requirements, we propose in this paper a region-based algorithm based on multiple watermarking in the frequency and spatial domains. Confidentiality and authenticity are provided by embedding robust watermarks in the region-of-non-interest (RONI) of the image using a blind scheme in the discrete wavelet transform and singular value decomposition domain (DWT-SVD). On the other hand, integrity is provided by embedding local fragile watermarks in the region-of-interest (ROI) of the image using a reversible scheme in the spatial domain. The integrity provided by the proposed algorithm is implemented on a block-level of the partitioned-image, thus enabling localized detection of tampered regions. The algorithm was evaluated with respect to imperceptibility, robustness, capacity, and tamper localization capability, using MRI, Ultrasound, and X-ray gray-scale medical images. Performance results demonstrate the effectiveness of the proposed algorithm in providing the required security services for telemedicine applications.
PMCID: PMC4391064  PMID: 24874408
Telemedicine; Confidentiality; Authenticity; Integrity; Tamper localization; Medical image transmission; DWT; SVD; Watermarking
29.  Watermarking Techniques used in Medical Images: a Survey 
Journal of Digital Imaging  2014;27(6):714-729.
The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients’ privacy be protected. As a result of this, there is a need for medical image watermarking (MIW). However, MIW needs to be performed with special care for two reasons. Firstly, the watermarking procedure cannot compromise the quality of the image. Secondly, confidential patient information embedded within the image should be flawlessly retrievable without risk of error after image decompressing. Despite extensive research undertaken in this area, there is still no method available to fulfill all the requirements of MIW. This paper aims to provide a useful survey on watermarking and offer a clear perspective for interested researchers by analyzing the strengths and weaknesses of different existing methods.
PMCID: PMC4391065  PMID: 24871349
Medical image watermarking; Fragile watermarking; Robust watermarking; Reversible watermarking; Medical confidentiality; Authentication
30.  Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images 
Journal of Digital Imaging  2014;27(6):833-847.
The level set approach to segmentation of medical images has received considerable attention in recent years. Evolving an initial contour to converge to anatomical boundaries of an organ or tumor is a very appealing method, especially when it is based on a well-defined mathematical foundation. However, one drawback of such evolving method is its high computation time. It is desirable to design and implement algorithms that are not only accurate and robust but also fast in execution. Bresson et al. have proposed a variational model using both boundary and region information as well as shape priors. The latter can be a significant factor in medical image analysis. In this work, we combine the variational model of level set with a multi-resolution approach to accelerate the processing. The question is whether a multi-resolution context can make the segmentation faster without affecting the accuracy. As well, we investigate the question whether a premature convergence, which happens in a much shorter time, would reduce accuracy. We examine multiple semiautomated configurations to segment the prostate gland in T2W MR images. Comprehensive experimentation is conducted using a data set of a 100 patients (1,235 images) to verify the effectiveness of the multi-resolution level set with shape priors. The results show that the convergence speed can be increased by a factor of ≈ 2.5 without affecting the segmentation accuracy. Furthermore, a premature convergence approach drastically increases the segmentation speed by a factor of ≈ 17.9.
PMCID: PMC4391066  PMID: 24865859
Image segmentation; MR imaging; Image processing; Prostate segmentation; Multi-resolution; Level set segmentation
31.  Automatic Cardiac Segmentation Using Semantic Information from Random Forests 
Journal of Digital Imaging  2014;27(6):794-804.
We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The superpixels containing RV regions constitute the region of interest (ROI) which is used to segment the actual RV. Probability maps are generated for each ROI pixel using a second set of RF classifiers which give the probabilities of each pixel belonging to RV or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low-level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that compared to conventional method our algorithm achieves superior performance due to the inclusion of semantic knowledge and context information.
PMCID: PMC4391067  PMID: 24895064
Automatic segmentation; MRI; Right ventricle; Graph cut; Semantic information
32.  The Effectiveness of Service Delivery Initiatives at Improving Patients’ Waiting Times in Clinical Radiology Departments: A Systematic Review 
Journal of Digital Imaging  2014;27(6):751-778.
