Adapting Breast Density Classification from Digitized to Full-Field Digital Mammograms

Tortajada, Meritxell; Oliver, Arnau; Martí, Robert; Vilagran, Mariona; Ganau, Sergi; Tortajada, Lidia; Sentís, Melcior; Freixenet, Jordi

doi:10.1007/978-3-642-31271-7_72

Cited by 19 publications

(10 citation statements)

References 9 publications

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“…We note that in the literature there have also been a number of studies comparing the use of SFM and FFDM images in development of CAD algorithms. 29,30,31,32,33,34,35,36 Collectively, these studies demonstrate the potential of applying the CAD algorithms developed previously on SFM images to FFDM images, and vice versa. Such exchangeability of SFM and FFDM images could also benefit temporal mammogram diagnosis which is shown to have the potential to improve the detection and diagnosis performance of MC lesions.…”

Section: Introductionsupporting

confidence: 57%

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Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography

2017

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The quantitative results demonstrate that MC lesions in for-presentation images are highly consistent with that in for-processing images in terms of image features, detectability of individual MCs, and classification accuracy between malignant and benign lesions. These results indicate that for-presentation images can be compatible with for-processing images for use in CAD algorithms for MC lesions.

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Section: Introductionsupporting

confidence: 57%

“…We note that in the literature there have also been a number of studies comparing the use of SFM and FFDM images in development of CAD algorithms . Collectively, these studies demonstrate the potential of applying the CAD algorithms developed previously on SFM images to FFDM images, and vice versa.…”

Section: Introductionmentioning

confidence: 68%

Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography

2017

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“…Moreover, the physical limits of the mass, which were not visible on the original mammograms, could properly be assessed after enhancement. On the other hand, automatic analysis, such as computer aided detection and breast density measurement systems can also profit from this enhancement [43,44].…”

Section: Discussionmentioning

confidence: 99%

Breast peripheral area correction in digital mammograms

Tortajada

Oliver

Martí

et al. 2014

Computers in Biology and Medicine

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Digital mammograms may present an overexposed area in the peripheral part of the breast, which is visually shown as a darker area with lower contrast. This has a direct impact on image quality and affects image visualisation and assessment. This paper presents an automatic method to enhance the overexposed peripheral breast area providing a more homogeneous and improved view of the whole mammogram. The method automatically restores the overexposed area by equalising the image using information from the intensity of non-overexposed neighbour pixels. The correction is based on a multiplicative model and on the computation of the distance map from the breast boundary. A total of 334 digital mammograms were used for evaluation. Mammograms before and after enhancement were evaluated by an expert using visual comparison. In 90.42% of the cases, the enhancement obtained improved visualisation compared to the original image in terms of contrast and detail. Moreover, results show that lesions found in the peripheral area after enhancement presented a more homogeneous intensity distribution. Hence, peripheral enhancement is shown to improve visualisation and will play a role in further development of CAD systems in mammography.

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“…Comparison of the accuracy of breast density classification of ULDP with respect to(Muštra et al, 2012) and(Tortajada et al, 2012) using INbreast. 2012) was made.…”

mentioning

confidence: 99%

“…As there exist few works that have used digital databases, a quantitative comparison with only two related works(Tortajada et al, 2012;Muštra et al, …”

mentioning

confidence: 99%

Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern

Abdel-Nasser

Rashwan

Puig

et al. 2015

Expert Systems with Applications

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Adapting Breast Density Classification from Digitized to Full-Field Digital Mammograms

Cited by 19 publications

References 9 publications

Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography

Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography

Breast peripheral area correction in digital mammograms

Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern

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