(acr) phantom image using image processing technique alnazer hamza...

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AUTOMATIC SCORING OF AMERICAN COLLEGE OF RADIOLOGY (ACR) PHANTOM IMAGE USING IMAGE PROCESSING TECHNIQU E

BY

Alnazer Hamza (PhD)1, M J Alport (PhD)2, W I D Rae (PhD)2 1Sudan University of Sciences and Technology, 2University of Kwa-Zulu Natal, Durban, South Africa.

ABSTRACT A phantom image is used to perform quality control in mammographic facilities. The interpretation of the ACR phantom images is depend upon the experience, visual threshold, scoring criteria and state of the observer. This paper summarizes the first step of automating this score using digital image processing, namely template matching via cross correlation, and suggests a metric for the visibility of the test objects in the images. A set of ACR phantom images was obtained. These images were presented to a medical physicist, who indicated the number of circular test objects visible in each image. The radiographs of the phantom are scanned with 8 bits depth resolution at 600 dpi. These images are then filtered using a Gaussian filter having a width that is optimized to the scale size of the feature. A new circle edge detector was developed since the conventional Sobel and Roberts edge detectors are omnidirectional local operators, which do not take into account the geometry of the feature. The results were compared with our template matching technique. Our proposed visibility metric was consistent with the physicist’s scoring results and has the added advantage that it quantitatively scores the ACR phantom. In addition a critical value of the visibility could be defined which can then be applied automatically for quality control purposes to digital mammography systems. KEYWORDS : Phantom, mammography, Digital mammography, Automatic detection, Template matching algorithm

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تقييم . عادة ما تستخدم صور النماذج لمراقبة الجودة في أجهزة تصوير الثدي الشعاعيطربقه ,عتبة البصر, يعتمد على الخبرة (ACR)الصور الناتجه من نموذج كلية األشعة األمريكية

تلخص هذه الورقه الخطوة األولى في استخدام الحاسوب . التقببم المستخدمة و حالة المقيموذلك باستخدام طريقة مطابقة األشكال ACR ومعالجة الصور الرقمية في تقييم صور نموذج


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(Template Matching with Coss-Correlation). آلية جديدة لتقييم المقترح هو تطويرتم أخذ مجموعة من صور نموذج . صور مراقبة جوده اجهزه تصوير الثدي الشعاعي أوتوماتيكياً

ACR تم . ثم عرضت على فيزيائي طبي لتقيمهاوذلك بتعداد األشكال الواضحة في الصورعد هذه الصور ب, dpi 600بت و 8) عمق(تحويل صور األشعه لألنموذج الي صور رقمية بتمييز

في . ذو العرض المتغير على حسب أشكال النموذج Gaussianذلك رشحت باستخدام مرشح التقليديه Robertsو Sobelهذه الدراسه أيضا تم تطوير كاشف للدوائر جديد حيث إن كواشف

ثم قورنت هذه النتائج ببرنامج مطابقة األشكال . ال تأخذ في االعتبار األبعاد الهندسية لألشكالخلصت الدراسه إلى أن الطريقة الجديدة المقترحة متفقة . األتوماتيكي الذي طور في هذه الدراسة

فاصل بين وضوح أشكال مع تقييم الفيزيائي و تتميز عليه بأن التقييم كمي باإلضافة إلى أن حدالنموذج من عدم الوضوح يمكن تعريفه ويمكن بعد ذلك أن يطبق األسلوب الجديد أوتوماتيكبا

