Medical Image Archiving Research Articles

A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images

In this paper, we attempt to investigate the secure archiving of medical images which are stored on semi-trusted cloud servers, and focus on addressing the complicated and challenging integrity control and privacy preservation issues. With the intention of protecting the medical images stored on a semi-trusted server, a novel ROI-based high capacity reversible data hiding (RDH) scheme with contrast enhancement is proposed in this paper. The proposed method aims at improving the quality of the medical images effectively and embedding high capacity data reversibly meanwhile. Therefore, the proposed method adopts “adaptive threshold detector” (ATD) segmentation algorithm to automatically separate the “region of interest” (ROI) and “region of non-interest” (NROI) at first, then enhances the contrast of the ROI region by stretching the grayscale and embeds the data into peak bins of the stretched histogram without extending the histogram bins. Lastly, the rest of the required large of data are embedded into NROI region regardless its quality. In addition, the proposed method records the edge location of the segmentation instead of recording the location of the overflow and underflow. The experiment shows that the proposed method can improve the quality of medical images obviously whatever in low embedding rate or high embedding rate when compared with other contrast-based RDH methods.

Protocol for transmission and archiving of medical images (wavREPro.JPEG)

The transmission and archiving of medical images are among the most important operations in telemedicine. Indeed, the collaboration platforms have to ensure video conferencing, medical images and patient data transfer and archiving. However, the big volume of this kind of images represents a major obstacle for the work of a tele-staff. In order to overcome this problem, we suggest a new archiving and transmission protocol that we call 'wavREPro.JPEG'. Its process consists of two phases. In fact, before the information broadcast, this protocol reduces images by Discrete Wavelet Transforms and compresses them by the classic JPEG. At the reception step, it enlarges images after their decompression. Experimental results have demonstrated that our proposed method is more efficient than the classic JPEG compression. Effectively, it offers more minimisation in terms of network bandwidth consumption and storage space.

Protocol for transmission and archiving of medical images (wavREPro.JPEG)

Protocol for transmission and archiving of medical images (wavREPro.JPEG)

Toward Distributed Conduction of Large-Scale Studies in Radiation Therapy and Oncology: Open-Source System Integration Approach

The conduct of multicenter studies in the field of radiation therapy and oncology is one of the key prerequisites for stimulating translational research and accelerating the application of healthcare innovations into day to day patient treatment. To effectively run such studies, the participating research institutions need access to radiotherapy-specific information technology (IT), which enables collection of case report forms (eCRFs) linked with medical imaging and treatment planning data. Existing commercial systems that provide multimodal data collection features may fit for industry funded clinical trials but do not consider academia needs for long-lasting and affordable IT infrastructure. This paper presents an alternative way for development of a sustainable clinical research IT platform based on open-source systems. The platform design is driven by an integration of its core components, namely, electronic data capture system (EDC), medical image archive (PACS), and patient identity management system (PIDG). Three deployment scenarios are described that allow setting up the platform in a central, virtually hosted or decentralized environment. The first production implementation of this infrastructure was established as RadiationDosePlan-Image/Biomarker-Outcome-platform (RadPlanBio) in September 2013. It is maintained by the German Cancer Consortium (DKTK), Dresden and the German Cancer Research Center (DKFZ), Heidelberg. Until now, it manages 9 studies across 11 sites with 600 enrolled subjects. This paper shows how the integration of open-source systems can drive the development of sustainable clinical research IT environment supporting translational research projects in the field of radiation therapy and oncology.

Multiple-Instance Learning for Anomaly Detection in Digital Mammography

This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as "normal" or "abnormal". Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation.