We reviewed the literature for the impact of service delivery initiatives (SDIs) on patients’ waiting times within radiology departments. We searched MEDLINE, EMBASE, CINAHL, INSPEC and The Cochrane Library for relevant articles published between 1995 and February, 2013. The Cochrane EPOC risk of bias tool was used to assess the risk of bias on studies that met specified design criteria. Fifty-seven studies met the inclusion criteria. The types of SDI implemented included extended scope practice (ESP, three studies), quality management (12 studies), productivity-enhancing technologies (PETs, 29 studies), multiple interventions (11 studies), outsourcing and pay-for-performance (one study each). The uncontrolled pre- and post-intervention and the post-intervention designs were used in 54 (95 %) of the studies. The reporting quality was poor: many of the studies did not test and/or report the statistical significance of their results. The studies were highly heterogeneous, therefore meta-analysis was inappropriate. The following type of SDIs showed promising results: extended scope practice; quality management methodologies including Six Sigma, Lean methodology, and continuous quality improvement; productivity-enhancing technologies including speech recognition reporting, teleradiology and computerised physician order entry systems. We have suggested improved study design and the mapping of the definitions of patient waiting times in radiology to generic timelines as a starting point for moving towards a situation where it becomes less restrictive to compare and/or pool the results of future studies in a meta-analysis.
PMCID: PMC4391068  PMID: 24888629
Systematic review; Humans; Radiology Department, Hospital; Radiology Information Systems/is [Instrumentation]; Radiology Information Systems/og [Organization & Administration]; Radiology Information Systems
33.  Pilot Study: Evaluation of Dual-Energy Computed Tomography Measurement Strategies for Positron Emission Tomography Correlation in Pancreatic Adenocarcinoma 
Journal of Digital Imaging  2014;27(6):824-832.
We sought to determine whether dual-energy computed tomography (DECT) measurements correlate with positron emission tomography (PET) standardized uptake values (SUVs) in pancreatic adenocarcinoma, and to determine the optimal DECT imaging variables and modeling strategy to produce the highest correlation with maximum SUV (SUVmax). We reviewed 25 patients with unresectable pancreatic adenocarcinoma seen at Mayo Clinic, Scottsdale, Arizona, who had PET–computed tomography (PET/CT) and enhanced DECT performed the same week between March 25, 2010 and December 9, 2011. For each examination, DECT measurements were taken using one of three methods: (1) average values of three tumor regions of interest (ROIs) (method 1); (2) one ROI in the area of highest subjective DECT enhancement (method 2); and (3) one ROI in the area corresponding to PET SUVmax (method 3). There were 133 DECT variables using method 1, and 89 using the other methods. Univariate and multivariate analysis regression models were used to identify important correlations between DECT variables and PET SUVmax. Both R2 and adjusted R2 were calculated for the multivariate model to compensate for the increased number of predictors. The average SUVmax was 5 (range, 1.8–12.0). Multivariate analysis of DECT imaging variables outperformed univariate analysis (r = 0.91; R2 = 0.82; adjusted R2 = 0.75 vs r < 0.58; adjusted R2 < 0.34). Method 3 had the highest correlation with PET SUVmax (R2 = 0.82), followed by method 1 (R2 = 0.79) and method 2 (R2 = 0.57). DECT thus has clinical potential as a surrogate for, or as a complement to, PET in patients with pancreatic adenocarcinoma.
PMCID: PMC4391069  PMID: 24994547
Cancer; Dual-energy CT; Informatics; Pancreas; Pancreatic adenocarcinoma; PET; PET/CT
34.  Automated Classification of Radiology Reports to Facilitate Retrospective Study in Radiology 
Journal of Digital Imaging  2014;27(6):730-736.