.ألغراض مراقبة الجوده في أجهزه تصوير الثدي باألشعة الرقمية1.0 INTRODUCTION

A number of test objects, embedded in phantoms, are used to assess image quality in mammographic systems: these include the TOR (MAMMOGRAPHY) phantom[1], Barts[2] and Dupont[3] phantoms. The RMI-156 or Nuclear Associates mammographic phantoms usually used for American College of Radiology (ACR) mammography accreditation program[4]. These phantoms are designed to attenuate the X-ray beam in the same way as a human breast[5]. Subjective judgments about images are always difficult. Different individuals will typically perceive different numbers of test objects in the image. Furthermore, the same individual may count a different number of objects in the same image at different times[6]. The interpretation of the resultant QC images is however dependent upon the experience, visual threshold, scoring criteria and state of the observer[4]. It is well documented that inter-observer variability can mask any changes in the performance of a system or confound comparative exercises across several system[7]. The advantage of digital mammography (DM) is to provide direct capture of digital data for image analysis. QC by computer based quantitative evaluation will likely replace human observation, in order to avoid intra - and inter-observer variance (caused by e.g. experience, fatigue, training, compulsiveness, visual acuity) in QC in this new modality[4, 5, 10]. The standard geometric shapes in the ACR phantom suggest that an automated


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image processing algorithm can be developed which can match human acceptance levels.

The idea to analyze a reference phantom digital image to determine the image quality produced by mammographic equipment is not new. Automated methods for image assessment have been developed in the United States to support the ACR accreditation programme for mammography[8]. In one study, an operator-independent algorithm which emulates human detection was develope[4] and applied to digitized images of ACR phantom. This image quality algorithm was developed to establish some visibility criterions for the phantom test objects, which are located using a technique based on the Fast Fourier Transform (FFT). Template matching algorithm has been used[9] to search for potential masses on a mammogram. Using the radiological characteristics of a circumscribed mass (approximately circular shape, and they vary in size); a simple prototype model of a circumscribed mass was generated. A range of sizes for the templates is used in the search for region of interest (ROIs). The templates are used to search for the potential lesions by cross-correlating the template with sub-regions of the mammographic image. These studies and the associated algorithms are specific to the given test object in each study, but they highlight the potential of computational methods for image quality assessment. No single automatic algorithm is yet available which can score the test objects (masses, specks and fibres) in the ACR phantom.

In this paper we attempt to carry out an objective QC evaluation of the mammographic image using reference phantoms (RMI-156), by processing its digitize image in an automatic way using digital image processing techniques specifically developed for this type of QC image. The digital image processing analysis provides information about the phantom characteristics which are not easily obtained by means of direct observation of the image. Information about the test objects allows characterizations of the phantom image obtained and use these parameters in order to determine the image quality. The small size and the low contrast of the test objects make them difficult to be seen by observers. So, it is important to establish some visibility criteria for the different test objects, which can be analysed in an automatic way, and in approximately the same way as done by an expert medical physicist, but producing reproducible quantitative results.


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2.0 MATERIAL AND METHODS 2.1 PHANTOM This study was based on a set of images taken of ACR phantom (Model–156, # 0203024). In the ACR (RMI-156) accreditation phantom (Fig. 1 and Table. 1), the test objects (artefacts) and their approximate locations are defined a priori. The shapes include: six rectangular shaped nylon fibres slanted at ± 45˚ to simulate soft tissue edges, five groups with six spherical specks of aluminium oxide in each group to simulate micro-calcifications (specks) and five larger lens shape water density masses (Phenolic wafers) to simulate tumours. These test objects are constructed so that their visibility in the resultant mammographic images ranges from the easily visible to the invisible, and, therefore, these objects straddle the threshold of visibility[10]. The X-ray image of the ACR phantom should permit visualization of the largest four fibrils, three speck groups with the largest specks and largest three masses. Under standard testing procedures, the average number of objects detected in each group (masses, specks and fibrils) should not changed by more than 0.5 if viewed under ideal conditions by the same observer[6]. The small size and weight of the ACR phantom makes it convenient to mail between the institution and the analysis site[11]. The depiction of these objects in mammographic images is usually scored by qualified medical physicists[12]. Table 1 description of the ACR phantom (RMI 156) :

Label Diameter (mm) Label Diameter (mm) Label Thickness (mm) F1 1.56 S1 0.54 M1 2.00 F2 1.12 S2 0.40 M2 1.00 F3 0.89 S3 0.32 M3 0.75 F4 0.75 S4 0.24 M4 0.50 F5 0.54 S5 0.16 M5 0.25 F6 0.40 - - - -

test object dimensions shown in Fig. 1.