Cryoablation of Osteoid Osteoma in the Pediatric and Adolescent Population

PurposeTo evaluate the technical feasibility and clinical efficacy of osteoid osteoma (OO) cryoablation in a large, pediatric/adolescent cohort. Materials and MethodsAn electronic medical record and imaging archive review was performed to identify all cryoablations performed for OOs between 2011 and 2015 at a single tertiary care pediatric hospital. The subsequent analysis included 29 patients with suspected OOs treated by cryoablation (age range, 3–18 y; mean age, 11.3 y; 17 boys; 12 girls). Conventional CT guidance was used in 22 procedures; cone-beam CT guidance was used in 7 procedures. Follow-up data were obtained via a standardized telephone questionnaire (23/29 patients; 79.3%) and clinical notes (5/29 patients; 17.2%). One patient was lost to follow-up. ResultsTechnical success was achieved in 100% of patients (29/29). Immediate clinical success (cessation of pain and nonsteroidal antiinflammatory drug [NSAID] use within 1 mo after the procedure) was achieved in 27/28 patients (96.4%). Short-term clinical success (cessation of pain and NSAID use for > 3 mo after the procedure) was achieved in 24/25 patients (96%). Long-term clinical success (cessation of pain and NSAID use for > 12 mo after the procedure) was achieved in 19/21 patients (90.5%). Median pain scale score before the procedure was 10 (range, 5–10); median pain scale score after the procedure was 0 (range, 0–8; P < .0001). There were 6 minor complications (21%) and no major complications. ConclusionImage-guided cryoablation is a technically feasible, clinically efficacious therapeutic option for children and adolescents with symptomatic OO.

Software Services Delivered from the Cloud

Picture Archiving and Communication Systems (PACS), alongside Radiological Information Systems (RIS) are nowadays widely disseminated, proven useful, hospital information systems components. The “Région Sans Film” (“Filmless Region”) program was launched in 2009 by the French ministry of health in order to help the generalization to all healthcare structures such as hospitals or general physician practices, of PACS, RIS, and archiving of medical images. It is done by means of a mutualized service platform whom building and running has been entrusted to an industrial consortium. This platform implements the latest technologies of medical image processing and of cloud computing. It is built in order to support service-oriented enterprise architecture composed of one main layer of software services. This natively scalable platform is innovative because it is the first one which contains all the materials for the implementation of all services in the cloud. The corresponding SLA are defined in order to be adaptable to the needs of further health structures which could later join the platform by participating in its mutualized purchasing. The goal is not only to share the costs but also to deliver new images sharing services. New business processes/services around sharing of images such as teleradiology or access to the images produced in hospitals to the general practitioners are defined towards the exercise of real filmless radiology.

Texture based feature extraction methods for content based medical image retrieval systems.

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

On the Performance of Lossless Wavelet Compression Scheme on Digital Medical Images in JPEG, PNG, BMP and TIFF Formats

The major challenges facing digital radiological systems notably: teleradiology and picture archiving and communication systems (PACS) are not unconnected to: limited storage space and bandwidth requirements for storage, transmission, retrieval and archiving of digital medical images. Lossless compression techniques have been adopted by the medical community to help solve these problems as it does not degrades the quality of the medical image which is crucial for diagnosis while reducing the size (often in ratio 2:1) of the original image. However, little attention has been paid to knowing the performance of a choice compression scheme on the medical image format type for efficient handling in the digital radiology system. Hence, this work presents an evaluation of the performance of a lossless wavelet compression scheme on samples of computed tomography (CT) images of the brain in JPEG, PNG, BMP and TIFF formats.

Multi-series DICOM: an Extension of DICOM That Stores a Whole Study in a Single Object

Today, most medical images are stored as a set of single-frame composite Digital Imaging and Communications in Medicine (DICOM) objects that contain the four levels of the DICOM information model-patient, study, series, and instance. Although DICOM addresses most of the issues related to medical image archiving, it has some limitations. Replicating the header information with each DICOM object increases the study size and the parsing overhead. Multi-frame DICOM (MFD) was developed to address this, among other issues. The MFD combines all DICOM objects belonging to a series into a single DICOM object. Hence, the series-level attributes are normalized, and the amount of header data repetition is reduced. In this paper, multi-series DICOM (MSD) is introduced as a potential extension to the DICOM standard that allows faster parsing, transmission, and storage of studies. MSD extends the MFD de-duplication of series-level attributes to study-level attributes. A single DICOM object that stores the whole study is proposed. An efficient algorithm, called the one-pass de-duplication algorithm, was developed to find and eliminate the replicated data elements within the study. A group of experiments were done that evaluate MSD and the one-pass de-duplication algorithm performance. The experiments show that MSD significantly reduces the amount of data repetition and decreases the time required to read and parse DICOM studies. MSD is one possible solution that addresses the DICOM limitations regarding header information repetition.

