Scholars@Duke

Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images

Publication ,  Conference
Li, H; Giger, ML; Yuan, Y; Lan, L; Suzuki, K; Jamieson, A; Yarusso, L; Nishikawa, RM; Sennett, C
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2006
Published version (DOI)

We have developed computerized methods for the analysis of mammo-graphic lesions in order to aid in the diagnosis of breast cancer. Our automatic methods include the extraction of the lesion from the breast parenchyma, the characterization of the lesion features in terms of mathematical descriptors, and the merging of these lesion features into an estimate of the probability of malignancy. Our initial development was performed on digitized screen film mammograms. We report our progress here in converting our methods for use with images from full-field digital mammography (FFDM). It is apparent from our initial comparisons on CAD for SFMD and FFDM that the overall concepts and image analysis techniques are similar, however reoptimization for a particular lesion segmentation or a particular mammographic imaging system are warranted. © Springer-Verlag Berlin Heidelberg 2006.

Duke Scholars

Li Lan
Author Li Lan Molecular Genetics and Microbiology

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/11783237_77

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2006

Volume

4046 LNCS

Start / End Page

569 / 575

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Li, H., Giger, M. L., Yuan, Y., Lan, L., Suzuki, K., Jamieson, A., … Sennett, C. (2006). Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4046 LNCS, pp. 569–575). https://doi.org/10.1007/11783237_77
Li, H., M. L. Giger, Y. Yuan, L. Lan, K. Suzuki, A. Jamieson, L. Yarusso, R. M. Nishikawa, and C. Sennett. “Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4046 LNCS:569–75, 2006. https://doi.org/10.1007/11783237_77.
Li H, Giger ML, Yuan Y, Lan L, Suzuki K, Jamieson A, et al. Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 569–75.
Li, H., et al. “Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4046 LNCS, 2006, pp. 569–75. Scopus, doi:10.1007/11783237_77.
Li H, Giger ML, Yuan Y, Lan L, Suzuki K, Jamieson A, Yarusso L, Nishikawa RM, Sennett C. Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 569–575.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/11783237_77

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2006

Volume

4046 LNCS

Start / End Page

569 / 575

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences