What is the minimum amount of simulated breast movement required for visual detection of blurring? An exploratory investigation

Kei, Wang; Aspin, Rob; Kelly, Judith; Millington, Sara; Hogg, Peter

doi:10.1259/bjr.20150126

Cited by 10 publications

(31 citation statements)

References 14 publications

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“…This was not the case for microcalcifications, where detection performance became statistically worse as the magnitude of blurring was increased. The previous work 13 has suggested that motion blur is visible at 0.7 mm for a soft-edged blur, and our work seems to confirm this. This could have implications for practice as it could mean that when blur is observed in an image, repeat imaging should be considered as in clinical work one would simply not know how much blurring is present and what impact it is having.…”

Section: Discussionsupporting

confidence: 88%

“…13 Simulated motion blur was applied using a convolution mask that provided a 3 standard deviation (3SD) distribution of blur over the desired blur radius. The 3SD range is consistent with the application of a Gaussian blur mask, typically used to generate generic blur effects (equivalent to a semi-transparent film being placed over an image).…”

Section: Simulated Blurring and Image Displaymentioning

confidence: 99%

“…In accordance with observations made by Ma et al, 12,13 two levels of simulated blurring were used in our study and images were evaluated under 3 conditions -without blurring (0 mm), and with two For the free-response receiver operating characteristic (FROC) study, images were displayed on a 5-Megapixel reporting grade monitor calibrated to the DICOM Grey Scale Display Function (GSDF) Standard. 24 Ambient room lighting was set to below 10 lux.…”

Section: Simulated Blurring and Image Displaymentioning

confidence: 99%

“…11 Recent research identified paddle motion to be present in a number FFDM machines during in the clamping phase, with estimates of motion being as high as 1.7 mm. 12,13 Further reports suggest that the visual impact of simulated image blurring can be detected from 0.4mm of movement. 14 Anecdotal evidence within the National Health Service Breast Screening Programme (NHSBSP) suggests that image blurring may require images to be repeated, thus increasing patient radiation dose, anxiety, and service costs.…”

Section: Introductionmentioning

confidence: 99%

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The impact of simulated motion blur on lesion detection performance in full-field digital mammography

Abdullah

Kelly

Thompson

et al. 2017

The British Journal of Radiology

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Objective: Motion blur is a known phenomenon in full-field digital mammography, but the impact on lesion detection is unknown. This is the first study to investigate detection performance with varying magnitudes of simulated motion blur.Method: Seven observers (15±5 years' reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for three conditions: no blurring (0 mm) and two magnitudes of simulated blurring (0.7 mm and 1.5 mm).Abnormal cases were biopsy proven. Mathematical simulation was used to provide a pixel shift in order to simulate motion blur. A free-response observer study was conducted to compare lesion detection performance for the three conditions. The equally weighted jackknife alternative freeresponse receiver operating characteristic (wJAFROC) was used as the figure of merit. Test alpha was set at 0.05 to control probability of Type I error.Results: wJAFROC analysis found a statistically significant difference in lesion detection Advances in knowledge:This research demonstrates for the first time that motion blur has a negative and statistically significant impact on lesion detection performance digital mammography.

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Section: Discussionsupporting

confidence: 88%

Section: Simulated Blurring and Image Displaymentioning

confidence: 99%

Section: Simulated Blurring and Image Displaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The impact of simulated motion blur on lesion detection performance in full-field digital mammography

Abdullah

Kelly

Thompson

et al. 2017

The British Journal of Radiology

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“…1 Motion blur can occur for various reasons, including patient movement and paddle motion during the clamping phase. 1,3 Blurry mammograms may need to be repeated; one screening unit in the UK reported that image blur is the main reason for 90% of recalls. 4 Furthermore, one screening service reported that 0.86% of screening candidates were recalled because of blurred mammograms.…”

Section: Introductionmentioning

confidence: 99%

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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Closed‐loop control of compression paddle motion to reduce blurring in mammograms

2017

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10Background: Since the introduction of full field digital mammography (FFDM) a large number of UK breast cancer screening centers have reported blurred images, which can be caused by movement at the compression paddle during image acquisition.Purpose: To propose and investigate the use of position feedback from the breast side of the compression paddle to reduce the settling time of breast side motion. 15Method: Movement at the breast side of the paddle was measured using two calibrated linear potentiometers. A mathematical model for the compression paddle, machine drive and breast was developed using the paddle movement data. Simulation software was used to optimize the position

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What is the minimum amount of simulated breast movement required for visual detection of blurring? An exploratory investigation

Abstract: What is the minimum amount of simulated breast movement required for visual detection of blurring? An exploratory investigation. Br J Radiol 2015; 88: 20150126.

Cited by 10 publications

References 14 publications

The impact of simulated motion blur on lesion detection performance in full-field digital mammography

The impact of simulated motion blur on lesion detection performance in full-field digital mammography

Automatic detection of simulated motion blur in mammograms

Closed‐loop control of compression paddle motion to reduce blurring in mammograms

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