A tutorial on digital mammography imaging equipment Part 2: developments in digital support technologies

RAD

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EDUCATION

A tutorial on digital Part 2: developments

mammography imaging equipment in digital support technologies

Arnold R. Cowen University of Leeds, ResenrchSchool of Medicine, Division oJ Clinical Sciences-{ Medical Physics}, Wellcome Wing, The General Infimmry, Leeds LSI 3EX, U.K. (Received 6 November 1997; ncceyted 3 Mny 7998)

Key words: digital mammography image storage; tele-mammography; computer assisted diagnosis (CAD)

Currently the field of digital mammography is undergoing a particularly active and productive phase of technological development. In due course this will result in the availability of direct digital mammography image detectors capable of replacing screen-film mammography in its primary role of recording breast X-ray images. Developments in direct digital mammographic image acquisition, computerized image enhancement and high-resolution softcopy and hardcopy display were discussed in part I of this tutorial. Beyond these developments, in the new millennium we will also see the introduction of a range of new digital support technologies which will further extend the capabilities and facilities of digital mammography. In this, the second part of the tutorial, we will review developments in some of the most promising digital These include digital image archival, support technologies for mammography. tele-mammography, and computer assisted diagnosis (CAD). This tutorial is based upon a set of lecture notes produced by the author as leader of the ‘Science and Technology-Mammography’ module of the University of Leeds Masters Degree, MHSc Diagnostic Imaging (Breast Pathway).

Introduction

l

The status of digital mammography image detection and display systems was the subject of the first part of this tutorial [I]. In this the second part, we will review the range of digital support technologies which will come on-stream once direct digital mammographic image acquisition has become established. These new types of digital imaging support facilities will enhance the quality of clinical service offered by departments exploiting digital mammography. The digital support technologies we will review in this tutorial include: l Digital image storage and managementsupports the storage of images in digital form, providing a more compact, reliable and accessible archive of vast numbers of mammographic images l Digital tele-mammography-enables the computer transmission of image data to a distant clinical site for diagnosis or second opinion 1078-8174/98/040239+

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Computer assisted diagnosis (or CAD)-will support the use of computerized image analysis techniques to automatically detect abnormalities from mammographic images-providing a machine-based second reader to support the radiologist.

Digital image storage In theory, picture archiving and communications systems (PACS) promise rapid, reliable and efficient recall and review of digitally archived images [2]. Diagnostic radiology departments in the U.K. are beginning to investigate the relevance of PACS technologies to their future imaging needs. Some departments plan to go entirely ‘film-less’, whereby all images will be acquired, displayed and subsequently archived in digital form. PACS should enable the financial cost of film and related 0 1998 The College

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consumables to be substantially reduced, or in a film-less PACS department, to be eliminated completely. Digital storage should enable huge volumes of image data to be archived in compact form, releasing space within the hospital for more effective clinical use. Both referral and screening mammography are clinical applications that could benefit greatly from archiving and managing images as digital data. The potential of digital archival of mammography images is only slowly being exploited. bne reason for this is the extremely large data content of digital mammographic images and the technical difficulties in supporting this. For a quantitative analysis of the data content of digital mammography images see Appendix 1. ’ The data content of individual computed mammography (CM) and direct digital mammography (DDM) images are typically 10 and 40 megabytes, respectively. Each clinical digital mammography examination will produce two (or three) pairs of breast X-ray images per patient per session. This corresponds to: 40 (or 60) megabytes of data for CM and ,160 (or 240) megabytes of data for future DDM systems Associated with this will be the data from patient images acquired during previous examinations, which must also be available in compatible digital form for comparison. Therefore the digital mammography folder of the future could easily accumulate several 100 megabytes of data for each patient. For a weekly workload of 100 patients this represents a medium-term archive requirement of tens of gigabytes of data to be stored on-line each week. In terms of long-term storage, which can be off-line, this means a clinical DM service could generate a storage requirement of over a terabyte of image data per year. Note that this scale of data storage is similar to that often used to describe the storage requirements of a complete digital imaging department (excluding mammography, of course). Clearly a busy clinical digital mammography service will generate prodigious volumes of data. Fortunately there are a number of modem digital storage technologies that can satisfy archival demands of this scale. It must be noted that no single storage technology can meet all the demands of digital mammography. A hierarchy of different storage technologies is required to meet the demands of the various stages of the medical image production process [2]:

Short-term (instant access) storage will be supported by a hierarchy of static (SRAM), video (VRAM) and dynamic (DRAM) random access memory arrays, plus fast on-board magnetic hard-discs at the digital mammography workstation l Medium-term (on-line) storage is likely to be supported via RAID (Redundant Array of Inexpensive/Independent Discs) technology that offers 20 to SO gigabytes of reliable, rapidly accessible image data. RAID arrays can access, retrieve and display even very large image data-files, as will be produced by a busy digital mammography service, in a few seconds. l Long-term (off-line) archival can be supported by a variety of digital storage media including WORM (Write Once Read Many) optical discs, magneto-optical discs, recordable CD discs, or digital linear tape (DLT) or in the future recordable DVD discs l For extremely high data storage capacities the above media can be operated within a robotcontrolled enclosure which contains up to several hundred discs or tapes. Such ‘jukeboxes’ (or in the case of tape ‘libraries’) provide permanent image data archival, ranging from 0.3 to 30 terabytes, depending upon the storage media and capacity of the enclosure. The author’s group recently published a technical assessment of one such device, a !&O-disc CD jukebox capable of storing over 300 gigabytes of data before data compression [3]. A follow-up clinical evaluation project investigating the archival of computed mammography images is currently underway. The internal construction of a CD jukebox showing the banks of CD-R discs and auto-changer unit is presented in Fig. 1. Jukebox archive devices usually employ built-in redundancy (duplicate copies of the images) in case of a major system crash. l Off-site image archival services via commercial data warehouses are also now being considered as an alternative to an on-site hospital archive. l Clinicians may also develop their own personal multi-media digital mammographic archive of images, text reports and graphical data from clinically interesting cases, to Support staff training and research studies. Recordable CD could prove an effective and inexpensive means of providing this [4]. Problems of image data storage can be eased somewhat using digital image compression. The role of digital image compression is to reduce l

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Figure I. Internal construction of a 500 disc CD-R jukebox (Pioneer Corporation type DRM-5004X), showing banks of recordable CD discs and the disc auto-changer unit.

the quantity of data required to describe an image. Digital image compression can provide several benefits including: l minimizing the volume of data to be stored l increasing data transmission speeds (see also the section below on tele-mammography) l minimizing the time to recall and display images on a workstation, all of which could prove beneficial in digital mammography. For medico-legal reasons some radiologists prefer only to compress images using so-called ‘reversible data compression’ techniques which enable images to be compressed and de-compressed with absolutely no loss of information content. These techniques offer limited capacity for data compression-(only reducing data content by between 2- and j-fold). Irreversible data compression techniques enable an image to be compressed to a much greater extent, but ineviin image quality. tably result in some deterioration Modest degrees of irreversible compression may have only an insignificant impact on image quality. On the other hand, excessive irreversible data compression could have important consequences,

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particularly if diagnostic information is eliminated. Care must always be taken in applying irreversible compression to primary image data that has not been previously backed up. It must be noted that there is a time overhead in implementing the data compression and subsequently the de-compression of images. A number of image data compression techniques have been used in diagnostic radiology. The current JPEG or Joint Photographic Experts Group standard supports a 5- to lo-fold degree of irreof digital mammography versible compression image data, before diagnostic quality is significantly impaired. At higher data compression rates (i.e. above [lO:l]), JPEG produces block-structured images which radiartefacts in the re-constructed ologists find a distraction. In the future, Waveletand Fractal-based digital image compression algorithms will become available which ease this problem. The Wavelet Transform is an interesting new branch of mathematics which enables an image to be ‘spatially decomposed’ by successively filtering out features of different size. When used in irreversible compression applications any artefacts, which are produced in the re-constructed images, appear diffuse and un-structured. As a result human observers find such techniques acceptable at comparatively high compression rates. These new compression techniques will significantly improve the clinical acceptability of irreversibly compressed images. For example, Wavelet compression promises a JO- to 60-fold reduction in digital mammography data content before diagnostic performance is significantly impaired. The advantages of Wavelet compression compared with the JPEG standard is illustrated in Fig. 2. DICOM ~3.0 is the suite of information technology standards which are being developed to support the implementatibn of digital imaging facilities such as PACS, in medical imaging departments [5]. The DICOM standard defines protocols for transmitting data over a network with a number of devices connected to it. DICOM ~3.0 is consistent with the TCP/IP and OS1 communications protocols and therefore image data can be transmitted by any industry standard computer network (such as Ethernet, Token Ring, FDDI etc.) at the appropriate software levels. The standard also specifies information ‘objects’ not only for images and graphics, but also text reports to facilitate data exchange with radiological (and hospital) information systems. DICOM standards to support general digital radiography are well advanced and

