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Effect of image quality on calcification detection in digital mammography
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Image of FIG. 1.

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FIG. 1.

Examples of clusters used in the observer study inserted in four different categories of background: (a) fatty, (b) mixed without high density structure, (c) mixed with high density structure, and (d) glandular. Each image segment is 200 × 200 pixels (pixel size of 70 μm.

Image of FIG. 2.

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FIG. 2.

Characterization measurements on Hologic Selenia DR system and simulated CR used in study. (a) NEQ for DR and simulated CR (incident air kerma to detector = 89 μGy) and (b) MTF for DR and simulated CR.

Image of FIG. 3.

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FIG. 3.

Processed regions of interest (200 × 200 pixels) from an image in the study at all image qualities; (a) normal dose DR images with Hologic image processing, (b) normal dose DR images with Agfa image processing, (c) half dose DR with Agfa image processing, (d) quarter dose DR images Agfa image processing, (e) normal dose CR with Agfa image processing, and (f) half dose CR with Agfa image processing.

Image of FIG. 4.

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FIG. 4.

CDMAM test object.

Image of FIG. 5.

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FIG. 5.

Characteristics of extracted calcification clusters: (a) Cluster diameters and (b) number of calcifications in a cluster.

Image of FIG. 6.

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FIG. 6.

AFROC curves for two observers [(a) and (b)] inspecting the six image qualities. The crosses show the raw data points calculated using the confidence scores for three of the image qualities. The associated error bars show the 95% confidence intervals estimated using bootstrapping. Data points and error bars for the remaining three image qualities have been omitted to reduce clutter in the figure.

Image of FIG. 7.

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FIG. 7.

Reader-averaged AFROC curves showing performance at all six image qualities.

Image of FIG. 8.

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FIG. 8.

Difference in FoM between several image quality pairs (error bars indicate 95% confidence intervals).

Image of FIG. 9.

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FIG. 9.

Threshold gold thickness at five different image qualities: DR at normal, half, and quarter dose levels shown with disc points, and CR at normal and half dose levels shown with square points: (a) 0.1 mm gold disc diameter and (b) 0.25 mm gold disc diameter. Acceptable and achievable standards as set in the European protocol (Ref. 15) are also shown along with dose limit for a breast thickness equivalent to 50 mm PMMA.

Image of FIG. 10.

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FIG. 10.

Reader-averaged JAFROC FoM from the observer study plotted against the threshold gold thickness from CDMAM phantom images at the same IQ for (a) 0.10 mm and (b) 0.25 mm gold disc diameter. DR at normal, half, and quarter dose levels shown with cross points, and CR at normal and half dose levels shown with diamond points The results from the observer study include only images processed using Agfa image processing. The CDMAM analysis was performed on the unprocessed image. Error bars represent the 95% confidence interval. The acceptable and achievable limits as set in the European protocol (Ref. 15) are displayed as dashed lines.


Generic image for table

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Reader-averaged JAFROC and ROC FoM and reader-averaged LLF at a NLF equal to 0.1 for all six image qualities. Image qualities 1 and 2 are clinical images processed with both Hologic and Agfa Musica-2 image processing. Image qualities 3–6 are simulated image qualities from the original clinical images.


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This study aims to investigate if microcalcification detection varies significantly when mammographicimages are acquired using different image qualities, including: different detectors,dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection.


One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography(CR)imagingsystem at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data.


There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC (AFROC) area decreased from 0.84 to 0.63 and the ROC area decreased from 0.91 to 0.79 (p < 0.0001). This corresponded to a 30% drop in lesion sensitivity at a NLF equal to 0.1. Detection was also sensitive to the dose used. There was no significant difference in detection between the two image processing algorithms used (p > 0.05). It was additionally found that lower threshold gold thickness from CDMAM analysis implied better cluster detection. The measured threshold gold thickness passed the acceptable limit set in the EU standards for all image qualities except half doseCR. However, calcification detection varied significantly between image qualities. This suggests that the current EU guidelines may need revising.


Microcalcification detection was found to be sensitive to detector and dose used. Standard measurements of image quality were a good predictor of microcalcification cluster detection.


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Scitation: Effect of image quality on calcification detection in digital mammography