High Quality Medical Images Research Articles

Automated Exudates Detection in Retinal Fundus Image Using Morphological Operator and Entropy Maximization Thresholding

Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. Early detection through automatic screening programs reduces considerably expansion of the disease. Exudates are one of the earliest signs. This paper presents an automated method for exudates detection in digital retinal fundus image. The first step consists of image enhancement. It focuses on histogram expansion and median filter. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. The second step refers to blood vessel removal by using morphological operators. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. Evaluations were done with retinal fundus image DIARETDB1 database. DIARETDB1 gathers high-quality medical images which have been verified by experts. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. This tool provides a unified framework for benchmarking the methods, but also points out clear deficiencies in the current practice in the method development. Comparing to other recent methods available in literature, we found that the proposed algorithm accomplished better result in terms of sensibility (94.27%) and specificity (97.63%).

Synthetic Medical Images Using F&BGAN for Improved Lung Nodules Classification by Multi-Scale VGG16

Lung cancer is one of the highest causes of cancer-related death in both men and women. Therefore, various diagnostic methods for lung nodules classification have been proposed to implement the early detection. Due to the limited amount and diversity of samples, these methods encounter some bottlenecks. In this paper, we intend to develop a method to enlarge the dataset and enhance the performance of pulmonary nodules classification. We propose a data augmentation method based on generative adversarial network (GAN), called Forward and Backward GAN (F&BGAN), which can generate high-quality synthetic medical images. F&BGAN has two stages, Forward GAN (FGAN) generates diverse images, and Backward GAN (BGAN) is used to improve the quality of images. Besides, a hierarchical learning framework, multi-scale VGG16 (M-VGG16) network, is proposed to extract discriminative features from alternating stacked layers. The methodology was evaluated on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, with the best accuracy of 95.24%, sensitivity of 98.67%, specificity of 92.47% and area under ROC curve (AUROC) of 0.980. Experimental results demonstrate the feasibility of F&BGAN in generating medical images and the effectiveness of M-VGG16 in classifying malignant and benign nodules.

Radiology: Imaging Trauma Patients in a Deployed Setting.

Medical imaging plays a critical role in the rapid diagnosis, effective triage, and management of complex poly-trauma patients. High-quality medical imaging can be accomplished successfully in a deployed or wartime setting. Due to advances in aggressive resuscitation techniques and the speed of the latest generation computed tomography scanners (64-detector and beyond), rapid trauma scans utilizing computed tomography and ultrasound imaging can routinely be performed prior to taking the patient to the operating room potentially providing the trauma team with lifesaving information. This clinical practice guideline provides an overview of the imaging modalities available in austere settings, the equipment required, and the role that each plays in triaging and diagnosis of the acutely injured poly-trauma patients.

Medical images Compression using convolutional neural network with LWT

In compression of medical image using convolutional neural network trained with the back-propagation algorithm and lefted wavelet transformation is proposed to compress high quality medical images. It gives much better result as compared to feed-forward neural network . Medical image processing process is one of the most important section of research in medical applications in digital medical information. In this new approach , a three hidden layer convolutional network (CNN) is applied directly as the main compression algorithm to compress an MRI, X-ray, computer tomography images. After training with sufficient sample images, the compression process will be carried out on the target image. The coupling weights and activation values of each neuron in the hidden layer will be stored after training. Compression is achieved by using smaller number of hidden neurons as compare to the number of image pixels due to lesser information being stored. experimental results proves that the anticipated algorithm is superior to another algorithm in both lossy and lossless compression for all medical images tested Experimental results show that the CNN is able to achieve comparable compression performance to popular existing medical image compression schemes such as JPEG2000 and JPEG-LS. The Wavelet-SPIHT algorithm provides PSNR very important values for MRI and CT scan images.

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

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

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

Alarming system in real time and high quality display using volumerendering for otologic surgical navigation: a preclinical study

Objective:The aim of this study is to design and develop a novel navigation framework that alarms the surgeon with sound during drilling and dynamically renders the high quality medical image data. Method:The accuracy of the proposed system were measured with a skull model using paired point registration and the target registration error (TRE) was computed. We segmented the accurate structure of target and compute the minimum distance between the drill tip and the target. Once the drill tip approached the target structures, the system would alarm the surgeon. We proposed a novelty method that used a mask to render the medical image data in real time. Result:The maximum distance error of 90 target points was 1.016 mm, the minimum was 0.427 mm, and the average distance error was (0.74±0.07) mm. The design and development of the alarming system and dynamically rendering the medical image data of navigation system was accomplished. Conclusion:The results show that the accuracy of the navigation system can meet the clinical needs. It also demonstrates the feasibility of the alarm system and dynamic display system and its application prospects.