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Figure 2. A comparison of the digital mammogram containing (3) Same image compressed by produces superior results. (This

effectiveness of Wavelet and JPEC irreversible data compression techniques. (1) Section of a micro-calcifications. (7) Same image compressed by over 50:1 using the Wavelet technique. over 51):1 using the IPEG standard technique. The Wavelet compression technique clearly image was kindly supplied by M.D. Clarkson.)

have received backing from most medical imaging equipment suppliers. As yet no specialized DICOM standards have been developed to address the exacting demands of digital mammographic imaging.

Digital

tele-mammography

Modern computer networks enable digital medical image data and text reports to be transmitted rapidly, reliably and economically between sites within a hospital or beyond. A local area network (or LAN) provides data communication within the hospital site. External communication to a distant site is usually provided by a wide-area network (or WAN). In 1993 Davies 161 published an interesting tutorial evaluating the performance of networking technologies of that time. More recently, the author published a technical review of LAN and WAN technologies suitable for medical imaging [7]. The use of a specialist WAN to support mammography is known as ‘Tele-mammography’. Tele-mammography systems enable digitized film or direct digital breast X-ray images, acquired at a remote site (or satellite department) which possesses no radiological expertise, to be automatically transmitted to a centre of expertise for reporting. Tele-mammography will also enable a radiologist to gain an authoritative second opinion on a particularly difficult case, from an acknowledged expert in a different city or country or even continent. It could also facilitate medical teleconferencing and interactive teaching resources. The clinical potential of tele-mammography is only just beginning to be explored.

A clinical tele-mammography service can be upon one of a number of possible data based communication standards. The type of technology preferred will depend upon the nature of the tele-mammography service to be established. Inevitably there is a close relationship between the speed of data transmission and equipment purchase and running costs. For example, establishing a dedicated on-line imaging service will demand a high data tra ns f er rate and will prove expensive to set up and run. Setting up a tele-mammography service based upon batch-transfer image data outof-hours would demand less sophisticated technology and therefore provide a more economical solution. A schematic diagram of a land-based tele-mammography service, identifying the major equipment components, is presented in Fig. 3. This concept depicts a remote digital image acquisition centre by a WAN site linked to a central reporting channel. Data communication technologies suitable for tele-mammography include: l Modem connected standard telephone service (ST’S) which enables digital data to be transmitted over a standard (copper wire) telephone line. This provides only a modest data transmission rate of typically 28.8 kilo-baud (or kilobits per second). Cost of hardware, installation and running costs are comparatively low. Typically, STS would take approximately an hour to transfer a single CM image and several hours for a high resolution DDM image. l The ISDN-2 (Integrated Services Digital Network) digital tele-communications service provides faster (128 kilo-baud) and more reliable data transfer rate than standard telephone lines.

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Digital Image Acquisition

Transmission Image

Acquisition

Receiving and Reporting Station

Stations

Image

Site

Review

Site

Tele-mammography Figure 3. A schematic diagram of a typical land-based componenk.

