Kenneth R. Persons

Evaluation of irreversible compression of digitized posterior-anterior chest radiographs

The purpose of this article is to assess lossy image compression of digitized chest radiographs using radiologist assessment of anatomic structures and numerical measurements of image accuracy. Forty posterior-anterior (PA) chest radiographs were digitized and compressed using an irreversible wavelet technique at 10, 20, 40, and 80∶1. These were presented in a blinded fashion with an uncompressed image for A-B comparison of 11 anatomic structures as well as overall quality assessments. Mean error, root-mean square (RMS) error, maximum pixel error, and number of pixels within 1% of original value were also computed for compression ratios from 5∶1 to 80∶1. We found that at low compression (10∶1) there was a slight preference for compressed images. There was no significant difference at 20∶1 and 40∶1. There was a slight preference on some structures for the original compared with 80∶1 compressed images. Numerical measures showed high image faithfulness, both in terms of number of pixels that were within 1% of their original value, and by the average error for all pixels. Our findings suggest that lossy compression at 40∶1 or more can be used without perceptible loss in the representation of anatomic structures. On this finding, we will do a receiver-operator characteristic (ROC) analysis of nodule detection in lossy compressed images using 40∶1 compression.

Considerations for Exchanging and Sharing Medical Images for Improved Collaboration and Patient Care: HIMSS-SIIM Collaborative White Paper

The need for providers and patients to exchange and share imaging has never been more apparent, yet many organizations are only now, as a part of a larger enterprise imaging initiative, taking steps to streamline an important process that has historically been facilitated with the use of CDs or insecure methods of communication. This paper will provide an introduction to concepts and common-use cases for image exchange, outline challenges that have hindered adoption to date, and describe standards for image exchange that show increasing promise of being adopted by vendors and providers.

An evaluation of JPEG and JPEG 2000 irreversible compression algorithms applied to neurologic computed tomography and magnetic resonance images

We performed visual comparison of 200 head magnetic resonance (MR) and 200 head computed tomography (CT) images compressed at two levels using standard Joint Photographic Experts Group (JPEG) irreversible compression and a preliminary version of the JPEG 2000 irreversible algorithm. Blinded evaluations by neuroradiologists compared original versus either JPEG or JPEG 2000. We found that this version of JPEG 2000 did not perform as well as the current JPEG for head CTs, but for MR images, JPEG 2000 performed as well or better. Around 7∶1 compression ratio seemed to be a conservative point where there was no perceptible difference.

Evaluation of Irreversible JPEG Compression for A Clinical Ultrasound Practice

A prior ultrasound study indicated that images with low to moderate levels of JPEG and wavelet compression were acceptable for diagnostic purposes. The purpose of this study is to validate this prior finding using the Joint Photographic Experts Group (JPEG) baseline compression algorithm, at a compression ratio of approximately 10:1, on a sufficiently large number of grayscale and color ultrasound images to attain a statistically significant result. The practical goal of this study is to determine if it is feasible for radiologists to use irreversibly compressed images as an integral part of the day to day ultrasound practice (ie, perform primary diagnosis with, and store irreversibly compressed images in the ultrasound PACS archive). In this study, 5 Radiologists were asked to review 300 grayscale and color static ultrasound images selected from 4 major anatomic groups. Each image was compressed and decompressed using the JPEG baseline compression algorithm at a fixed quality factor resulting in an average compression ratio of approximately 9:1. The images were presented in pairs (original and compressed) in a blinded fashion on a PACS workstation in the ultrasound reading areas, and radiologists were asked to pick which image they preferred in terms of diagnostic utility and their degree of certainty (on a scale from 7 to 4). Of the 1,499 total readings, 50.17% (95% confidence intervals at 47.6%, and 52.7%) indicated a preference for the original image in the pair, and 49.83% (95% confidence intervals at 47.3%, and 52.0%) indicated a preference for the compressed image. These findings led the authors to conclude that static color and gray-scale ultrasound images compressed with JPEG at approximately 9:1 are statistically indistinguishable from the originals for primary diagnostic purposes. Based on the authors laboratory experience with compression and the results of this and other prior studies JPEG compression is now being applied to all ultrasound images in the authors radiology practice before reading. No image quality-related issues have been encountered after 12 months of operation (approximately 48,000 examinations).

