Noise estimation and measures for detection of clustered microcalcifications

Márton Csapodi, Ágota Petrányi, György Liszka, A. Zarándy, T. Roska

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

Abstract

Image transforms used to preprocess mammogram images and highly selective microcalcification feature extraction are analyzed in this paper. It is demonstrated that the results obtained by the proposed method (especially at high true positive rates) exceed the specificity levels of other measures for which FROC analysis is provided using the same public (Nijmegen) database.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages412-417
Number of pages6
Volume1613
ISBN (Print)3540661670, 9783540661672
Publication statusPublished - 1999
Event16th International conference on Information Processing in Medical Imaging, IPMI 1999 - Visegrad, Hungary
Duration: Jun 28 1999Jul 2 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1613
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th International conference on Information Processing in Medical Imaging, IPMI 1999
CountryHungary
CityVisegrad
Period6/28/997/2/99

Fingerprint

Noise Estimation
Microcalcifications
Feature extraction
Mammogram
Feature Extraction
Specificity
Exceed
Transform

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Csapodi, M., Petrányi, Á., Liszka, G., Zarándy, A., & Roska, T. (1999). Noise estimation and measures for detection of clustered microcalcifications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1613, pp. 412-417). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1613). Springer Verlag.

Noise estimation and measures for detection of clustered microcalcifications. / Csapodi, Márton; Petrányi, Ágota; Liszka, György; Zarándy, A.; Roska, T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1613 Springer Verlag, 1999. p. 412-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1613).

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

Csapodi, M, Petrányi, Á, Liszka, G, Zarándy, A & Roska, T 1999, Noise estimation and measures for detection of clustered microcalcifications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1613, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1613, Springer Verlag, pp. 412-417, 16th International conference on Information Processing in Medical Imaging, IPMI 1999, Visegrad, Hungary, 6/28/99.
Csapodi M, Petrányi Á, Liszka G, Zarándy A, Roska T. Noise estimation and measures for detection of clustered microcalcifications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1613. Springer Verlag. 1999. p. 412-417. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Csapodi, Márton ; Petrányi, Ágota ; Liszka, György ; Zarándy, A. ; Roska, T. / Noise estimation and measures for detection of clustered microcalcifications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1613 Springer Verlag, 1999. pp. 412-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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