Patterns of breast skin thickness in normal mammograms

Willson, S. A.; Adam, E.J.; Tucker, A.K.

doi:10.1016/s0009-9260(82)80407-8

Cited by 43 publications

(26 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using this new modality, Huang et al (2008) found an average skin thickness for individual women ranging from 0.9 to 2.3 mm with an overall average of 1.45 mm and Vedantham et al (2012a) found an average thickness of 1.44 mm. Earlier estimates of the thickness range from screen-film mammography, were slightly larger, for example 0.7 to 3.0 mm (Pope et al , 1984 and Willson et al , 1982). …”

Section: Breast Size and Compositionmentioning

confidence: 93%

Dosimetry in x-ray-based breast imaging

2016

View full text Add to dashboard Cite

The estimation of the mean glandular dose to the breast (MGD) for x-ray based imaging modalities forms an essential part of quality control and is needed for risk estimation and for system design and optimisation. This review considers the development of methods for estimating the MGD for mammography, digital breast tomosynthesis (DBT) and dedicated breast CT (DBCT). Almost all of the methodology used employs Monte Carlo calculated conversion factors to relate the measurable quantity, generally the incident air kerma, to the MGD. After a review of the size and composition of the female breast, the various mathematical models used are discussed, with particular emphasis on models for mammography. These range from simple geometrical shapes, to the more recent complex models based on patient DBCT examinations. The possibility of patient-specific dose estimates is considered as well as special diagnostic views and the effect of breast implants. Calculations using the complex models show that the MGD for mammography is overestimated by about 30% when the simple models are used. The design and uses of breast-simulating test phantoms for measuring incident air kerma are outlined and comparisons made between patient and phantom-based dose estimates. The most widely used national and international dosimetry protocols for mammography are based on different simple geometrical models of the breast, and harmonisation of these protocols using more complex breast models is desirable.

show abstract

Section: Breast Size and Compositionmentioning

confidence: 93%

Dosimetry in x-ray-based breast imaging

2016

View full text Add to dashboard Cite

show abstract

“…It has been reported that the skin thickness in breast is 1.45 ± 0.30 mm. 41,42 We can calculate the skin voxels based on the voxel size of the breast CT images. In our studies, as the breast CT voxel size is 0.27 × 0.27 × 0.27 mm 3 , we constrain the skin thickness within seven voxels.…”

Section: Iid Morphologic Operations For the Skinmentioning

confidence: 99%

Cupping artifact correction and automated classification for high‐resolution dedicated breast CT images

Yang

Sechopoulos

et al. 2012

Medical Physics

View full text Add to dashboard Cite

show abstract

“…45±0. 30 mm [12,13]. We can calculate the voxel for the skin based on the resolution of the breast CT images.…”

Section: Methodsmentioning

confidence: 99%

Automatic tissue classification for high-resolution breast CT images based on bilateral filtering

2011

View full text Add to dashboard Cite

Breast tissue classification can provide quantitative measurements of breast composition, density and tissue distribution for diagnosis and identification of high-risk patients. In this study, we present an automatic classification method to classify high-resolution dedicated breast CT images. The breast is classified into skin, fat and glandular tissue. First, we use a multiscale bilateral filter to reduce noise and at the same time keep edges on the images. As skin and glandular tissue have similar CT values in breast CT images, we use morphologic operations to get the mask of the skin based on information of its position. Second, we use a modified fuzzy C-mean classification method twice, one for the skin and the other for the fatty and glandular tissue. We compared our classified results with manually segmentation results and used Dice overlap ratios to evaluate our classification method. We also tested our method using added noise in the images. The overlap ratios for glandular tissue were above 94. 7% for data from five patients. Evaluation results showed that our method is robust and accurate.

show abstract

Patterns of breast skin thickness in normal mammograms

Cited by 43 publications

References 4 publications

Dosimetry in x-ray-based breast imaging

Dosimetry in x-ray-based breast imaging

Cupping artifact correction and automated classification for high‐resolution dedicated breast CT images

Automatic tissue classification for high-resolution breast CT images based on bilateral filtering

Contact Info

Product

Resources

About