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Integrating CAD modules in a PACS environment using a wide computing infrastructure
Abstract
Purpose:
The aim of this paper is to describe a project designed to achieve a total integration of different CAD algorithms into the PACS environment by using a wide computing infrastructure.
Methods:
The aim is to build a system for the entire region of Galicia, Spain, to make CAD accessible to multiple hospitals by employing different PACSs and clinical workstations. The new CAD model seeks to connect different devices (CAD systems, acquisition modalities, workstations and PACS) by means of networking based on a platform that will offer different CAD services. This paper describes some aspects related to the health services of the region where the project was developed, CAD algorithms that were either employed or selected for inclusion in the project, and several technical aspects and results.
Results:
We have built a standard-based platform with which users can request a CAD service and receive the results in their local PACS. The process runs through a web interface that allows sending data to the different CAD services. A DICOM SR object is received with the results of the algorithms stored inside the original study in the proper folder with the original images.
Conclusions:
As a result, a homogeneous service to the different hospitals of the region will be offered. End users will benefit from a homogeneous workflow and a standardised integration model to request and obtain results from CAD systems in any modality, not dependant on commercial integration models. This new solution will foster the deployment of these technologies in the entire region of Galicia.
... But it also clearly consolidated a fact of even greater dimensions: technology and digitalization began to grow and to occupy more space every day in the departments and specialized journals, and they came to dominate definitively the diagnostic imaging. Because of this fast and continuous way of research, the techniques of diagnostic imaging are today "mestizo" products in which the principles of radiology and computer science have shown that both R & D earthquakes could be fused and supplemented to give a prestigious image of science [2]. ...
... Finally, a project was designed to form an integrated algorithm which integrated CAD systems with PACS using big computing infrastructure. 9 This work aimed to create a system where users can request a CAD service and get the outcome to their PACS. This system helped the end users request to obtain the results in any modality. ...
Purpose: The purpose of this article was to represent the first experience of applying a picture archiving and communication system (PACS) at the Universiti Putra Malaysia with the cooperation of Universiti Teknologi MARA hospital, and to analyze the applicability of PACS, its impact on health care, its benefits to medical employees, and propose a prototype application of PACS.
Methods: The main PACS components were discussed, HL7 and DICOM standards were introduced, and a prototype of WebXA application was proposed.
Results: The results of WebXA revealed the ability of this application to retrieve, store, and display angiography images on a web browser anywhere, as long as an Internet connection is provided.
Conclusion: This article presented PACS with its components and standards, a prototype application was discussed and evaluated, and a few recommendations have been provided for more improvements in the future.
The purpose of this work is to describe a chest radiography computer-Aided diagnostic (CAD) scheme designed to analyze the chest radiographs performed in the framework of the Galician (Spain) Health Service (GHS), including the radiographs that are not reported by the radiologists. The final goal of this CAD system is its integration in the GHS daily clinical environment, with a feasible RIS-PACS-CAD and EHR-CAD integration model. The database of the study included 55 chest radiographies with 64 nodules/lung cancer. This database was used to develop and test the CAD system in our research laboratory. Free-Response Receiver Operating Characteristic (FROC) curves were employed to evaluate the performance of the CAD system. An independent database was employed to evaluate the performance of the CAD system by external radiologists. After the application of a linear classifier, our CAD system achieved a sensitivity of 70% with a false positive rate between 4 and 6 per image depending on the testing database. When compared with other commercial systems, our CAD scheme achieved similar performance results. Therefore, our CAD scheme could be utilized to help radiologists in the detection of lung nodules in chest radiography, and therefore, it can be integrated in the clinical practice.
The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the
major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated
detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for
clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies
K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way
that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in
aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans
and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the
proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar
K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early
stage and can suggest a better drug for preventing further retinal damage.
The performance of a computer-aided diagnosis system depends on the feature set used in it. This paper shows the results of image feature selection experiments. We evaluated 210 features to look for the optimum feature set. For the purpose, a forward stepwise selection approach was employed. The area under the receiver operating characteristic (ROC) curve was adopted to evaluate the performance of each feature set. Analysis of the optimally selected feature set is given and the experiments using 247 chest x-ray images are also shown.
Malignant melanoma and basal cell carcinoma are common skin tumours. For skin lesion classification it is necessary to determine and calculate different attributes such as exact location, size, shape and appearance. It has been noted that illumination, dermoscopic gel and features such as blood vessels, hair and skin lines can affect border detection. Thus, there is a need for approaches that minimise the effect of such features. This study aims to detect multiple borders from dermoscopy with increased sensitivity and specificity for the detection of early melanoma and other pigment lesions. An automated border detection method based on minimising geodesic active contour energy and incorporating homomorphic, median and anisotropic diffusion (AD) filtering, as well as top-hat watershed transformation is used. Extensive experiments on various skin lesions were conducted on real dermoscopic images and proved to enhance accurate border detection and improve the segmentation result by reducing the error rate from 12.42% to 7.23%. The results have validated the integrated enhancement of numerous lesion border detections with the noise removal algorithm which may contribute to skin cancer classification.
Computer‐aided diagnostic (CAD) schemes have been developed to assist radiologists in the early detection of lung cancer in radiographs and computed tomography (CT) images. In order to improve sensitivity for nodule detection, many researchers have employed a filter as a preprocessing step for enhancement of nodules. However, these filters enhance not only nodules, but also other anatomic structures such as ribs, blood vessels, and airway walls. Therefore, nodules are often detected together with a large number of false positives caused by these normal anatomic structures. In this study, we developed three selective enhancement filters for dot, line, and plane which can simultaneously enhance objects of a specific shape (for example, dot‐like nodules) and suppress objects of other shapes (for example, line‐like vessels). Therefore, as preprocessing steps, these filters would be useful for improving the sensitivity of nodule detection and for reducing the number of false positives. We applied our enhancement filters to synthesized images to demonstrate that they can selectively enhance a specific shape and suppress other shapes. We also applied our enhancement filters to real two‐dimensional (2D) and three‐dimensional (3D) CT images to show their effectiveness in the enhancement of specific objects in real medical images. We believe that the three enhancement filters developed in this study would be useful in the computerized detection of cancer in 2D and 3D medical images.
Supplement 23 to Digital Imaging and Communications in Medicine (DICOM) is an introduction to the structured reporting (SR) classes, which are used for transmission and storage of clinical documents. The SR classes fully support both conventional free-text reports and structured information, thus enhancing the precision, clarity, and value of clinical documentation. In addition, the SR standard provides the capability to link text and other data to particular images or waveforms and to store the coordinates of findings. In other words, SR documents not only describe the specific features contained in images or waveforms but can also refer to any number of images or waveforms. Accordingly, SR bridges the traditional gap between imaging systems and information systems. Furthermore, SR plays an essential role in Integrating the Healthcare Enterprise by providing healthcare practitioners with an effective tool that encompasses a variety of clinical contexts. Harmonization of DICOM SR and the Health Level Seven clinical document architecture standard is under way.
Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists' image interpretation by improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. In this article, a number of CAD schemes are presented, with emphasis on potential clinical applications. These schemes include: (1) detection and classification of lung nodules on digital chest radiographs; (2) detection of nodules in low dose CT; (3) distinction between benign and malignant nodules on high resolution CT; (4) usefulness of similar images for distinction between benign and malignant lesions; (5) quantitative analysis of diffuse lung diseases on high resolution CT; and (6) detection of intracranial aneurysms in magnetic resonance angiography. Because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations, it is likely that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century.
Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.
The purpose of this study was to evaluate the diagnostic performance of a computer-aided diagnosis (CAD) system, on the detection of pulmonary nodules in multidetector row computed tomography (MDCT) images, by using two different MDCT scanners. The computerized scheme was based on the iris filter. We have collected CT cases of patients with pulmonary nodules. We have included in the study one hundred and thirty-two calcified and noncalcified nodules, measuring 4-30 mm in diameter. CT examinations were performed by using two different equipments: a CT scanner (SOMATOM Emotion 6), and a dual-source computed tomography system (SOMATOM Definition) (Siemens Medical System, Forchheim, Germany), with the following parameters: collimation, 6x1.0mm (Emotion 6); and 64×0.6mm (Definition); 100-130 kV; 70-110 mAs. Data were reconstructed with a slice thickness of 1.25mm (Emotion 6) and 1mm (Definition). True positive cases were determined by an independent interpretation of the study by three experienced chest radiologists, the panel decision being used as the reference standard. Free-response Receiver Operating Characteristic curves, sensitivity and number of false-positive per scan, were calculated. Our CAD scheme, for the test set of the study, yielded a sensitivity of 80%, with an average of 5.2 FPs per examination. At an average false positive rate of 9 per scan, our CAD scheme achieved sensitivities of 94% for all nodules, 94.5% for solid, 80% for non-solid, 84% for spiculated, and 97% for non-spiculated nodules. These encouraging results suggest that our CAD system, advocated as a second reader, may help radiologists in the detection of lung nodules in MDCT.
Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.
Objetives
Recent years have seen growing interest in the development of algorithms for computer-assisted diagnosis (CAD) for the detection of pulmonary nodules on both plainfilm radiographs and computed tomography (CT) studies. The purpose of CAD algorithms in this context is to alert radiologists to suspicious radioopacities that might represent cancer in the images. We are developing a CAD system for the detection of pulmonary nodules on helical CT images.
Material and methods
We collected cases of patients with pulmonary nodules examined with helical CT. A total of 64 nodules, including both calcified and noncalcified lesions, ranging from 3 to 30 mm in diameter were included in the study. Studies were acquired on one 4-slice and one 64-slice CT scanners. Three chest radiologists at two institutions interpreted the studies to determine whether pulmonary nodules were present. We calculated the sensitivity and the number of false positives per image to evaluate the CAD system.
Results
We have developed and evaluated an algorithm for the automatic detection of pulmonary nodules on CT images. For a sensitivity of 76%, the false-positive rate was 1.3 per image.
Conclusions
Our preliminary results suggest that the system might be useful for radiologists in the detection of pulmonary nodules on helical CT images.
The purpose of this article is to evaluate the impact on the diagnosis of breast cancer of implementing full-field digital mammography (FFDM) in a multidisciplinary breast pathology unit and, 1 year later, the addition of a computer-aided detection (CAD) system.
A total of 13,453 mammograms performed between January and July of the years 2004, 2006, and 2007 were retrospectively reviewed using conventional mammography, digital mammography, and digital mammography plus CAD techniques. Mammograms were classified into two subsets: screening and diagnosis. Variables analyzed included cancer detection rate, rate of in situ carcinoma, tumor size at detection, biopsy rate, and positive predictive value of biopsy.
FFDM increased the cancer detection rate, albeit not statistically significantly. The detection rate of in situ carcinoma increased significantly using FFDM plus CAD compared with conventional technique (36.8% vs 6.7%; p = 0.05 without Bonferroni statistical correction) for the screening dataset. Relative to conventional mammography, tumor size at detection decreased with digital mammography (T1, 61.5% vs 88%; p = 0.018) and with digital mammography plus CAD (T1, 79.7%; p = 0.03 without Bonferroni statistical correction). Biopsy rates in the general population increased significantly using CAD (10.6/1000 for conventional mammography, 14.7/1000 for digital mammography, and 17.9/1000 for digital mammography plus CAD; p = 0.02). The positive predictive value of biopsy decreased slightly, but not significantly, for both subsets.
The incorporation of new techniques has improved the performance of the breast unit by increasing the overall detection rates and earlier detection (smaller tumors), both leading to an increase in interventionism.
Picture Archiving and Communication System (PACS) is a mature technology in health care delivery for daily clinical imaging service and data management. Computer-aided detection and diagnosis (CAD) utilizes computer methods to obtain quantitative measurements from medical images and clinical information to assist clinicians to assess a patient's clinical state more objectively. CAD needs image input and related information from PACS to improve its accuracy; and PACS benefits from CAD results online and available at the PACS workstation as a second reader to assist physicians in the decision making process. Currently, these two technologies remain as two separate independent systems with only minimal system integration. This paper describes a universal method to integrate CAD results with PACS in its daily clinical environment.
The method is based on Health Level 7 (HL7) and Digital imaging and communications in medicine (DICOM) standards, and Integrating the Healthcare Enterprise (IHE) workflow profiles. In addition, the integration method is Health Insurance Portability and Accountability Act (HIPAA) compliant.
The paper presents (1) the clinical value and advantages of integrating CAD results in a PACS environment, (2) DICOM Structured Reporting formats and some important IHE workflow profiles utilized in the system integration, (3) the methodology using the CAD-PACS integration toolkit, and (4) clinical examples with step-by-step workflows of this integration.
We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives. The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.
Recent years have seen growing interest in the development of algorithms for computer-assisted diagnosis (CAD) for the detection of pulmonary nodules on both plain-film radiographs and computed tomography (CT) studies. The purpose of CAD algorithms in this context is to alert radiologists to suspicious radioopacities that might represent cancer in the images. We are developing a CAD system for the detection of pulmonary nodules on helical CT images.
