2009
DOI: 10.1117/12.811657
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Automated scoring method for the CDMAM phantom

Abstract: CDMAM phantoms are widely used in the Europe to assess the performance of mammography systems utilising small size and low contrast disc details. However, the assessment of CDMAM images by human observers is slow and tedious. An automated method for scoring CDMAM images (CDCOM) is widely available to address this issue. We have developed an alternative automated scoring tool to score CDMAM images, Quantitative Assessment System (QAS), for similarly removing inter-and intra-observer variability. This provides a… Show more

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Cited by 7 publications
(11 citation statements)
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“…w=ETgsgnEwhen the result w is above a detectability threshold, the signal of interest is deemed detectable. We calculated this measure and detectability at 5 mm intervals patch by patch throughout the breast for the full range of diameters, with an image patch size of two times the size of each FWHM . We exclude non‐breast regions from calculations by defining the skin edge and muscle region via thresholding and in‐house software, and the calculation was only done if the inserted lesion fit within the skin edge.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…w=ETgsgnEwhen the result w is above a detectability threshold, the signal of interest is deemed detectable. We calculated this measure and detectability at 5 mm intervals patch by patch throughout the breast for the full range of diameters, with an image patch size of two times the size of each FWHM . We exclude non‐breast regions from calculations by defining the skin edge and muscle region via thresholding and in‐house software, and the calculation was only done if the inserted lesion fit within the skin edge.…”
Section: Methodsmentioning
confidence: 99%
“…We calculated this measure and detectability at 5 mm intervals patch by patch throughout the breast for the full range of diameters, with an image patch size of two times the size of each FWHM. 23 We exclude nonbreast regions from calculations by defining the skin edge and muscle region via thresholding and in-house software, and the calculation was only done if the inserted lesion fit within the skin edge. In order to determine the threshold of detectability, we implemented a 2-Alternative Forced Choice (2-AFC) test 22 and calculated the threshold peak thickness value that was detectable for each lesion size at a 75 percent correct threshold.…”
Section: B3 Model Observer and Detectabilitymentioning
confidence: 99%
“…The stability of quality control for mammography can be improved using computerized evaluation methods for ACR phantom, together with other phantom such as contrast detail mammography phantom. [29][30][31] Moreover, one of the issues of this study is the comparison with other methods. [15][16][17][18][19][20] A combination of our method and other methods would improve the computerized scoring performance for ACR mammographic phantom images.…”
Section: Computerized Scorementioning
confidence: 99%
“…As tecnologias que favorecem a aquisição de mamografias no formato digital tornaram possível a automatização destes procedimentos e tem sido alvo de diversos pesquisadores que vêm se empenhando na tentativa de se criar uma ferramenta computacional precisa e adequada à rotina de controle de qualidade (PRIETO et al, 2008;YOUNG et al, 2008;YIP et al, 2009). …”
Section: Capítulo 5 Materiais E Métodosunclassified