Fig. 1 a radiograph of the ACR phantom showing the light grey, fibres (F1, F2, F3, F4, F5, F6), specks groups (S1,

S2, S3, S4, S5), and masses (M1, M2, M3, M4, M5).

F1 F2 F3 F4

F5 F6 S1 S2

M2 M3 M4 M5

S3 S4 S5 M1


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2.2 Film acquisition and digitization The objects in the ACR phantom image contain ill-defined edges

therefore the boundary of these test objects could not easily be detected. Since the change in intensity of many edge pixels in the image is very low, it is difficult to entirely separate the edges from the noise signal. Images for ACR accreditation phantom were taken (using A Phillips mammography unit, Philips Mammo Diagnost manufactured on January 1992, located at Addignton Hospital, Durban/ South Africa was used to obtain most of the radiograph) whilst the X-ray tube output (mAs) and X-ray voltage (kVp) were systematically varied. The developed algorithms were applied to different images of the ACR phantom, obtained with the same mammographic equipment under different clinical conditions. In the first experiment, the tube voltage was kept constant at 28 kVp and the tube current was varied between 10 and 125 mAs. In the second experiment exposures were performed at 80 mAs with X-ray kVp varying between 22 and 28 kVp. This specific tube voltage (22 kVp and 28 kVp) was selected because this is the X-ray range which is utilised on most mammography systems to image the female breast.

Sixteen images were presented to three medical physicists, who indicated the number of circular masses, specks and fibres visible in each image. The radiographs were then scanned with an Epson Expression TM 1640 XL scanner (Model EU-22, # 015119, Seiko Epson Corporation, Japan), producing images of maximum size, 2021× 2038 pixels, and a grey scale resolution of 8 bits/pixel with spatial resolution of 600 dpi (dot per inch), that is to say, the grey range has 256 values and saved in an image file using a lossless TIFF format. The radiographs of the ACR phantom then cross-correlated using a Gaussian kernel having a width that is optimized to the scale size of the features (masses, specks and masses). Novel circle and fibre edge detectors were developed and applied to ACR phantom test objects. The results from medical physicists were compared with our template matching algorithm technique (TMA).

Initially, in this study, a set of image processing algorithms were developed to quantitatively analyse each of the different artefacts found in the ACR phantom. Each phantom area (test object) in the ACR phantom required a specific algorithm. A set of digitized ACR phantom images were passed to the TMA for feature extraction, detection and scoring. The results were then compared to the medical physicist's scores. The ACR phantom


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image (Fig. 1) is divided automatically into 16 sub-images, so that each one of the phantom interest areas fall into one of the sub-images. The readers (three experienced medical physicists in Addignton Hospital, Durban/ South Africa) scored the ACR phantom original images for visibility of (masses, specks and fibres) and the mean value was taken and compared to the TMA

scores, generated by the IDL algorithms. The medical physicists viewed images of the ACR phantom under similar conditions and provided individual scores for each test objects (masses, specks and fibres). 2.3 TEMPLATE MATCHING ALGORITHM (TMA) Fig. 2 shows TMA flowchart, which used to detect and score visibility of the test objects in the ACR phantom images.