l

l

l

l

tele-mammography

This is a more expensive dial-up service to install and run than the STS. ISDN-2 can transfer a single CM image in approximately 10 min and a DDM image in approximately 40 min [S]. The ISDN-30 service uses multiplexed ISDN-2 lines to achieve a much higher data transfer rate ( - 2 megabaud), but at proportionally higher equipment, installation and running costs. The image transmission times for ISDN-30 can be estimated by reducing ISDN-2 figures above by a factor of 1.5 (i.e. to less than a minute for CM and only a few (2 or 3) min for a DDM image). A dedicated Tl line provides a dedicated high speed (1.54 megabaud), highly reliable WAN connection. Tl is much more expensive than above and is an uneconomical solution for intermittent data traffic. ATM (asynchronous transfer mode) is being promoted as the WAN technology of the future. ATM supports both LAN and WAN applications. ATM supports extremely high data transfer (155 megabaud and in time well beyond) but is currently rather specialized and expensive. ATM may be integrated with metropolitan fibre-optic WAN environments. Future ATM services will enable CM and DDM images to be transferred in a matter of seconds. Radio wave or microwave links can transmit large volumes of data at a high transfer rate. They have the advantage of cable-free connection, but as a result may be limited to line-of-site communication limiting the transmission range.

l

service, identifying

the major equipment

They are also more expensive than cable-based telecommunication. A satellite in geo-stationary orbit can sustain very high data transfer rates over vast distances, but at comparatively high cost. As satellite data-transmission is not location-specific, this technique may prove a suitable data-transfer medium for mammography screening using mobile vans. In practice, satellite-based telemammography is probably more relevant to intra- or inter-continental image data transfer (typically across U.S.A. or Europe, or between Europe and the U.S.A.).

Data compression techniques could play a useful role in improving the efficiency of the lower cost WAN technologies discussed above. Many STS modems and .ISDN-2 gateways incorporate reversible data compression, improving the data transmission figures quoted above by between 2- and 3-fold. Again, irreversible data compression could improve data transfer speeds significantly, but only within the limit of the time taken to implement the compression and de-compression algorithms at either end of the line. The ‘World Wide Web’ or ‘Internet’ has been designed to provide convenient and economical communication of multi-media data files containing digital images, graphics and text. Software to support imaging functions on the Internet including digital image compression, transmission and presentation are readily available. Technology

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options are currently being considered for providing . the broadband information super-highway, which will be required in the next century. Provided that the data security issues can be resolved it may eventually prove possible to provide tele-mammography services via the Internet.

CAD Environment

for Digital Mammography

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I I 1 I

Computer assisted diagnosis (CAD) for mammography Diagnosis from mammographic film is a difficult and demanding process due to the radiographic variability of the breast and the subtle presentation of t*he clinical indicators of breast disease. These difficulties are often compounded for patients who have radiographically dense breasts: a quarter of all women have been categorized as having dense breasts [9]. Even the most highly trained and experienced radiologist is known to misinterpret clinical signs or occasionally overlook (miss) a lesion. These difficulties may be exacerbated when the radiologist is under pressure to report large numbers of film images against time. Research has shown that a second independent film reader can often detect diagnostic features that have been completely missed by a first radiologist. However, it is not always practical or economical to provide a back-up reader. Computer assisted (or aided) diagnosis (CAD) describes the use of computerized image analysis techniques to assist the radiologist to make more effective diagnoses [lo, II]. Over 50 research groups throughout the world are currently investigating CAD techniques for digital mammography. CAD systems are designed to analyse the digital mammographic image data and then provide visual prompts that locate sites where the software ‘detects’ specific diagnostic features, The input to a CAD system can be digital image data from digitized film, or in the future from CM or DDM image acquisition systems. The CAD system comprises a suite of image analysis software usually running on a dedicated CAD workstation of sufficient computing power to enable it to complete the image analysis in a reasonable period of time. A recently launched CAD software package quotes a processing time of the order of one minute, to analyse each breast image. CAD processing times can be eased by using parallel computing techniques: for example separate processors can be used to carry out the searches for micro-calcification

CAD Results Displayed on Workstation Figure 4. A schematic diagram of a CAD environment for digital mammography, identifying relevant hardware and software componenk.

clusters and masses simultaneously. A schematic diagram of a CAD environment for digital mammography, identifying typical hardware and software components, is presented in Fig. 4. In this configuration, the CAD computer searches for tumour masses and micro-calcification clusters in parallel, producing independent CAD prompts for their respective diagnostic target. CAD prompts, identifying the clinical region(s) of interest, are then displayed as an overlay on the digital mammographic images. CAD results can either be reviewed interactively on a high-resolution workstation display or alternatively a hard copy record can be printed out. It must be appreciated that CAD software is designed not to carry out a ‘diagnosis’ as such, but a systematic search of the images to identify defined target features. The radiologist then has the responsibility of reviewing the CAD output prior to making a final clinical diagnosis. CAD systems exhibit certain advantages over human observers in that they are not subject to fatigue, boredom or distraction-although it must be remembered however, that computers are subject to crashes.