Retrospective research is an import tool in radiology. Identifying imaging examinations appropriate for a given research question from the unstructured radiology reports is extremely useful, but labor-intensive. Using the machine learning text-mining methods implemented in LingPipe [1], we evaluated the performance of the dynamic language model (DLM) and the Naïve Bayesian (NB) classifiers in classifying radiology reports to facilitate identification of radiological examinations for research projects. The training dataset consisted of 14,325 sentences from 11,432 radiology reports randomly selected from a database of 5,104,594 reports in all disciplines of radiology. The training sentences were categorized manually into six categories (Positive, Differential, Post Treatment, Negative, Normal, and History). A 10-fold cross-validation [2] was used to evaluate the performance of the models, which were tested in classification of radiology reports for cases of sellar or suprasellar masses and colloid cysts. The average accuracies for the DLM and NB classifiers were 88.5 % with 95 % confidence interval (CI) of 1.9 % and 85.9 % with 95 % CI of 2.0 %, respectively. The DLM performed slightly better and was used to classify 1,397 radiology reports containing the keywords “sellar or suprasellar mass”, or “colloid cyst”. The DLM model produced an accuracy of 88.2 % with 95 % CI of 2.1 % for 959 reports that contain “sellar or suprasellar mass” and an accuracy of 86.3 % with 95 % CI of 2.5 % for 437 reports of “colloid cyst”. We conclude that automated classification of radiology reports using machine learning techniques can effectively facilitate the identification of cases suitable for retrospective research.
PMCID: PMC4391070  PMID: 24874407
Radiology report classification; Machine learning; Natural language processing; Retrospective studies; Computer analysis; Radiology reporting; Radiology Information Systems (RIS)
36.  The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model 
Journal of Digital Imaging  2014;27(6):692-701.
Knowledge contained within in vivo imaging annotated by human experts or computer programs is typically stored as unstructured text and separated from other associated information. The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation information model is an evolution of the National Institute of Health’s (NIH) National Cancer Institute’s (NCI) Cancer Bioinformatics Grid (caBIG®) AIM model. The model applies to various image types created by various techniques and disciplines. It has evolved in response to the feedback and changing demands from the imaging community at NCI. The foundation model serves as a base for other imaging disciplines that want to extend the type of information the model collects. The model captures physical entities and their characteristics, imaging observation entities and their characteristics, markups (two- and three-dimensional), AIM statements, calculations, image source, inferences, annotation role, task context or workflow, audit trail, AIM creator details, equipment used to create AIM instances, subject demographics, and adjudication observations. An AIM instance can be stored as a Digital Imaging and Communications in Medicine (DICOM) structured reporting (SR) object or Extensible Markup Language (XML) document for further processing and analysis. An AIM instance consists of one or more annotations and associated markups of a single finding along with other ancillary information in the AIM model. An annotation describes information about the meaning of pixel data in an image. A markup is a graphical drawing placed on the image that depicts a region of interest. This paper describes fundamental AIM concepts and how to use and extend AIM for various imaging disciplines.
PMCID: PMC4391072  PMID: 24934452
Big data; Image annotation; Imaging informatics; Image markup; Information resources
37.  Understanding Visual Search Patterns of Dermatologists Assessing Pigmented Skin Lesions Before and After Online Training 
Journal of Digital Imaging  2014;27(6):779-785.
The goal of this investigation was to explore the feasibility of characterizing the visual search characteristics of dermatologists evaluating images corresponding to single pigmented skin lesions (PSLs) (close-ups and dermoscopy) as a venue to improve training programs for dermoscopy. Two Board-certified dermatologists and two dermatology residents participated in a phased study. In phase I, they viewed a series of 20 PSL cases ranging from benign nevi to melanoma. The close-up and dermoscopy images of the PSL were evaluated sequentially and rated individually as benign or malignant, while eye position was recorded. Subsequently, the participating subjects completed an online dermoscopy training module that included a pre- and post-test assessing their dermoscopy skills (phase 2). Three months later, the subjects repeated their assessment on the 20 PSLs presented during phase I of the study. Significant differences in viewing time and eye-position parameters were observed as a function of level of expertise. Dermatologists overall have more efficient search than residents generating fewer fixations with shorter dwells. Fixations and dwells associated with decisions changing from benign to malignant or vice versa from photo to dermatoscopic viewing were longer than any other decision, indicating increased visual processing for those decisions. These differences in visual search may have implications for developing tools to teach dermatologists and residents about how to better utilize dermoscopy in clinical practice.