After the original ACR phantom images have been obtained and scanned, sixteen sub-images (corresponding to the number of test objects in the phantom) were extracted in the region of the masses, specks and fibres. The sub-images were cross correlated with a Gaussian kernel (computed in the Fourier domain) having a width (obtained from the fitted Gaussian and

Fit Gaussian and quadratic to find the width of the test object

Apply the circle or fibre edge detectors

Find the height of each peak, (0A )

Find the average of the background, B

Find the standard deviation of the background,Bσ

Calculate the visibility using B

BAV

σ−

= 0

Original ACR phantom image

Cross correlate the image with Gaussian kernel using this width


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quadratic) that was optimised to the scale size of the test objects. This resulted in visible peaks for all structures (Fig. 4). Circle and fibre edge detectors were developed as new techniques to enhance the imaged masses, specks and fibres by considering the specific characteristics of these test objects. The Visibility Index, V, was then calculated automatically. 3.0 RESULTS AND DISCUSSION 3.1 APPLYING THE TMA TO THE ACR PHANTOM IMAGES

The template-matching algorithm (shown in Fig. 2) was used to find the artefacts (masses, specks and fibres) in the ACR (RMI-156) phantom image shown in (Fig. 1). To find the individual objects in the image they were modelled as a symmetric Gaussian kernel for specks and masses because the shapes of the masses and the specks are lens and spherical respectively and this tend to follow a Gaussian distribution. For the fibres are empirically found to be well fitted to symmetric Gaussian distributions. The fibres require a non symmetric Gaussian kernel. The test objects (artefacts) in the ACR phantom images were then searched using these Gaussian kernels which had a width that were optimized to the scale size of the artefacts. This process was obtained through cross correlation using FFT.

A shade surface plot of the greyscale variation (Fig. 3) of the ACR phantom image shows a number of characteristics. Firstly, no recognizable large-scale features can be seen apart from large spikes which results from the

Fig. 3 shows a shade surface plot of the raw ACR (RMI-

156) phantom image shown in Fig. 1.

M2

M1

M3 M4

Fig.4 shows the ACR phantom image after applying a symmetric Gaussian kernel. The width of the kernel was selected to search only for M1.


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high intensity micro-calcification and other salt and pepper noise. This demonstrates the need for specialised image processing algorithms to identify and measure the visibility of the artefacts. Secondly, there is an overall decrease in intensity from the back to the front edge due to the intensity fall-off from the X-ray tube (heel effect). After the Gaussian kernel with a width of =28.30 pixels and =28.30 pixels was applied to filter the

entire ACR phantom image the mass labelled M1 (Fig. 4) is now more visible compared to the ACR phantom image (Fig. 3). The other mass (M2, M3 and M4) are visible, but to a lesser extent since the optimised width chosen for M1 is not optimised for these other masses. The M1 is not a global maximum due to the ramp in intensity which maximised due to the heel effect. 3.2 A NEW EDGE DETECTORS FOR CIRCULAR AND LINEARSTRUCTUR E

Two new novel techniques called circle and fibre edge detectors have been developed. These can be applied to enhance circular or ring features which are required to identify masses, specks and fibres. What is required is edge detector that only detects circular masses, spherical specks or rectangular fibres. The normal linear edge detectors, such as Sobel and Roberts are very local operators that do not depend on the larger scale structure of the edge region[13, 14], furthermore they are omnidirectional local operators, which do not take into account the geometry of the feature.

xσ yσ

Global max

Ridge

Fig. 5 shows the ACR phantom image after circular edge detector was applied to Fig.4. The width of the kernel (28.30 pixels) was selected to search for M1 in

the ACR phantom the value was obtained from the Gaussian fit function.

After the circle edge detector was applied, the Gaussian peak for M1 is now well identified by being a global maximum other than the spurious high edge due to the edge effect at y ~ 600 (Fig.5). The background, which steadily increases in the y-direction, has now been removed. The high ridge at y ~ 600 is attributed to an edge effect and should typically be cropped. The Gaussian peak now has an amplitude, which is significantly larger than the standard deviation of the noise in the background throughout the image.