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m CAD systems are aimed at one or more of the following general clinical goals [II]: l Provision of an automated second reader to support the radiologist l Improving the accuracy of selecting patients for referral to surgical biopsy or other follow-up procedures-thereby minimizing unnecessary procedures and stress to the patient l Improved detection of lesions from images of patients with dense breasts l Automated pre-screening of large numbers of mammographic images l Risk analysis-e.g. via computerized texture analysis of breast parenchyma for automated Wolfe grading of breast patterning l Automated quality control analysis of test images

CAD algorithms, based on a wide variety of different mathematical techniques, are being developed and evaluated by research groups throughout the world. It is too early to highlight any particular CAD algorithm (or algorithms) as being the clinically most effective, however a consensus will no doubt emerge in the next few years. Algorithms used in several of the more convincing CAD proposals share certain generic features in common, and these are briefly outlined below. Figure 5 illustrates the typical stages involved in the computer assisted diagnosis of a cluster of microcalcifications using a Wavelet Transform based CAD algorithm. n

m A typical CAD algorithm designed to identify clusters of micro-calcifications might include the following computing processes 1121:

.

Individual CAD algorithms are usually designed to target specific diagnostic features in the mammographic images. Typical target features for CAD algorithms include: Clusters of micro-calcifications [12] Soft-tissue masses [13] Spiculated or stellate mass lesions [14] Breast asymmetries and architectural distortion (151 Patterns in the texture of breast parenchyma [16] Features in dense breasts [9]. n

A CAD software algorithm, aimed at identifying one of the target features listed above, usually incorporates some (or all) of the following generic operations: Data pre-processing-this is used to condition the data prior to the CAD analysis proper. Typical processes include auto-detection of the skin edge, grey-scale normalization and spatial resolution selection Digital image processing (filtering and segmentation)-this process is used to suppress background anatomy but emphasize and detect potential diagnostic features Computer vision techniques are then used to classify the detected features as relevant diagnostic targets and reject others (as false positive signals) Artificial intelligence techniques (e.g. neural networks) can be used in the final decision process Graphical prompts are then overlaid on the digital mammographic images identifying the potential sites of any target lesions

Process l

l

l

detection

High frequency filtering of images to selectively emphasize all features which look like microcalcifications and selectively suppress background anatomy This filtered image is then segmented to detect (isolate) a significant proportion of the ‘micro-calcification like’ features

Process l

l-Feature

P-Feature

classification

Detected features are then classified as potentially significant clusters of micro-calcification or rejected according to specific criteria(typical criterion-number of micro-calcificafions per unif area in fhe image-viz., significanf clusfers Cproups) retained buf individual micro-calcifications are eliminafed) Micro-calcifications in potential clusters are evaluated with regard to likelihood of malignancy using specific criteria-&pica2 criterion-d egree of irregularify of fhe shape of fhe micro-calcificafions~

n A typical CAD algorithm designed to identify malignant soft-tissue tumour masses might include the following computing processes [13]:

Process

1 -Feature

detection

0 Low-frequency filtering of images to selectively emphasize all features which look like mass lesions, and selectively suppress competing background anatomical structure

Fig;ure 5. Illustration of the stages involved in the computer a ssisted diagnosis of a cluster wt avelet Transform based CAD algorithm. (a) Region of interc st of the original CM image mi cro-calcifications. (b) Wavelet processed image data to empb rasise micro-calcification like cro-calcifications isolated from the background anatomy usir ~g CAD. (d) Original breast ;I qese images were kindly supplied by Gavin MacLeod.) 0 This filtered

0

image is then segmented to detect (isolate) a significant proportion of ‘mass lesion-like’ features Further information may also be derived by bi-lateral comparison of the images of the two breasts

l

0 Detected

classification

features are classified as potential mass lesions or rejected according to specific criteria-&pica2 criteria-size, shape, densiiy and positi

in h-east)