Design and evaluation of medical ultrasonic adaptive beamforming algorithm implementation on heterogeneous embedded computing platform

Medical ultrasonic imaging has been utilized in a variety of clinical diagnoses for many years. Recently, because of the needs of portable and mobile medical ultrasonic diagnoses, the development of real-time medical ultrasonic imaging algorithms on embedded computing platforms is a rising research direction. Typically, delay-and-sum beamforming algorithm is implemented on embedded medical ultrasonic scanners. Such algorithm is the easiest to implement at real-time frame rate, but the image quality of this algorithm is not high enough for complicated diagnostic cases. As a result, minimum-variance adaptive beamforming algorithm for medical ultrasonic imaging is considered in this paper, which shows much higher image quality than that of delay-and-sum beamforming algorithm. However, minimum-variance adaptive beamforming algorithm is a complicated algorithm with O(n3) computational complexity. Consequently, it is not easy to implement such algorithm on embedded computing platform at real-time frame rate. On the other hand, GPU is a well-known parallel computing platform for image processing. Therefore, embedded GPU computing platform is considered as a potential real-time implementation platform of minimum-variance beamforming algorithm in this paper. By applying the described effective implementation strategies, the GPU implementation of minimum-variance beamforming algorithm performed more than 100 times faster than the ARM implementation on the same heterogeneous embedded platform. Furthermore, platform power consumptions, computation energy efficiency, and platform cost efficiency of the experimental heterogeneous embedded platforms were also evaluated, which demonstrated that the investigated heterogeneous embedded computing platforms were suitable for real-time portable or mobile high-quality medical ultrasonic imaging device constructions.

An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases.

The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48%.

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

Medical Image Fusion (MIF) can improve the performance of medical diagnosis, treatment planning and image-guided surgery significantly through providing high-quality and rich-information medical images. Traditional MIF techniques suffer from common drawbacks such as: contrast reduction, edge blurring and image degradation. Pulse-coupled Neural Network (PCNN) based MIF techniques outperform the traditional methods in providing high-quality fused images due to its global coupling and pulse synchronization property; however, the selection of significant features that motivate the PCNN is still an open problem and plays a major role in measuring the contribution of each source image into the fused image. In this paper, a medical image fusion algorithm is proposed based on the Non-subsampled Contourlet Transform (NSCT) and the Pulse-Coupled Neural Network (PCNN) to fuse images from different modalities. Local Average Energy is used to motivate the PCNN due to its ability to capture salient features of the image such as edges, contours and textures. The proposed approach produces a high quality fused image with high contrast and improved content in comparison with other image fusion techniques without loss of significant details on both levels: the visual and the quantitative.

Pre‐study to detect cancer with RF coil: brain and breast

With the rising number of elderly people, the cancer rate is also increasing. Thus, to detect cancer in its early stages is very important for increasing survival rates because cancer is one of the leading causes of death. Therefore, diagnosing cancer with a radio frequency (RF) coil that is used to obtain high-quality medical images from magnetic resonance imaging (MRI) systems is proposed in order to reduce the cost and time needed for the test. Specifically, an RF coil can sense a change in the electrical characteristics of body tissue, such as permittivity and conductivity. This pre-study shows that RF coils without an MR magnet can be used to determine the difference between normal and abnormal tissues (i.e. a tumour).

‘The role of smartphones in the recording and dissemination of medical images’

Background:Smartphones have evolved rapidly in the medical profession, and can now produce highquality medical images, providing a quick and simple method of image distribution. This has thepotential to improve clinical care of patients, but comes with specific ethical and medico-legalconsiderations that include issues of confidentiality, privacy and policy control.Aim: To quantify the use, distribution and storage of medical images taken using smartphones byclinicians, along with their perceptions regarding policies, practices and patient care.Methods: All clinicians and medical students employed or undergoing rotation at Peninsula Healthduring March 2012 were asked to participate in a de-identified, 36 item, online survey administeredby SurveyMonkey. The survey questioned respondent’s demographics, and issues surrounding therecording and dissemination of medical images using smartphones.Results: 134 responses were received. Most respondents were from the surgical discipline, followedby medicine, then emergency. Sixty five per cent admitted to taking medical images on theirsmartphones, yet no consent was obtained in almost a quarter (24%). When consent was taken, itwas predominantly verbal, but only documented 23% of the time. Of those who took medicalimages, 64% stored them personally and 82% shared them with someone else, mostly for input fromanother clinician. Forty three per cent were aware that an institutional policy existed, but only 28%had read the policy.Conclusion: Whilst the use of smartphones in a hospital setting is inevitable, the results obtainedhighlight issues related to privacy, confidentiality and patient care. This study will enable discussionand formulation of an evidence-based hospital policy