A QR Code Based Zero-Watermarking Scheme for Authentication of Medical Images in Teleradiology Cloud

Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (CT)—Singular Value Decomposition (SVD) domain for unambiguous authentication of medical images. Further, a framework is proposed for accessing patient records based on the watermarking scheme. The patient identification details and a link to patient data encoded into a Quick Response (QR) code serves as the watermark. In the proposed scheme, the medical image is not subjected to degradations due to watermarking. Patient authentication and authorized access to patient data are realized on combining a Secret Share with the Master Share constructed from invariant features of the medical image. The Hu's invariant image moments are exploited in creating the Master Share. The proposed system is evaluated with Checkmark software and is found to be robust to both geometric and non geometric attacks.

Comparison of Hybrid Codes for MRI Brain Image Compression

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanning techniques produce human body pictures in digital form, which are prohibitive in nature. Hence compression is necessary for storage and transmission purposes of such medical images. In general, medical images are compressed in a lossless manner in order to preserve details and to avoid wrong diagnosis. But this leads to a lower compression rate. Hence we consider region of interest (ROI) normally the abnormal region in the image and compress it without loss to achieve high compression ratio in par with maintaining high image quality and the Non-region of interest (Non-ROI) of the image is compressed in a lossy manner. This paper discusses two simple hybrid coding techniques (Hybrid A and Hybrid B) on MRI human brain tumor image datasets. Also we evaluate their performance by comparing them with the standard lossless technique JPEG 2000 in terms of compression ratio (CR) and peak to signal noise ratio (PSNR). Both hybrid codes have resulted in computationally economical scheme producing higher compression ratio than existing JPEG2000 and also meets the legal requirement of medical image archiving. The results obtained prove that our proposed hybrid schemes outperform existing schemes.

Request redirection paradigm in medical image archive implementation

It is widely recognized that the JPEG2000 facilitates issues in medical imaging: storage, communication, sharing, remote access, interoperability, and presentation scalability. Therefore, JPEG2000 support was added to the DICOM standard Supplement 61. Two approaches to support JPEG2000 medical image are explicitly defined by the DICOM standard: replacing the DICOM image format with corresponding JPEG2000 codestream, or by the Pixel Data Provider service, DICOM supplement 106. The latest one supposes two-step retrieval of medical image: DICOM request and response from a DICOM server, and then JPIP request and response from a JPEG2000 server. We propose a novel strategy for transmission of scalable JPEG2000 images extracted from a single codestream over DICOM network using the DICOM Private Data Element without sacrificing system interoperability. It employs the request redirection paradigm: DICOM request and response from JPEG2000 server through DICOM server. The paper presents programming solution for implementation of request redirection paradigm in a DICOM transparent manner.

DIAGNOSIS: A Telematics-Enabled System for Medical Image Archiving, Management, and Diagnosis Assistance

In this paper, a modular system for medical image archiving, management, diagnosis support, and telematic cooperation is presented. The system provides digital imaging and communications in medicine (DICOM)-compatible tools for digital image processing and database management of medical images. The software features algorithms for preprocessing, manual or semi-automatic segmentation, automatic calculation of geometrical/size characteristics, and 3-D visualization of organs or selected regions of interest. Additionally, the system incorporates a database where patient data and information can be stored and retrieved. Access to the database is only permitted to authorized users. The user-friendly interface makes the software handy and accessible to clinicians, whereas the telematic components allow collaboration with remote experts. The pilot system incorporates a computer-aided diagnosis module aiming at providing support in the diagnosis of focal liver lesions from computed tomography images.

An interactive 3D medical visualization system based on a light field display

We present a prototype medical data visualization system exploiting a light field display and custom direct volume rendering techniques to enhance understanding of massive volumetric data, such as CT, MRI, and PET scans. The system can be integrated with standard medical image archives and extends the capabilities of current radiology workstations by supporting real-time rendering of volumes of potentially unlimited size on light field displays generating dynamic observer-independent light fields. The system allows multiple untracked naked-eye users in a sufficiently large interaction area to coherently perceive rendered volumes as real objects, with stereo and motion parallax cues. In this way, an effective collaborative analysis of volumetric data can be achieved. Evaluation tests demonstrate the usefulness of the generated depth cues and the improved performance in understanding complex spatial structures with respect to standard techniques.