PMCID: PMC4391073  PMID: 24939005
Telemedicine; Decision making; Diagnostic evaluation; Image perception; Visual search
38.  Patient Dose Management Solution Directly Integrated in the RIS: “Gray Detector” Software 
Journal of Digital Imaging  2014;27(6):786-793.
On X-ray modalities, the information concerning the dose delivered to the patient is usually available in image headers or in structured reports stored in the picture archiving and communication system (PACS). Sometimes this information is sent in the Modality Performed Procedure Step message. By saving the information inside the Radiological Information System, it can be linked to the patient and to his/her episode/request. A software, “Gray Detector,” implementing different and complementary extraction methods was developed. Query/retrieve on images header, Modality Performed Procedure Step message analysis, or the combination of the two methods were used. In order to avoid erroneous dose-protocol association, every accession number is linked to its unique report code, allowing multiple-protocols exam recognition. The adoption of different methods to extract dosimetric information makes it possible to integrate any kind of modality in a vendor/version neutral way. Linking the dosimetric information received from a modality to the patient and to the unique report code solves, for example, common problems in computed tomography exams, where the dosimetric value related to multiple segments/studies on the modality can be associated by the technician who performs the exam only to one accession number corresponding to a single study/segment. Analyses of dosimetric indexes’ dependence on modality type, patient age, technician, and radiologist were performed. Linking dosimetric information to radiological information system data allows a contextualization of the former and helps to optimize the image-quality/dose ratio, thereby making it possible to take a clinical decision that is “patient-centered.”
PMCID: PMC4391074  PMID: 24965275
Radiation dose; Radiology information systems (RIS); Electronic medical record (EMR); Medical informatics applications; Data collection; Information storage and retrieval; Information management
39.  Test–Retest Reproducibility Analysis of Lung CT Image Features 
Journal of Digital Imaging  2014;27(6):805-823.
Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test–retest, the biological range and a feature independence measure. There were 66 (30.14 %) features with concordance correlation coefficient ≥ 0.90 across test–retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R2Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91 % for a size-based feature and 92 % for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test–retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.
Electronic supplementary material
The online version of this article (doi:10.1007/s10278-014-9716-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4391075  PMID: 24990346
Test–retest reproducibility; Lung cancer; CT; Quantitative image features
40.  User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy 
Journal of Digital Imaging  2015;29(2):264-277.
Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians’ expertise and computers’ potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the “strokes” and the “contour”, to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design.
PMCID: PMC4788616  PMID: 26553109
Radiotherapy; Organs at risk; Semi-automatic segmentation; Human-computer interaction; Evaluation; Correlations
41.  The Importance of Human–Computer Interaction in Radiology E-learning 
Journal of Digital Imaging  2015;29(2):195-205.
With the development of cross-sectional imaging techniques and transformation to digital reading of radiological imaging, e-learning might be a promising tool in undergraduate radiology education. In this systematic review of the literature, we evaluate the emergence of image interaction possibilities in radiology e-learning programs and evidence for effects of radiology e-learning on learning outcomes and perspectives of medical students and teachers. A systematic search in PubMed, EMBASE, Cochrane, ERIC, and PsycInfo was performed. Articles were screened by two authors and included when they concerned the evaluation of radiological e-learning tools for undergraduate medical students. Nineteen articles were included. Seven studies evaluated e-learning programs with image interaction possibilities. Students perceived e-learning with image interaction possibilities to be a useful addition to learning with hard copy images and to be effective for learning 3D anatomy. Both e-learning programs with and without image interaction possibilities were found to improve radiological knowledge and skills. In general, students found e-learning programs easy to use, rated image quality high, and found the difficulty level of the courses appropriate. Furthermore, they felt that their knowledge and understanding of radiology improved by using e-learning. In conclusion, the addition of radiology e-learning in undergraduate medical education can improve radiological knowledge and image interpretation skills. Differences between the effect of e-learning with and without image interpretation possibilities on learning outcomes are unknown and should be subject to future research.
PMCID: PMC4788615  PMID: 26464115
Human–computer interaction; E-learning; Radiology; Education
42.  Entropy-Based Straight Kernel Filter for Echocardiography Image Denoising 
Journal of Digital Imaging  2014;27(5):610-624.
A new filter has been proposed with the aim of eliminating speckle noise from 2D echocardiography images. This speckle noise has to be eliminated to avoid the pseudo prediction of the underlying anatomical facts. The proposed filter uses entropy parameter to measure the disorganized occurrence of noise pixel in each row and column and to increase the image visibility. Straight kernels with 3 pixels each are chosen for the filtering process, and the filter is slided over the image to eliminate speckle. The peak signal-to-noise ratio (PSNR) is obtained in the range of 147 dB, and the root mean square error (RMSE) is very low of approximately 0.15. The proposed filter is implemented on 36 echocardiography images, and the filter has the competence to illuminate the actual anatomical facts without degrading the edges.
PMCID: PMC4171422  PMID: 24838117
Echocardiography; Speckle noise; Kernel; Entropy; Additive noise; Variance
43.  A Reliable, Low-Cost Picture Archiving and Communications System for Small and Medium Veterinary Practices Built Using Open-Source Technology 
Journal of Digital Imaging  2014;27(5):563-570.
Picture Archiving and Communications Systems (PACS) are the most needed system in a modern hospital. As an integral part of the Digital Imaging and Communications in Medicine (DICOM) standard, they are charged with the responsibility for secure storage and accessibility of the diagnostic imaging data. These machines need to offer high performance, stability, and security while proving reliable and ergonomic in the day-to-day and long-term storage and retrieval of the data they safeguard. This paper reports the experience of the authors in developing and installing a compact and low-cost solution based on open-source technologies in the Veterinary Teaching Hospital for the University of Torino, Italy, during the course of the summer of 2012. The PACS server was built on low-cost x86-based hardware and uses an open source operating system derived from Oracle OpenSolaris (Oracle Corporation, Redwood City, CA, USA) to host the DCM4CHEE PACS DICOM server (DCM4CHEE, This solution features very high data security and an ergonomic interface to provide easy access to a large amount of imaging data. The system has been in active use for almost 2 years now and has proven to be a scalable, cost-effective solution for practices ranging from small to very large, where the use of different hardware combinations allows scaling to the different deployments, while the use of paravirtualization allows increased security and easy migrations and upgrades.
PMCID: PMC4171423  PMID: 24793019
PACS; DICOM; Image storage and retrieval; Open source
44.  Lossless Compression on MRI Images Using SWT 
Journal of Digital Imaging  2014;27(5):594-600.
Medical image compression is one of the growing research fields in biomedical applications. Most medical images need to be compressed using lossless compression as each pixel information is valuable. With the wide pervasiveness of medical imaging applications in health-care settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods. Furthermore, providing random access as well as resolution and quality scalability to the compressed data has become of great utility. Random access refers to the ability to decode any section of the compressed image without having to decode the entire data set. The system proposes to implement a lossless codec using an entropy coder. 3D medical images are decomposed into 2D slices and subjected to 2D-stationary wavelet transform (SWT). The decimated coefficients are compressed in parallel using embedded block coding with optimized truncation of the embedded bit stream. These bit streams are decoded and reconstructed using inverse SWT. Finally, the compression ratio (CR) is evaluated to prove the efficiency of the proposal. As an enhancement, the proposed system concentrates on minimizing the computation time by introducing parallel computing on the arithmetic coding stage as it deals with multiple subslices.
PMCID: PMC4171424  PMID: 24848945
Stationary wavelet transform; EBCOT; Arithmetic coding; Bit plane coding
45.  Evaluation of Low-Cost Telemammography Screening Configurations: A Comparison with Film-Screen Readings in Vulnerable Areas 
Journal of Digital Imaging  2014;27(5):679-686.