A


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3.3 The results of the test object detection (ACR phantom sub-images) Sixteen sub-images were extracted in the region of the masses specks and fibres from the entire ACR phantom image. The sub-images were cross correlate with Gaussian kernel having a width that is optimised to the scale size of the masses specks and fibres resulted in visible peaks. These visible peaks are not global maximum due to the geometry of the mammographic unit. Circle and fibre edge detectors were applied to correct for this effect. 3.3.1 Mass detection There are five masses in the ACR (RMI-156) phantom. The thickness of the masses decreases thus, spanning a range of visibilities. Although the shade surface plot in Fig. 6 shows no recognizable large-scale mass apart from large spikes come from salt and pepper noise. The masses detection for each sub-image contains one mass has been carried out by TMA (Fig. 1).

After the sub-image of M1 from the entire ACR phantom image was

cross correlated with the symmetric Gaussian kernel, visible peak of the M1 was obtained. Further more the

M1

M1

Fig. 6 shows a sub-image (475 X 487 pixels) containing a single mass extracted from the entire ACR phantom image in the region of M1 (Fig. 1).

Fig.7 shows the ACR phantom image after applying the symmetric Gaussian kernel and circle edge detector. The width of the kernel (88.13 pixels) was selected to search only for M1 in the ACR phantom.


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mass peak was more visible and being global maximum after the circular edge detector was applied (Fig. 7). 3.3.2 SPECK DETECTION

There are five groups of micro-calcifications (Specks) in the ACR (RMI-156) phantom. Each group is constituted by six specks, placed approximately in the vertexes and centre of a regular pentagon. The diameter of the specks in each group decreases thus, spanning a range of visibilities. Although the shade surface (Fig. 8) shows noisy peaks for all the six specks in S1 as it is expected since the diameter of S1 (0.54 mm) is quite large compare to S5 (0.16 mm). The micro-calcifications identification has been carried out by TMA (Fig. 2).

After the TMA was applied to sub-image containing the S1 group of micro-calcifications in ACR phantom (Fig. 1), the shade surface (Fig. 14) shows recognizable large peaks corresponding to the 6 micro-calcifications. The visibility of the specks varies in the group but almost all have the same diameter and hens similar Gaussian fit widths (from 3.76 - 3.74 pixels). The ACR manufacturer included this varying visibility within the group to allow

S1

S1

Fig. 8 shows a sub-image (483× 431 pixels) containing a single group of six specks (S1) extracted from the entire ACR phantom image in the region of S1 (Fig. 1).

Fig. 9 shows the detected micro-calcifications (S1) in the ACR phantom using a Gaussian kernel with width 3.8 pixels and the circular edge detector.


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more discrimination and gives the medical physicist the option of partially scoring a group.

3.3.3 FIBRE DETECTION

Interesting the observer fibril scores were predicted just as well as the specks and masses, in spite of the fact that no computer measurements on fibrils were made[14, 16]. This work introduces a novel approach on fibre detection. There are six rectangular shaped nylon fibres slanted at ± 45˚ in the ACR (RMI-156) phantom to simulate soft tissue edges. The diameters of the fibres decrease in size. They are oriented at ± 45˚ to give an estimate of the spatial resolution in all directions. The Hough line transform was used to measure the angle of each fibre. The shade surface for F1 (Fig. 10) shows only background noise. Using TMA and our specialised fibre edge detector, the fibres can be detected and the Visibility Index can be measured and compared to medical physicist scores.