Content-based access to oral and maxillofacial radiographs

Content-based access (CBA) to medical image archives, i.e. data retrieval by means of image-based numerical features computed automatically, has capabilities to improve diagnostics, research and education. In this study, the applicability of CBA methods in dentomaxillofacial radiology is evaluated. Recent research has discovered numerical features that were successfully applied for an automatic categorization of radiographs. In our experiments, oral and maxillofacial radiographs were obtained from the day-to-day routine of a university hospital and labelled by an experienced dental radiologist regarding the technique and direction of imaging, as well as the displayed anatomy and biosystem. In total, 2000 radiographs of 71 classes with at least 10 samples per class were analysed. A combination of co-occurrence-based texture features and correlation-based similarity measures was used in leaving-one-out experiments for automatic classification. The impact of automatic detection and separation of multi-field images and automatic separability of biosystems were analysed. Automatic categorization yielded error rates of 23.20%, 7.95% and 4.40% with respect to a correct match within the first, fifth and tenth best returns. These figures improved to 23.05%, 7.00%, 4.20%, and 20.05%, 5.65% and 3.25% if automatic decomposition was applied and the classifier was optimized to the dentomaxillofacial imagery, respectively. The dentulous and implant systems were difficult to distinguish. Experiments on non-dental radiographs (10,000 images of 57 classes) yielded 12.6%, 5.6% and 3.6%. Using the same numerical features as in medical radiology, oral and maxillofacial radiographs can be reliably indexed by global texture features for CBA and data mining.

Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.

Managing Medical Images and Clinical Information: InCor's Experience

Patients usually get medical assistance in several clinics and hospitals during their lifetime, archiving vital information in a dispersed way. Clearly, a proper patient care should take into account that information in order to check for incompatibilities, avoid unnecessary exams, and get relevant clinical history. The Heart Institute (InCor) of São Paulo, Brazil, has been committed to the goal of integrating all exams and clinical information within the institution and other hospitals. Since InCor is one of the six institutes of the University of São Paulo Medical School and each institute has its own information system, exchanging information among the institutes is also a very important aspect that has been considered. In the last few years, a system for transmission, archiving, retrieval, processing, and visualization of medical images integrated with a hospital information system has been successfully created and constitutes the InCor's electronic patient record (EPR). This work describes the experience in the effort to develop a functional and comprehensive EPR, which includes laboratory exams, images (static, dynamic, and three dimensional), clinical reports, documents, and even real-time vital signals. A security policy based on a contextual role-based access control model was implemented to regulate user's access to EPR. Currently, more than 10 TB of digital imaging and communications in medicine (DICOM) images have been stored using the proposed architecture and the EPR stores daily more than 11 GB of integrated data. The proposed storage subsystem allows 6 months of visibility for rapid retrieval and more than two years for automatic retrieval using a jukebox. This paper addresses also a prototype for the integration of distributed and heterogeneous EPR.

A New Way for Multidimensional Medical Data Management: Volume of Interest (VOI)-Based Retrieval of Medical Images With Visual and Functional Features

The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis.

A Mobile Phone Integrated Health Care Delivery System of Medical Images

With the growing computing capability of mobile phones, a handy mobile controller is developed for accessing the picture archiving and communication system (PACS) to enhance image management for clinicians with nearly no restriction in time and location using various wireless communication modes. The PACS is an integrated system for the distribution and archival of medical images that are acquired by different imaging modalities such as CT (computed tomography) scanners, CR (computed radiography) units, DR (digital radiography) units, US (ultrasonography) scanners, and MR (magnetic resonance) scanners. The mobile controller allows image management of the PACS including display, worklisting, query and retrieval of medical images in DICOM format. In this mobile system, a server program is developed in a PACS Web server which serves as an interface for client programs in the mobile phone and the enterprise PACS for image distribution in hospitals. The application processing is performed on the server side to reduce computational loading in the mobile device. The communication method of mobile phones can be adapted to multiple wireless environments in Hong Kong. This allows greater feasibility to accommodate the rapidly changing communication technology. No complicated computer hardware or software is necessary. Using a mobile phone embedded with the mobile controller client program, this system would serve as a tool for heath care and medical professionals to improve the efficiency of the health care services by speedy delivery of image information. This is particularly important in case of urgent consultation, and it allows health care workers better use of the time for patient care.