The aim of this study was to evaluate the diagnostic accuracy for detecting breast cancer using different telemammography configurations, including combinations of both low-cost capture devices and consumer-grade color displays. At the same time, we compared each of these configurations to film-screen readings. This study used a treatment-by-reader-by-case factorial design. The sample included 70 mammograms with 34 malignant cases. The readers consisted of four radiologists who classified all of the cases according to the categories defined by the Breast Imaging Reporting and Data System (BI-RADS). The evaluated capture devices included a specialized film digitizer and a digital camera, and the evaluated displays included liquid crystal display (LCD) and light-emitting diode (LED) consumer-grade color displays. Receiver operating characteristic curves, diagnostic accuracy (measured as the area under these curves), accuracy of the composition classification, sensitivity, specificity, and the degree of agreement between readers in the detection of malignant cases were also evaluated. Comparisons of diagnostic accuracy between film-screen and the different combinations of digital configurations showed no significant differences for nodules, calcifications, and asymmetries. In addition, no differences were observed in terms of sensibility or specificity when the degree of malignancy using the film-screen method was compared to that provided with digital configurations. Similar results were observed for the classification of breast composition. Furthermore, all observed reader agreements of malignant detection between film-screen and digital configurations were substantial. These findings indicate that the evaluated digital devices showed comparable diagnostic accuracy to the reference treatment (film-screen).
PMCID: PMC4171425  PMID: 24802372
Mammography; Teleradiology; Display device; Observer performance; ROC-based analysis; Sensitivity and specificity
46.  Computerized Breast Mass Detection Using Multi-Scale Hessian-Based Analysis for Dynamic Contrast-Enhanced MRI 
Journal of Digital Imaging  2014;27(5):649-660.
This study aimed to investigate a computer-aided system for detecting breast masses using dynamic contrast-enhanced magnetic resonance imaging for clinical use. Detection performance of the system was analyzed on 61 biopsy-confirmed lesions (21 benign and 40 malignant lesions) in 34 women. The breast region was determined using the demons deformable algorithm. After the suspicious tissues were identified by kinetic feature (area under the curve) and the fuzzy c-means clustering method, all breast masses were detected based on the rotation-invariant and multi-scale blob characteristics. Subsequently, the masses were further distinguished from other detected non-tumor regions (false positives). Free-response operating characteristics (FROC) curve and detection rate were used to evaluate the detection performance. Using the combined features, including blob, enhancement, morphologic, and texture features with 10-fold cross validation, the mass detection rate was 100 % (61/61) with 15.15 false positives per case and 91.80 % (56/61) with 4.56 false positives per case. In conclusion, the proposed computer-aided detection system can help radiologists reduce inter-observer variability and the cost associated with detection of suspicious lesions from a large number of images. Our results illustrated that breast masses can be efficiently detected and that enhancement and morphologic characteristics were useful for reducing non-tumor regions.
PMCID: PMC4171426  PMID: 24687641
Breast; Magnetic resonance imaging; Detection; Morphologic; Hessian
47.  Quantitative Detection of Cirrhosis: Towards the Development of Computer-Assisted Detection Method 
Journal of Digital Imaging  2014;27(5):601-609.
There are distinct morphologic features of cirrhosis on CT examinations; however, such impressions may be subtle or subjective. The purpose of this study is to build a computer-aided diagnosis (CAD) method to help radiologists with this diagnosis. One hundred sixty-seven abdominal CT examinations were randomly divided into training (n = 88) and validation (n = 79) sets. Livers were analyzed for morphological markers of cirrhosis and logistic regression models were created. Using the area under curve (AUC) for model performance, the best model had 0.89 for the training set and 0.85 for the validation set. For radiology reports, sensitivity of reporting cirrhosis was 0.45 and specificity 0.99. Using the predictive model adjunctively, radiologists’ sensitivity increased to 0.63 and specificity slightly decreased to 0.97. This study demonstrates that quantifying morphological features in livers may be utilized for diagnosing cirrhosis and for developing a CAD method for it.
PMCID: PMC4171427  PMID: 24811859
Computed tomography; Quantification; Computer-assisted image interpretation; Computer aided diagnosis; Cirrhosis
48.  MyCases—A Portable Application for Radiologic Case Collections 
Journal of Digital Imaging  2014;27(5):557-562.
