Contrast-detail comparison between unprocessed and processed CDMAM images

Zanca, Federica; Bosmans, Hilde; Jacobs, Jurgen; Michielsen, Koen; Sisini, Francesco; Nens, Joris; Young, Kenneth C.; Shaheen, Eman; Jacobs, A.; Marchal, Guy

doi:10.1117/12.811732

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Studies comparing IQF inv results from raw and processed images are available in the literature [11][12][13][14] , but it is recommended that 1 cm spacer should be used on phantom edges to better simulate breast thickness with respect to European standards. For this reason, in our study, 60 mm breast thickness was simulated by using 1cm thick spacer.…”

Section: Methodsmentioning

confidence: 99%

Use of Processed Mammography Images as a Quality Control Tool

Bektemür¹

2018

Haydarpasa Numune Med J

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According to international quality control testing standards, image quality determination in mammography systems is based on measuring contrast-detail measurements with contrast-detail for mammography (CDMAM) phantom. It is therefore important to evaluate the CDMAM phantom images correctly. The purpose of this study is to compare the inverse image quality figure index (IQF inv) obtained by automatically evaluating digital CDMAM phantom images with Artinis software (version 2.2, CDCOM) for raw and processed images. Methods: Digital phantom images were acquired in three different full-field digital mammography systems by using automatic exposure conditions (AEC). Five different phantom images were obtained from each mammography system as raw and processed as well as evaluated by the software. In addition, the IQF inv values of the systems were compared for mammography systems and the difference in between values was observed. Results: The differences between the IQF inv values of raw and processed images were found to be 1.34%, 5.62%, and 6.7% for System A, B, and C, respectively. Discussion and Conclusion: It was observed that the differences between the IQF inv values of raw and processed images were almost negligible and the IQF inv values obtained from the raw images were slightly higher than the values obtained from the processed images.

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Section: Methodsmentioning

confidence: 99%

Use of Processed Mammography Images as a Quality Control Tool

Bektemür¹

2018

Haydarpasa Numune Med J

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“…It has been shown that the threshold gold thickness measured with the CDMAM phantom is predictive of calcification detection [7,8], and, to a lesser extent, mass detection [8]. Studies have investigated if the CDMAM curves from raw images can be predicted from CDMAM curves with processed images [9]. However, the authors are unaware of any work investigating if change in CDMAM curves correlates with change in cancer detection when different image processing algorithms are applied.…”

Section: Introductionmentioning

confidence: 98%

Impact of two types of image processing on cancer detection in mammography

et al. 2016

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The impact of image processing on cancer detection is still a concern to radiologists and physicists. This work aims to evaluate the effect of two types of image processing on cancer detection in mammography. An observer study was performed in which six radiologists inspected 349 cases (a mixture of normal cases, benign lesions and cancers) processed with two types of image processing. The observers marked areas they were suspicious were cancers. JAFROC analysis was performed to determine if there was a significant difference in cancer detection between the two types of image processing. Cancer detection was significantly better with the standard setting image processing (flavor A) compared with one that provides enhanced image contrast (flavor B), p = 0.036. The image processing was applied to images of the CDMAM test object, which were then analysed using CDCOM. The threshold gold thickness measured with the CDMAM test object was thinner using flavor A than flavor B image processing. Since Flavor A was found to be superior in both the observer study and the measurements using the CDMAM phantom, this may indicate that measurements using the CDMAM correlate with change in cancer detection with different types of image processing.

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“…While CDMAM phantoms may not be suitable to detect an impact of these processing algorithms (cf. [7]), several studies point towards their importance for the detection of microcalcifications in clinical images (e.g. [8,9]).…”

Section: Introductionmentioning

confidence: 99%

Determination of contrast-detail curves in mammography image quality assessment by a parametric model observer

et al. 2019

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A novel approach is proposed for the determination of contrast-detail curves in mammography image quality assessment. The approach is compared with current practice using virtual mammography.A binary parametric model observer is applied to images of the CDMAM phantom. The observer accounts for the simple disc shaped objects in the phantom and is applied separately to each cell of the phantom. For each of these applications, the area under the ROC curve (AUC) of the model observer is determined. The different AUCs, calculated from different applications of the parametric model observer, are then combined to a single contrastdetail curve quantifying the ability of the observer to detect details in the images. Virtual mammography is developed as a tool to simulate X-ray images of single CDMAM cells and to quantitatively assess the approach in comparison with current practice.It is shown that the proposed approach can lead to similar contrast-detail curves as current practice. The precision of the estimated contrast-detail curves is increased, i.e. using 5 images yields about the same precision for the proposed approach as 16 images when applying current practice.We conclude that contrast-detail curves in mammography image quality assessment can also be determined through the AUC of a binary parametric model observer. Since the proposed approach has higher precision than current practice, it is a promising candidate for contrast-detail analysis in mammography image quality assessment.

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Contrast-detail comparison between unprocessed and processed CDMAM images

Cited by 3 publications

References 6 publications

Use of Processed Mammography Images as a Quality Control Tool

Use of Processed Mammography Images as a Quality Control Tool

Impact of two types of image processing on cancer detection in mammography

Determination of contrast-detail curves in mammography image quality assessment by a parametric model observer

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