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Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis

Publication ,  Conference
Singh, S; Tourassi, GD; Lo, JY
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
September 9, 2008

Tomosynthesis as a technique is being developed and studied with the goal of overcoming mammography's limitations due to overlying tissue. Various algorithms exist for tomosynthesis datasets including a novel Computer Aided Detection (CADe) algorithm using a featureless False Positive (FP) reduction stage. The goal of this project is to study the previously unexplored effects of variation of Region of Interest (ROI) sizes as well as the crucial similarity metrics for such a CADe algorithm's performance. Four datasets consisting of 1479 tomosynthesis ROIs were generated by a CADe algorithm from reconstructed volumes of one hundred subjects consisting of 4 different sizes - 128 x 128, 256 x 256, 512 x 512, and 1024 x 1024 pixels. Five different similarity metrics - (1) mutual information, (2) average conditional entropy, (3) joint entropy, (3) Jensen divergence and (4) average Kullback-Leibler divergence were used for the task of FP reduction using a leave-one-case-out sampling scheme. Mutual information and average conditional entropy were the best performing metrics with an Area Under Curve (AUC) of 0.88. Cross-bin measures performed consistently higher than those that rely on only marginal distributions. Also, for all metrics, the datatset consisting of 256 x 256 pixel ROIs gave the best performance. In conclusion, for the tomosynthesis dataset, cross-bin measures such as MI and average conditional entropy should be used over other metrics using a ROI size of 256 x 256 pixels. © 2008 Springer-Verlag Berlin Heidelberg.

Duke Scholars

Published In

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

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

September 9, 2008

Volume

5116 LNCS

Start / End Page

286 / 291

Related Subject Headings

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

Citation

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Singh, S., Tourassi, G. D., & Lo, J. Y. (2008). Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 286–291). https://doi.org/10.1007/978-3-540-70538-3_40
Singh, S., G. D. Tourassi, and J. Y. Lo. “Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5116 LNCS:286–91, 2008. https://doi.org/10.1007/978-3-540-70538-3_40.
Singh S, Tourassi GD, Lo JY. Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 286–91.
Singh, S., et al. “Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, 2008, pp. 286–91. Scopus, doi:10.1007/978-3-540-70538-3_40.
Singh S, Tourassi GD, Lo JY. Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 286–291.

Published In

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

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

September 9, 2008

Volume

5116 LNCS

Start / End Page

286 / 291

Related Subject Headings

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