We collected cases of patients with pulmonary nodules examined with helical CT. A total of 64 nodules, including both calcified and noncalcified lesions, ranging from 3 to 30 mm in diameter were included in the study. Studies were acquired on one 4-slice and one 64-slice CT scanners. Three chest radiologists at two institutions interpreted the studies to determine whether pulmonary nodules were present. We calculated the sensitivity and the number of false positives per image to evaluate the CAD system.
We have developed and evaluated an algorithm for the automatic detection of pulmonary nodules on CT images. For a sensitivity of 76%, the false-positive rate was 1.3 per image.
Our preliminary results suggest that the system might be useful for radiologists in the detection of pulmonary nodules on helical CT images.
This paper describes the approach of the European HELIOS project to integrate image processing tools into ward information systems. The image processing tools are the result of the basic research in image analysis in the Department Medical and Biological Informatics at the German Cancer Research Center. These tools for the analysis of two-dimensional images and three-dimensional data volumes with 3D reconstruction and visualization ae part of the Image Related Services of HELIOS. The HELIOS software engineering environment allows to use the image processing functionality in integrated applications.
IHE is not a standard. It is an initiative that has produced a consensus document that serves as a technical framework within which medical information systems can be designed to interact successfully to accomplish real-world tasks. IHE uses existing standards as tools to achieve integration. Today, these tools are DICOM and HL7, which are sufficient for the tasks described in the current IHE integration profiles. As more integration profiles and tasks are defined in other areas, other standards will be added as necessary.
To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation.
A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis.
At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section.
With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.
To evaluate the use of a computer-aided detection (CAD) system (designed for mammographic mass detection) to help improve mass interpretation and to compare CAD results with independent double-reading results.
Screening mammograms from 500 cases were collected; 125 of these cases were screening-detected cancers, and 125 were interval cancers. Previously obtained screening mammograms (ie, prior mammograms) were available in all cases. All mammograms were analyzed by a CAD system, which detected mass regions and assigned a level of (cancer) suspicion to each mass. Ten experienced screening radiologists read the prior mammograms. For independent interpretation with CAD, the suspicion rating assigned to each finding by the radiologist was weighted with the CAD output at the area of the finding. CAD markers on areas that were not reported by the radiologist were not used. Independent double reading was implemented by using a rule to combine the levels of suspicion assigned to findings by two radiologists. Results were evaluated by using localized-response receiver operating characteristic analysis.
In a total of 141 cases, there was a visible abnormality at the location of the cancer on the prior mammogram, and 115 of these were classified as mass cases. For prior mammograms that depicted masses, the mean sensitivity of the radiologists, as averaged among the false-positive rates lower than 10%, was 39.4%; this increased by 7.0% with CAD and by 10.5% with double reading. Differences among single, double, and CAD readings were statistically significant (P <.001).
Although independent double reading yields the best detection performance, the presence and probability of CAD mass markers can improve mammogram interpretation.
Mammographic film reading for breast screening is a highly demanding visual task involving a detailed visual search for signs of abnormality, which are infrequent and often small or subtle. False-negative cases, in which a cancer is missed by a film reader, are known to occur. Although double reading has proved effective in reducing errors, there is a national shortage of film readers in the screening programme, and recent extensions to the programme have exacerbated this problem. The use of computer-aided detection (CAD) systems could potentially provide a solution by improving individual performance to the extent that double reading is no longer necessary. In this paper, we describe how CAD works, review the relevant literature and examine the strengths and weaknesses of the approach.
The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.
Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a “second opinion” and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms.
Computer-aided diagnosis (CAD) provides a computer output as a "second opinion" in order to assist radiologists in the diagnosis of various diseases on medical images. Currently, a significant research effort is being devoted to the detection and characterization of lung nodules in thin-section computed tomography (CT) images, which represents one of the newest directions of CAD development in thoracic imaging. We describe in this article the current status of the development and evaluation of CAD schemes for the detection and characterization of lung nodules in thin-section CT. We also review a number of observer performance studies in which it was attempted to assess the potential clinical usefulness of CAD schemes for nodule detection and characterization in thin-section CT. Whereas current CAD schemes for nodule characterization have achieved high performance levels and would be able to improve radiologists' performance in the characterization of nodules in thin-section CT, current schemes for nodule detection appear to report many false positives, and, therefore, significant efforts are needed in order further to improve the performance levels of current CAD schemes for nodule detection in thin-section CT.
Editorial and special issue on CAD and image-guided decision support
- H K Huang
- K Doi
- HK Huang