How Do Lesion Size and Random Noise Affect Detection Performance in Digital Mammography?

Huda, Walter; Ogden, Kent M.; Scalzetti, Ernest M.; Dance, David R.; Bertrand, Elizabeth A.

doi:10.1016/j.acra.2006.07.011

Cited by 24 publications

(19 citation statements)

References 33 publications

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“…With an anthropomorphic breast phantom study, Huda et al [Huda, 2006] also showed that the detection of millimeter sized lesions was mainly limited by anatomical noise. Bochud et al showed that the assumption of stationarity (statistical properties independent of the location in the image) did not hold for mammograms, as opposed to computer generated backgrounds that are generally stationary by construction.…”

Section: History Of Psychophysical Studies In Medical Imagingmentioning

confidence: 99%

Mass detection in breast tomosynthesis and digital mammography: a model observer study

et al. 2009

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Section: History Of Psychophysical Studies In Medical Imagingmentioning

confidence: 99%

Mass detection in breast tomosynthesis and digital mammography: a model observer study

et al. 2009

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“…Three different contrast levels were used for each size. The two smaller sizes are meant to mimic calcifications, which are relevant in the early detection of breast cancer [17]. The contrast levels were chosen in order to give a percent correct between 70-95% in the 4-AFC experiments.…”

Section: B Non-symmetric Signalmentioning

confidence: 99%

Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels

Díaz

Abbey

Timberg

et al. 2015

IEEE Trans. Med. Imaging

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Abstract-Anthropomorphic model observers are mathematical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.

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“…1- 4 The appearance of variability in an image due to normal anatomy is sometimes referred to as "structured" 5 or "anatomical" noise 4,6 indicating its detrimental effect. Dual energy imaging, [7][8][9] contrast enhanced imaging, 10 and breast magnetic resonance imaging 11 all attempt to reduce the influence of anatomical noise by selectively enhancing the contrast of suspicious lesions relative to the surrounding normal anatomy.…”

Section: Introductionmentioning

confidence: 99%

Characterizing anatomical variability in breast CT images

et al. 2008

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Previous work ͓Burgess et al., Med. Phys. 28, 419-437 ͑2001͔͒ has shown that anatomical noise in projection mammography results in a power spectrum well modeled over a range of frequencies by a power law, and the exponent ͑␤͒ of this power law plays a critical role in determining the size at which a growing lesion reaches the threshold for detection. In this study, the authors evaluated the power-law model for breast computed tomography ͑bCT͒ images, which can be thought of as thin sections through a three-dimensional ͑3D͒ volume. Under the assumption of a 3D power law describing the distribution of attenuation coefficients in the breast parenchyma, the authors derived the relationship between the power-law exponents of bCT and projection images and found it to be ␤ section = ␤ proj − 1. They evaluated this relationship on clinical images by comparing bCT images from a set of 43 patients to Burgess' findings in mammography. They were able to make a direct comparison for 6 of these patients who had both a bCT exam and a digitized film-screen mammogram. They also evaluated segmented bCT images to investigate the extent to which the bCT power-law exponent can be explained by a binary model of attenuation coefficients based on the different attenuation of glandular and adipose tissue. The power-law model was found to be a good fit for bCT data over frequencies from 0.07 to 0.45 cyc/ mm, where anatomical variability dominates the spectrum. The average exponent for bCT images was 1.86. This value is close to the theoretical prediction using Burgess' published data for projection mammography and for the limited set of mammography data available from the authors' patient sample. Exponents from the segmented bCT images ͑average value: 2.06͒ were systematically slightly higher than bCT images, with substantial correlation between the two ͑r = 0.84͒.

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How Do Lesion Size and Random Noise Affect Detection Performance in Digital Mammography?

Cited by 24 publications

References 33 publications

Mass detection in breast tomosynthesis and digital mammography: a model observer study

Mass detection in breast tomosynthesis and digital mammography: a model observer study

Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels

Characterizing anatomical variability in breast CT images

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