After the ACR phantom image of F1 was cross correlated with the asymmetric Gaussian kernel rotated with the angle θ , visible elongated peak of the fibre was obtained. Furthermore the fibre peak was more visible and being global maximum after the fibre edge detector was applied (Fig. 11). The Gaussian peak now has an amplitude, which is significantly larger than the standard deviation of the noise in the background (Fig. 12). 3.3.4 CALCULATION OF THE VISIBILITY INDEX

The noise can be evaluated globally by quantifying the fluctuations in the image of a uniform region, or determining the typical deviation and the mean value of the grey levels in a chosen area. A 50× 50 pixels region were extracted from a uniform area adjacent to M1 (Fig. 1) and the pixel values were averaged to obtain the background pixel value ( ). An estimate of the noise magnitude was made by computing the grey level standard deviation ( ) in the same region. This is shown by the white square (Fig. 5).

and

Where: n is the number of pixels in the image and is the pixel greyscale value at position in the extracted 50× 50 pixels region. Our

B

Bσ

∑=ji

jiBn

B,

),(1 ( )∑ −=

jiB BjiBn ,

2),(

1σ

),( jiBji,


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TMA Visibility Index, will given by Where is the mean

of the background, is the height of the peak and is the standard deviation of the background. 3.3.5 COMPARISON BETWEEN TMA AND MEDICAL PHYSICISTS’ SCOR ES

The aim of these experiments is to produce degraded radiographs and to compare the values obtained with our Visibility Index metric with the scores of medical physicist. After three medical physicists scored a set of 16 ACR phantom images taken with different exposures (varying kVp and mAs). The mean and the standard deviations for their scores together with TMA scores are shown in Fig.12 and Fig. 13. Constant kVp and varying mAs Constant mAs and varying kVp

VBσ

BAV

−= 0 B

0A Bσ

Fig. 13 shows the TMA scores (��) and the mean scores (□) which obtained by three medical physicists for masses, specks and fibres in 4 ACR phantom images as a function of kVp for tube current=80 mAs. The error bars correspond to the standard deviations of the observers’ scores.

Fig. 12 shows the TMA scores (��) and the mean scores (□) which obtained by three medical physicists for masses, specks and fibres in 7 ACR phantom images as a function of mAs for tube voltage=28 kVp. The error bars correspond to the standard deviations of the observers’ scores.


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A rapid increase (Fig. 12) from an average of 1 mass visible at 10 mAs to 3 masses visible at 28 mAs. The Visibility Index remains constant at 36 mAs and 63 mAs afterwards start to decrease. The number of visible specks increased from an average of 11 specks at 10 mAs to average of 23 specks at 63 mAs and then started to decrease. The number of visible fibres increased from 3 at 10 mAs to approximately 5 at 63 mAs and then decreased. There is variability between the medical physicists who scored the images (error bars). The TMA scores are not statistically different (t-test, p> 0.05) from the observers’ mean scores and in most cases fall in that range. The scoring remains constant (average of 3 masses visible) at 22 and 26 kVp (Fig. 13) afterwards the scoring decreased. The number of visible specks increased from an average of 18 specks at 22 kVp to average of 22 specks at 26 kVp and then started to decrease. The number of visible fibres increased from 4 at 22 kVp to approximately 6 at 26 kVp and then decreased. In all cases, the TMA has yielded scores, which have been comparable to (t-test, p>0.05) the scores from the human observers used as controls. The variability between the medical physicists scores are shown by the standard deviation error bars.

The Visibility Index of the different insert objects (masses, specks and fibres) in the ACR phantom image were calculated automatically. After defining the Visibility Index ( ) the cut off Visibility ( ), can be defined

as that when of the artefacts (masses, specks and fibres) become similar to the visibility of the well defined background noise then we can define the artefacts as being undetected. It is possible to determine the critical Visibility of the noise for the ACR phantom images since the standard number of artefacts were known; 6 nylon fibres, 5 specks groups with 6 specks in each group and 5 masses. When the Visibility Index of test objects becomes similar to the Visibility Index of the noise, then the TMA can define the test objects as being undetected. This cut off Visibility Index is used to determine the number of detected artefacts in ACR phantom, which can then be applied automatically for QC purposes to DM systems.