Improving the MRI Tumor Segmentation Process Using Appropriate Image Processing Techniques

Abstract —Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process. Index Terms — Medical image processing, Tumor segmentation, MRI images, Non-local Means, Histogram equalization, Laplacian filter. I. INTRODUCTION A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. Brain tumor has a higher mortality rate when compared to more common types of cancer. This underlines the need of accurate prognosis of brain tumor in health care industry [1]. MRI has become a widely-used method of high-quality medical imaging, especially in brain imaging [2]. Image segmentation refers to the process of partitioning a digital image into multiple segments, also known as super pixels. Image segmentation is an important process in computer vision and image processing, which divides an image into a number of disjoint regions such that the pixels have high similarity in order to use its different fields; science, medical, agriculture and industrial fields [3]. Besides, Magnetic Resonance Imaging (MRI) images are known to be corrupted by noise [4], blur [5] and low contrast artifact [6]. Therefore, this article discusses the issue of achieving a better segmentation through the use of proper image restoration and enhancement techniques. Moreover, the accurate and automatic segmentation of a brain tumor on MRI image is of great interest for assessing tumor growth and treatment responses, enhancing computer-assisted surgery, and constructing tumor growth models [7]. For those reasons, an automatic and reliable tumor segmentation method would be a useful tool since the medical image segmentation plays an instrumental role in clinical diagnosis [8]. The rest of the article sections are organized as the following: in section two, a brief elucidation about some related studies is presented as the used literature review. Moreover, detailed explanations about the employed algorithms are delivered in section three as a methodology. Also, the results of the proposed methodology are illustrated in section four as the experimental results. Likewise, a detailed discussion about the use of certain algorithms and its results is deliberated in section five. Finally, a brief closure about this article is provided in section six as a conclusion. II. LITERATURE REVIEW In this section, three recent studies are briefly described. The first study was presented by [9] which proposed a texture based analysis to identify irregularity in the brain and automatic segmentation using seeded region growing segmentation. The method of tumor detection based on the texture of the MRI is proposed to segment the abnormality from the regular adjacent, although small quantities misclassified, but the segmentation result is precise. Moreover, [10] suggested a strong technique for automatic detection of tumor for brain MRI images. He also proposed a new morphological technique which is used for the stripped skull region from the brain images. Detecting tumor precisely based on seeded region growing segmentation on an improved single seed point selection algorithm is

Real time exploration and management of large medical volumetric datasets on small mobile devices—Evaluation of remote volume rendering approach

In this paper I present the architecture of system that can be used for real time exploration and management of large medical volumetric datasets. The new state of the art solution presented in this paper is an example of visual data management system. System prototype evaluation proved that it is possible to use low-powered (and cheap) up-to-date mobile devices with programmable GPUs as the remote interfaces for exploration of large volumetric medical data. The implementation was done with high-level programming language that enables portability between different hardware models. The lack of lossy compression enables to display high quality medical images visualizations without any simplifications and noises in frequency domain. The prototype of system is capable to remotely render and send to a client (for example cell phone or tablet) rendered data with frequency 30fps with limited resolution during interaction. One second after the interaction is finished client machine receives full resolution image. The evaluation of the system was performed on volumetric computed tomography angiography image with approximate size 5123voxels.

Low Power Transmitter for Wireless Capsule Endoscope

This paper presents the transmitter circuit designed for the application of wireless capsule endoscope to overcome the limitation of conventional endoscope. The design is performed using CMOS 0.13 μm technology. The transmitter is designed to operate at centre frequency of 433.92 MHz, which is one of the ISM band. Active mixer and ring oscillator made up the transmitter and it consumes 1.57 mA of current using a supply voltage of 1.2 V, brings the dc power consumption of the transmitter to be 1.88 mW. Data rate of 3.5 Mbps ensure it can transmit high quality medical imaging.

An Asynchronous Sampling-Based 128 $\times$ 128 Direct Photon-Counting X-Ray Image Detector with Multi-Energy Discrimination and High Spatial Resolution