Potential mass lesions are evaluated with regard to likelihood of malignancy using specific cri teria--(typic621 criterion--degree 01 irreyuhuihy of the rlznrgir?of mflsses,e.g. circrfrnscriled us stellde (or spichted) f,iargin)

To summarize the story so far, CAD potentially represents an independent, reproducible and automated review of digital mammography images that could support diagnoses by the radiologist. In other words a CAD environment has the potential to operate as a computerized second reader. It must n

PI‘ocess 2-Feature

of micro-calcifications using a data containing a cluster of features, (c) Potential cluster of image data plus CAD overlay.

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be noted that CAD software is not foolproof and it is liable to generate false positive prompts and false negative responses in the same way as a human observer. Therefore the radiologist would have to assess each individual prompt produced by the CAD system with great care. It is important to note, that both human observers and computerized and CAD decision processes are subject to the matrix of possible outcomes shown below: Correct

CAD

responses

True-positive response‘Lesion present and correctly reported’ Incorrect CAD responses False-positive response‘No lesion present but incorrectly reported

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include True-negative responseNo lesion present and none reported’ include False-negative response‘Lesion present but not reported

Both types of incorrect CAD response have important clinical consequences: l A false-positive response means that a lesion will be reported where none is present, leading to unnecessary stress for the patient and redundant follow-up procedures thereby wasting staff time and resources l A false-negative response means that a dangerous lesion will be missed, giving the patient a false sense of security and allowing the tumour to grow while valuable treatment time is lost. n

n The success of a CAD algorithm is usually evaluated in terms of the ‘sensitivity’ and ‘specificity’ of the responses that are made, where, l Sensitivity is a measure of the fraction (or percentage) of true positive diagnostic responses (i.e. abnormal mammograms correctly identified by the CAD algorithm) l Specificity is a measure of the fraction (or percentage) of true negative responses (i.e. normal mammograms correctly categorized as such) n Sensitivity {True Positive (TP) response rate} and specificity {l-False Positive (FP) response rate} are inter-related such that an improvement in one factor inevitably produces a deterioration in the other. For example, l Sensitivity { TP response rate} can only be increased at a cost of an increase in false positive responses (i.e. responses to non-existent lesions)

l

Specificity (I-FP response rate} can only be improved at a cost of a reduction in true positive responses (i.e. an increase in the number of lesions missed)

The Receiver Operating Characteristic (or commonly ROC curve) is a special type of psychophysical test which can be used to scientifically evaluate the performance of a CAD system. The ROC curve is a graph depicting the increase in the fraction of ‘correct’ true positive responses as a result of allowing the fraction of false positive responses to increase [17]. In an influential paper Chan et al. (1990) [18] used the ROC methodology to show that the use of CAD can lead to a statistically significant improvement in a radiologist’s ability to detect micro-calcifications. The ROC curve provides a comprehensive assessment of the CAD system but is time consuming to carry out. A more practical adaptation of this technique, the FROC (or Free Response Operating Characteristic) is increasingly being used in the quantitative assessment of CAD environments. The FROC curve relates the fraction of true positive responses (the detection sensitivity) to the number of false positive sites per image. Quite often those working in the field prefer to quote a ‘spot’ measure of the true positive detection rate, for a particular number of accepted false positive sites per image-typically the sensitivity is quoted for an average number of false positive sites per image. Such a ‘spot’ measure provides a convenient method of comparing CAD performance. The balance of outcomes of a CAD analysis (in terms of detection sensitivity vs the resulting incidence of false-positive responses) is not fixed but can be changed by gdjusting values of the operating parameters used by the software algorithm. For example, if the number of false-positive sites is permitted to rise this will be accompanied by a greater probability of detecting real lesions. Unfortunately, such an improvement in detection sensitivity is achieved at the cost of extra work for the radiologist who has to review the increased number of CAD prompts, the majority of which could turn out to be false. The radiologist or radiographer responsible for a CAD system will have to establish operating criteria which result in a clinically acceptable balance between maximizing the detection of true lesions, while controlling the number of falsely identified features and minimizing the unnecessary work which follows.

