Blurred digital mammography images: an analysis of technical recall and observer detection performance

Kei, Wang; Borgen, Rita; Kelly, Judith; Millington, Sara; Hilton, Beverley; Aspin, Rob; Lança, Carla Costa; Hogg, Peter

doi:10.1259/bjr.20160271

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…The study of Ma et al tended to agree with this and suggested that low resolution monitors are not suitable for the detection of blur. 23 In this study the comparison took place between two monitors with different specifications and 2.3 and 5 MP resolution respectively. It appeared that the monitor with 2.3 MP resolution had poorer visual detection for blur and consequently higher technical recall rate than the higher resolution monitor.…”

Section: Articles Addressing Monitor Specificationsmentioning

confidence: 99%

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A systematic review of viewing conditions and monitor specifications in mammography

2020

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Section: Articles Addressing Monitor Specificationsmentioning

confidence: 99%

“…Ma et al, 2017, [23] Blurred digital mammography images: An analysis of technical recall and observer detection performance UK 5 MP monitor better for blur detection on mammographic images…”

Section: Ukmentioning

confidence: 99%

A systematic review of viewing conditions and monitor specifications in mammography

2020

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“…Often blur fails to be detected at the time of imaging, and it might not be detected until it reaches the reporting room for diagnostic examination. 9 While there have been studies investigating radiologists' ability to detect blur visually in mammograms, as of this date there is no work done on detecting blur automatically in mammograms except the work presented by Hill et al 8 In addition, there has been no effort to use machine-learning algorithms to automatically detect blurry mammograms. Instead of relying on the radiologists' subjective assessments of the presence or absence of blur in mammograms, we propose the use of quantitative mathematical features that quantify blur objectively in digital mammograms.…”

Section: Introductionmentioning

confidence: 99%

“…False‐negative decisions could have implications in clinical practice. Often blur fails to be detected at the time of imaging, and it might not be detected until it reaches the reporting room for diagnostic examination . While there have been studies investigating radiologists' ability to detect blur visually in mammograms, as of this date there is no work done on detecting blur automatically in mammograms except the work presented by Hill et al In addition, there has been no effort to use machine‐learning algorithms to automatically detect blurry mammograms.…”

Section: Introductionmentioning

confidence: 99%

“…This study has two aims: to develop a software‐based image processing algorithm that simulates motion blur to mimic real blur seen at various levels in mammograms; and to investigate quantitative blur measurements and to train machine‐learning classifiers to detect simulated blur automatically in mammograms. In the following sections, the methodology and rationale for the blur simulation model, blur quantification approaches, and the process of training machine‐learning classifiers will be discussed.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of simulated motion blur in mammograms

Kamona

Loew

2020

Medical Physics

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Purpose: To use machine-learning algorithms and blur measure (BM) operators to automatically detect motion blur in mammograms. Motion blur has been reported to reduce lesion detection performance and mask small abnormalities, resulting in failure to detect them until they reach more advanced stages. Automatic detection of blur could support the clinical decision-making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety. Methods: Blur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point-spread-function (PSF) mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super-sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur PSF mask. This randomly generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 434 mammograms from the INbreast and DDSM databases, respectively. Blur was quantified using nine BM operators for each mammogram and at each blur level. The mammograms were assigned to training (70%) and testing (30%) datasets to train three machine-learning classifiers: Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six-way classification. Results: For the INbreast mammograms, the average classification accuracies were 87.7%, 85.7%, and 85.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively, and the average classification accuracies for DDSM were 93.5%, 93.6%, and 92.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively. Conclusions: Preliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists' performance, there is evidence that motion blur might not be detected visually by a human observer and could negatively affect radiologists' lesion detection performance. As of this date, no other study has investigated the ability of machine-learning classifiers and BM operators to detect motion blur in mammograms.

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The Evaluation of Clinical Image Quality in Mammography

Poggi

2024

Breast Imaging Techniques for Radiographers

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Blurred digital mammography images: an analysis of technical recall and observer detection performance

Abstract: According to this study, monitors ≤2.3 MP are not suitable for technical review of full-field digital mammography images for the detection of blur. Advances in knowledge: This research proposes the first observer standard for the visual detection of blurring.

Cited by 11 publications

References 21 publications

A systematic review of viewing conditions and monitor specifications in mammography

A systematic review of viewing conditions and monitor specifications in mammography

Automatic detection of simulated motion blur in mammograms

The Evaluation of Clinical Image Quality in Mammography

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