Radiologists come across interesting patient cases almost every day. This work proposes a novel case database server for quick and easy storage of such cases including whole image series, patient data, and annotations. Cases can be added to the database by saving DICOM images into a predefined directory on the local network. The application automatically extracts patient and study data from the DICOM header and saves it in the database while images are stored as anonymized JPEG files. Users can mark their cases as private or public (visible to all users). Different data fields for annotations and categorization of a case are available. The user frontend also provides several retrieval mechanisms allowing for browsing the cases and performing different kinds of search queries. The stored series can be scrolled interactively in the form of scrollable image stacks. The project is realized as a web-based application using a portable web and database server software package (XAMPP). This makes the system very lightweight and easy to run on almost any desktop computer, even from a USB flash drive, without the need for deeper IT knowledge and administrative rights.
PMCID: PMC4171428  PMID: 24788304
Image libraries; Information storage and retrieval; Internet technology; Web technology
49.  Creation and Implementation of Department-Wide Structured Reports: An Analysis of the Impact on Error Rate in Radiology Reports 
Journal of Digital Imaging  2014;27(5):581-587.
The purpose of this study was to evaluate and compare textual error rates and subtypes in radiology reports before and after implementation of department-wide structured reports. Randomly selected radiology reports that were generated following the implementation of department-wide structured reports were evaluated for textual errors by two radiologists. For each report, the text was compared to the corresponding audio file. Errors in each report were tabulated and classified. Error rates were compared to results from a prior study performed prior to implementation of structured reports. Calculated error rates included the average number of errors per report, average number of nongrammatical errors per report, the percentage of reports with an error, and the percentage of reports with a nongrammatical error. Identical versions of voice-recognition software were used for both studies. A total of 644 radiology reports were randomly evaluated as part of this study. There was a statistically significant reduction in the percentage of reports with nongrammatical errors (33 to 26 %; p = 0.024). The likelihood of at least one missense omission error (omission errors that changed the meaning of a phrase or sentence) occurring in a report was significantly reduced from 3.5 to 1.2 % (p = 0.0175). A statistically significant reduction in the likelihood of at least one comission error (retained statements from a standardized report that contradict the dictated findings or impression) occurring in a report was also observed (3.9 to 0.8 %; p = 0.0007). Carefully constructed structured reports can help to reduce certain error types in radiology reports.
PMCID: PMC4171430  PMID: 24859725
Radiology; Structured reports; Errors
50.  Digitized Whole Slides for Breast Pathology Interpretation: Current Practices and Perceptions 
Journal of Digital Imaging  2014;27(5):642-648.
Digital whole slide imaging (WSI) is an emerging technology for pathology interpretation; however, little is known about pathologists’ practice patterns or perceptions regarding WSI. A national sample (N = 252) of pathologists from New Hampshire, Vermont, Washington, Oregon, Arizona, Alaska, Maine, and Minnesota were surveyed in this cross-sectional study (2011–2013). The survey included questions on pathologists’ experience, WSI practice patterns, and perceptions using a six-point Likert scale. Agreement was summarized with descriptive statistics to characterize pathologists’ use and perceptions of WSI. The majority of participating pathologists were males (63 %) between 40 and 59 years of age (70 %) and not affiliated with an academic medical center (72 %). Experience with WSI was reported by 49 %. Types of use reported included CME/board exams/teaching (28 %), tumor board/clinical conference (22 %), archival purposes (6 %), consultative diagnosis (4 %), research (4 %), and other uses (12 %). Most respondents (79 %) agreed that accurate diagnoses can be made with this technology, and that WSI is useful for obtaining a second opinion (88 %). However, 78 % of pathologists agreed that digital slides are too slow for routine clinical interpretation. Fifty-nine percent agreed that the benefits of WSI outweigh concerns. The respondents were equally split as to whether they would like to adopt WSI (51 %) or not (49 %). About half of pathologists reported experience with the WSI technology, largely for CME, licensure/board exams, and teaching. Positive perceptions regarding WSI slightly outweigh negative perceptions. Understanding practice patterns with WSI as dissemination advances may facilitate concordance of perceptions with adoption of the technology.
PMCID: PMC4171431  PMID: 24682769
Digital whole slide imaging; Pathology

Results 26-50 (1458)