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CAD environments to support digitized film ‘mammography have advanced very rapidly in recent years. Certain of these developments are now maturing into commercial products. Before a CAD product can be used in clinical routine it is obviously essential to carry out a large-scale evaluation using a representative population of patients, in order to rigorously establish its diagnostic viability. Clinical evaluations of various CAD environments are undeiway at a number of centres across the world. One CAD environment, developed by a U.S. industrial R. and D. group, designed for use with digitized film mammograms, has claimed that CAD detection sensitivities exceeding 90% are feasible for ‘a false-positive incidence of less th& one site (on average) per image. With the support of this CAD package it is claimed that radiologists’ detection sensitivities can be improved to 95% or beyond, for a minimum number of false-positive sites. The CAD system would &sure superior overall diagnostic performance compared with that found when the radiologist works unaided. At these levels of performance a department should feel confident in replacing a second human reader by a CAD environment. Measurements of success rates for CAD packages designed for use with direct digital mammography image data have not yet emerged. Preliminary CAD results using computed mammography image data indicate detection sensitivities of between 80% and 90% are achievable, for one or two false positive sites per image (on average) [I91 and [lo]. Further exciting advances in mammography CAD technologies are to be expected in the future.

Conclusions

and Discussion

As discussed in the first part of this tutorial [I], rapid strides are currently being made in developing direct digital mammography (DDM) image acquisition systems. Commercial DDM products are likely to emerge in the new millennium, which will lead to the replacement of screen-film mammography. This will, however, represent only the firsk step in what promises to be a revolution in the applications of digital imaging technologies jn support of mammography. Advances in digital imaging support technologies promise to enhance and improve the diagnostic performance and ergonomics in both referral and screening mammography. New digital support facilities include tele-mammography, which will exploit advanced

data communication to enable images to be transmitted from a remote acquisition site directly to the reporting centre for diagnosis. Digital image archival will lead to more relial;le and economical management of the rapidly increasing numbers of mammographic images accumulated by the breast imaging centres of the future. Computer assisted diagnosis (CAD) software and hardware environments for mammography are being developed which promise machine-based, second-reader support for the clinical staff. The physical principles and technologies behind these powerful new digital support technologies have been reviewed in this tutorial.

References 1. Cowen AR. A tutorial on digital mammography imaging equipment Part I: advances in image acquisition and display. Radiography 1998; 4: 159-71. 2. Cowen AR. A review of digital support technologies for X-ray imaging: Part Z-Digital storage and long-term archival of X-ray images. Imaging 1996A; 8: 45-55. 3. Clarkson MD, Bolet JP. Technical evaluation of the Pioneer DRM 5004X CD Jukebox for use in digital X-ray image archival-DH Medical Devices Agency-FAXiL Evaluation Report MDD/Y6/69, London: HMSO, 1996. 4. Sumsion HJ, Parkin GJS, Cowen AR. A training and assessment package for digital mammography. In: Gale AG, ed. Digibl Mummogruphy. Amsterdam: Elsevier Science, 1994: 367-73. ACR Nema. Digital imaging and communicafions in 5. medicinrDICOM: Version 3.0, Draft Standard ACRNema Committee, Working Group VI. Washington DC: ACR-NEMA, 1993. 6. Davies AS. A tutorial on computer networking for hospital staff involved with digital imaging-FAXiL Evaluation Report MDD/Y3/44, London: HMSO, 1993. 7. Cowen AR. A review of digital support technologies for X-ray imaging: Part l-Digital image communication and presentation. lrnnging 1996B; 8: 32-44. 8. Davies AG, Sumsion H, Parkin GJS. An assessment of ISDN-2 networking for teleradiology (Phase 1) and a technical evaluation of multiple ISDN-2 lines for on-line teleradiology-FAXiL Evaluation Report MDD/Y5/69, London: HMSO, 1995. 9. Jackson VP, Hendrick RE, Feig SA, Kopans DB. Imaging of the radiographically dense breast. Rndiology 1993;

M&297-301. 10. Vybomy CJ, Giger ML. Computer intelligence 699-708.

in mammography.

