Volumetric assessment of breast tissue composition from FFDM images

Keith Hartman, Ralph Highnam, Ruth Warren, Valerie Jackson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

52 Citations (Scopus)

Abstract

We present first results from a new automated algorithm for the volumetric measurement of the composition of breast tissue from digital mammograms. The new algorithm overcomes issues in previous implementations through better segmentation and use of additional information We measure the success of the new algorithm using an overall quality metric based upon the results from a large multi-site, multi-vendor, multi-detector set of digitally acquired mammograms.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages33-39
Number of pages7
Volume5116 LNCS
DOIs
StatePublished - 2008
Event9th International Workshop on Digital Mammography, IWDM 2008 - Tucson, AZ, United States
Duration: Jul 20 2008Jul 23 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5116 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Workshop on Digital Mammography, IWDM 2008
CountryUnited States
CityTucson, AZ
Period7/20/087/23/08

Fingerprint

Mammogram
Breast
Tissue
Chemical analysis
Information Measure
Segmentation
Detector
Detectors
Metric

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Hartman, K., Highnam, R., Warren, R., & Jackson, V. (2008). Volumetric assessment of breast tissue composition from FFDM images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 33-39). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5116 LNCS). https://doi.org/10.1007/978-3-540-70538-3_5

Volumetric assessment of breast tissue composition from FFDM images. / Hartman, Keith; Highnam, Ralph; Warren, Ruth; Jackson, Valerie.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5116 LNCS 2008. p. 33-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5116 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hartman, K, Highnam, R, Warren, R & Jackson, V 2008, Volumetric assessment of breast tissue composition from FFDM images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5116 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, pp. 33-39, 9th International Workshop on Digital Mammography, IWDM 2008, Tucson, AZ, United States, 7/20/08. https://doi.org/10.1007/978-3-540-70538-3_5
Hartman K, Highnam R, Warren R, Jackson V. Volumetric assessment of breast tissue composition from FFDM images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5116 LNCS. 2008. p. 33-39. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-70538-3_5
Hartman, Keith ; Highnam, Ralph ; Warren, Ruth ; Jackson, Valerie. / Volumetric assessment of breast tissue composition from FFDM images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5116 LNCS 2008. pp. 33-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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