Towards a More Cloud-Friendly Medical Imaging Applications Architecture: A Modest Proposal

Recent information technology literature, in general, and radiology trade journals, in particular, are rife with allusions to the “cloud” suggesting that moving one’s compute and storage assets into someone else’s data center magically solves cost, performance, and elasticity problems. More likely, one is only trading one set of problems for another, including greater latency (aka slower turnaround times) since the image data must now leave the local area network and travel longer paths via encrypted tunnels. To offset this, an imaging system design is needed that reduces the number of high-latency image transmissions, yet can still leverage cloud strengths. This work explores the requirements for such a design.

A Foundation for Enterprise Imaging: HIMSS-SIIM Collaborative White Paper

Care providers today routinely obtain valuable clinical multimedia with mobile devices, scope cameras, ultrasound, and many other modalities at the point of care. Image capture and storage workflows may be heterogeneous across an enterprise, and as a result, they often are not well incorporated in the electronic health record. Enterprise Imaging refers to a set of strategies, initiatives, and workflows implemented across a healthcare enterprise to consistently and optimally capture, index, manage, store, distribute, view, exchange, and analyze all clinical imaging and multimedia content to enhance the electronic health record. This paper is intended to introduce Enterprise Imaging as an important initiative to clinical and informatics leadership, and outline its key elements of governance, strategy, infrastructure, common multimedia content, acquisition workflows, enterprise image viewers, and image exchange services.

Histogram transformation for improved compression of CT images

CT images represent a unique challenge for medical image compression; they have many pixels with very high and very low intensity values, often with sharp edges between the two, and the intensity values have quantitative significance, representing the attenuation coefficient in Hounsfield units (HU). Thus, the intensity ranges which represent bone or various soft tissues are essentially known in advance. When viewing a CT image, different window and level settings for mapping the 12-bit intensity values to an 8-bit display are used, depending on the objects of interest. When viewing objects with very high or low values, large window values are used, so that differences in intensity values on the order of 10 or 20 HU are not significant and are scarcely noticed in practice. Conversely, when viewing soft tissues, small windows are used to capture subtle but important distinction, and an intensity difference of 10-20 HU can be highly significant. CT compression schemes, therefore, should have a mechanism to increase the representation accuracy of intensity values corresponding to soft tissue relative to those corresponding to bone and air. We describe a simple technique to force compression algorithms to assign more importance to specific intensity ranges by transforming the histogram of the image prior to compression, and show sample results. The technique significantly increases the ratio by which the images can be compressed while retaining acceptable image quality at both large and small window settings in common clinical use.

Technical Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper

This white paper explores the technical challenges and solutions for acquiring (capturing) and managing enterprise images, particularly those involving visible light applications. The types of acquisition devices used for various general-purpose photography and specialized applications including dermatology, endoscopy, and anatomic pathology are reviewed. The formats and standards used, and the associated metadata requirements and communication protocols for transfer and workflow are considered. Particular emphasis is placed on the importance of metadata capture in both order- and encounter-based workflow. The benefits of using DICOM to provide a standard means of recording and accessing both metadata and image and video data are considered, as is the role of IHE and FHIR.

Implementing a Radiology- Information Technology Project: Mobile Image Viewing Use Case and a General Guideline for Radiologist-Information Technology Team Collaboration

OBJECTIVE This article illustrates the importance of radiologist engagement in the successful implementation of radiology-information technology (IT) projects through the example of establishing a mobile image viewing solution for health care professionals. CONCLUSION With an understanding of the types of decisions that benefit from radiologist input, this article outlines an overall project framework to provide a context for how radiologists might engage in the project cycle.

Clinical evaluation of wavelet compression of digitized chest x-rays

In this paper we assess lossy image compression of digitalized chest x-rays using radiologist assessment of anatomic structures and numerical measurements of image accuracy. Forty chest x-rays were digitized and compressed using an irreversible wavelet technique at 10, 20, 40 and 80:1. These were presented in a blinded fashion with an uncompressed image for subjective A-B comparison of 11 anatomic structures as well as overall quality. Mean error, RMS error, maximum pixel error, and number of pixels within 1 percent of original value were also computed for compression ratios from 10:1 to 80:1. We found that at low compression there was a slight preference for compressed images. There was no significant difference at 20:1 and 40:1. There was a slight preference on some structures for the original compared with 80:1 compressed images. Numerical measures demonstrated high image faithfulness, both in terms of number of pixels that were within 1 percent of their original value, and by the average error for all pixels. Our findings suggest that lossy compression at 40:1 or more can be used without perceptible loss in the demonstration of anatomic structures.