This paper presents a direct photon-counting X-ray image detector with a <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm HgI}_{2}$</tex> </formula> photoconductor for high-quality medical imaging applications. The proposed sampling-based charge preamplifier with asynchronous self-reset enables a pixel to detect single X-ray photon energy with higher sensitivity and faster processing rate. The use of the correlated double sampling enabled by the sampling-based architecture also reduces flicker noise and contributes to the achievement of high pixel-to-pixel uniformity. Discrimination of the energy level of the detected X-rays is performed by the proposed compact in-pixel ADC with low power consumption. Three 15-bit counters in each pixel count up energy-discriminated photons for the reconstruction of multispectral X-ray images. A 128 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\times$</tex> </formula> 128 X-ray image detector with a pixel size of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$60\times 60\ \mu{\rm m}^{2}$</tex></formula> is implemented and measured using a 0.13- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu{\rm m}$</tex></formula> /0.35- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu{\rm m}$</tex></formula> standard CMOS process. It discriminates 3 energy levels of photon energy with a gain of 107 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm mV/ke}^{-}$</tex></formula> and a static power consumption of 4.6 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu{\rm W/pixel}$</tex></formula> . The measured equivalent noise charge (ENC) and minimum detectable energy level of the detector pixel are 68 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm e}^{-}\ {\rm rms}$</tex></formula> and 290 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">${\rm e}^{-}$</tex></formula> , respectively. The measured maximum threshold dispersion in the pixel array is 164 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">${\rm e}^{-}\ {\rm rms}$</tex></formula> without any calibration. The functionality of our chip is also successfully demonstrated using real X-ray images.

Reversible and High-Capacity Data Hiding in High Quality Medical Images

Via the Internet, the information infrastructure of modern health care has already established medical information systems to share electronic health records among patients and health care providers. Data hiding plays an important role to protect medical images. Because modern medical devices have improved, high resolutions of medical images are provided to detect early diseases. The high quality medical images are used to recognize complicated anatomical structures such as soft tissues, muscles, and internal organs to support diagnosis of diseases. For instance, 16-bit depth medical images will provide 65,536 discrete levels to show more details of anatomical structures. In general, the feature of low utilization rate of intensity in 16-bit depth will be utilized to handle overflow/underflow problem. Nowadays, most of data hiding algorithms are still experimenting on 8-bit depth medical images. We proposed a novel reversible data hiding scheme testing on 16-bit depth CT and MRI medical image. And the peak point and zero point of a histogram are applied to embed secret message k bits without salt-and-pepper.

Customized first and second order statistics based operators to support advanced texture analysis of MRI images.

Texture analysis is the process of highlighting key characteristics thus providing an exhaustive and unambiguous mathematical description of any object represented in a digital image. Each characteristic is connected to a specific property of the object. In some cases the mentioned properties represent aspects visually perceptible which can be detected by developing operators based on Computer Vision techniques. In other cases these properties are not visually perceptible and their computation is obtained by developing operators based on Image Understanding approaches. Pixels composing high quality medical images can be considered the result of a stochastic process since they represent morphological or physiological processes. Empirical observations have shown that these images have visually perceptible and hidden significant aspects. For these reasons, the operators can be developed by means of a statistical approach. In this paper we present a set of customized first and second order statistics based operators to perform advanced texture analysis of Magnetic Resonance Imaging (MRI) images. In particular, we specify the main rules defining the role of an operator and its relationship with other operators. Extensive experiments carried out on a wide dataset of MRI images of different body regions demonstrating usefulness and accuracy of the proposed approach are also reported.

A reversible data hiding method by histogram shifting in high quality medical images

Enormous demands for recognizing complicated anatomical structures in medical images have been demanded on high quality of medical image such as each pixel expressed by 16-bit depth. Now, most of data hiding algorithms are still applied in 8-bit depth medical images. We proposed a histogram shifting method for image reversible data hiding testing on high bit depth medical images. Among image local block pixels, we exploit the high correlation for smooth surface of anatomical structure in medical images. Thus, we apply a different value for each block of pixels to produce a difference histogram to embed secret bits. During data embedding stage, the image blocks are divided into two categories due to two corresponding embedding strategies. Via an inverse histogram shifting mechanism, the original image will be accurately recovered after the hidden data extraction. Due to requirements of medical images for data hiding, we proposed six criteria: (1) well-suited for high quality medical images, (2) without salt-and-pepper, (3) applicable to medical image with smooth surface, (4) well-suited sparse histogram of intensity levels, (5) free location map, (6) ability of adjusting data embedding capacity, PSNR and Inter-Slice PSNR. We proposed a data hiding methods satisfying above 6 criteria.

SMART Imagebase: Peer-Reviewed Medical Images

Electronic resources in the health sciences have historically been bibliographically based, but the need for image content is growing. The SMART Imagebase from EBSCO is a peer-reviewed source for high quality medical images, animations, videos, and monograph content. Nucleus Medical Art, a publisher of online medical illustrations, animations, interactive media, and educational materials for patients, consumers, and health care professionals, has teamed up with EBSCO to fill this need with the SMART Imagebase. This review will provide a profile of the features, search capabilities, and content of this resource.