Il.

vision and artificial

Am J Roenfgen1994; 162:

Schmidt RA, Nishikawa RM. Digital screening mammography. Prin Pratt Oncol 1994; 8: l-16. 12. Wu Y, Doi K, Giger ML, Nishikawa RM. Computerised detection of clustered micro-calcifications in digital mammography: application of artificial neural network. Med

Phys 1992;19:555-60.

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equipment-Part

2

13. Yin FF, Giger ML, Doi K, Vyborny

CJ, Schmidt RA. Computerised detection of masses in digital mammography: investigation of feature analysis techniques. ] Dig Inmgir7g 1994; 7: 18-26. 14. Kegelmeyer WI’, Pruneda JM, Bourland I’D, Hillis A, Riggers MW, Nipper ML. Computer aided mammographic screening for spiculated lesions. Radiology 1994;

191:331-7. Yin FF, Giger ML, Doi K, Metz C, Vybomy CJ, Schmidt RA. Computerised detection of masses in digital mammograms: analysis of bilateral subtraction images. Med Phys 1991; 18: 955-63. 16. Caldwell CB, Stapleton SJ, Holdsworth DW. Characterisation of mammographic parenchymal pattern by fractal dimensions. Phys Med Bio/ 1990; 36: 235-47. 17. Metz CE. ROC methodology in radiologic imaging. invest Radio1 1986; 21: 720-33. in radiolo18. Chan HP, Doi K. Vyborny CJ. Improvements gists’ detection of micro-calcifications on mammograms: the potential of computer aided diagnosis. Immt Radio/ 15.

1990; 25:1102-11. 19. MacLeod G, Parkin GJS, Cowen AR. Automatic

detection of clustered micro-calcifications using Wavelets. In: Doi K, ed. Proc lntl Workshop Digital Mmmogr~phy Amsterdam: Elsevier Science BV, 1996;311-16. 20. MacLeod G, Cowen AR, Parkin GJS. Wavelet based computer aided detection of abnormalities in digital X-ray mammography. Proc 6th Internationnl Conference on Image Processing md its Applications-LEE Conference P&mtion No. 443. Vol. 2 London: Institution of Electrical Engineers, 1997; 828-32

Appendix Data content images

1: of digital

mammographic

The data content of a digital mammographic image, like any other digital images can be expressed in terms of megabytes (or millions of bytes) of information. Where, I bit is the basic unit of information [i.e., a zero or one binary option] 1 byte=8 bits of information and 1 Megabyte=1 million (106) bytes of information 1 Gigabyte=1 thousand million (106) bytes of information 1 Terabytez 1 million million (1012) bytes of information The total information content of an derived from the product of the number (picture elements) making up the image grey-scale content per pixel expressed as

image is of pixels and the a number

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of bytes. For example an image of matrix array size (N x N] pixels where each pixel is represented by 256 independent shades of grey is equivalent to {[N x N] X l} bytes of information. If the matrix resolution of an image is increased, the image data content increases as [N x N=N’]. Hence increasing spatial resolution by a factor of two increases total data content by a factor of four. On the other hand doubling the number of grey-scale levels per pixel (say from 256 to 512 levels) only increases data content from 8 bits to 9 bits or by one eighth of a byte. The data content of a single CR mammogram can be estimated from the number of pixels in an image [ - 2000 X 2500] and a grey-scale content of 2 bytes of information per pixel. Therefore a single CR digital mammographic image contains typically, 10 megabytes of data (or 80 megabits). Note that 2 bytes correspond to 16 bits of grey-scale information. In practice, a modem CR image actually contains 10 or 12 bits (i.e. 1024 or 4096 levels) of useful grey-scale information. The digital code is however padded out to 16 bits for computing convenience. The data content of a single ultra-high resolution DDM image of the future will typically contain 1- 4000 x 50001 pixels and a grey-scale content of 2 bytes of information per pixel. Therefore a single DDM image will typically contain, 40 megabytes of data (or 320 megabits). The equivalent data content of a single screenfilm mammography image can be estimated assuming that it typically contains [ - 8000 x 10 0001 pixels and a grey-scale content of 1.5 bytes (or 12 bits) information per pixel. Therefore a single screen-film mammography image is (in principle) equivalent to a massive, 120 megabytes of data (or 960 megabits). It is interesting to compare the figures derived above with the following l Data content of single CT or MRI image