The effectiveness of the TMA has been evaluated by comparing its performance against three trained medical physicists. The algorithm scores are consistent with those of the human (medical physicist) but without influence of human variability. Student t-test shows no significant (p>0.05) different between the computer scores and the mean of three medical physicists scored masses, fibres and specks. The TMA technique described is

V CV

V


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reliable (gave results similar to those of medical physicists scoring the ACR phantom images) and can be applied as QC technique, which can be routinely applied to digital images and it agrees with the latest results[17] which showed that an automatic phantom system was more precise, reproducible and sensitive than standard phantom scoring by five readers.

Note that, in one respect the TMA or for that matter any computer method is fundamentally different from human observer (i.e. medical physicists) visual evaluations. Medical physicists need the marginally visible target objects, since they quantify the measurements by comparing the target features to the noise level. The medical physicists who visualize six fibres, five groups of specks and five masses are conveying the information that the sixth fibre, fourth group of specks and fourth mass are marginally visible, and that the fifth group of specks and the fifth mass were essentially invisible, because the lesion signal to noise ratio was low. A computer on the other hand, can in sometime perform well on this below human noise level and gives scores (e.g. the masses at 10 and 20 mAs, specks at 63, 80 and 125 mAs and fibres at 10 and 63 mAs), because it does not need the marginally visible targets. The template-matching method is practical and analysis time is minimal. The software automatically analyzes images at less than one minute per image and generates a QC report. 4.0 CONCLUSIONS

The mammographic image quality is determined by an expert medical physicist evaluating phantom images visually. The dependence on human factors results in a variability of the scoring results. In this work a method has been developed to quantitatively analyse the ACR phantom images by means of automatic image processing techniques. The methods used had to locate the test objects automatically and suggests a metric for their visibility. Much research effort is used to solve common problems in the field of image processing. These problems include poor edge detection for object localization in low contrast images including i.e. mammograms. The noise in the ACR phantom images is greater in regions of artefacts (fibres, specks and masses). One can use image processing to decrease image noise. TMA based on cross correlation computed in Fourier domain was found to be an efficient solution. The TMA was employed to provide localization of the test objects in ACR phantom images. This work


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introduces a novel, efficient approach to mammography QC image processing.

Automatic image-processing algorithm to implement the QC process in mammographic imaging was developed. This ensured that the human observer, and the inherent variability of that observer, is removed from the analysis. This work summarizes the first step of automating this scoring process using digital image processing and suggests a metric for the visibility of the masses, specks and fibres in the images. It uses template matching which searches for objects within an image based on the similarity of image regions to predefined templates (kernels). We found that as the mAs and kVp increased, the number of visible masses increased to a maximum, after which the number of visible objects decreased. Our proposed Visibility Index metric was consistent with the physicists' scoring results and has the added advantage that it quantitatively and objectively scores the masses, specks and fibres. In addition a critical value of the Visibility Index could be defined which can then be applied automatically for QC purposes to DM systems. A fully automatic algorithm for the detection of the insert objects masses, micro-calcification and fibres were developed.

The implication from this is that automatic scoring of ACR phantom images is feasible and could be used as a tool to help eliminate the effect of observer variability in SFM as well as digital mammograms. The automatic scoring provides an accurate automated measurement. It would not suffer from criterion uncertainty, between reader variability and inter-phantom variability. Given the availability of high quality film digitizer and direct digital acquisition systems (i.e. DM) such approaches are now quite feasible and could be used as an adjunct to the visual evaluations provided by the human and substantially improve the overall process. ACHNOWLEDGMENT

The authors gratefully acknowledge the contributions of mammography technologists at Addington Hospital, Durban, South Africa especially P. Baxter. The authors gratefully acknowledge and thank the contributions of the medical physicists who scored the phantom images, Ann Sweetlove, Professor Charles Herbst at University of Free State, Mahesh Rana from King Edward Hospital and Rosemary Rock from Kodak Company. We appreciate the organizations which provided financial


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support for this research: Sudan University for Science and Technology for providing teaching assistantships and the MRC of South Africa for funding the project.

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