A multiresolution wavelet analysis of digital mammograms

C. H. Chen, G. G. Lee

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

6 Citations (Scopus)


This paper discusses the significance of image segmentation via the combination of both statistical and nonstatistical methods based on the hierarchical framework of multiresolution wavelet analysis (MWA) and Gaussian Markov random fields (GMRF). Microcalculations and subtle mass regions are segmented via a fuzzy c-means (FCM) algorithm using localized features. For further enhancement, expected maximization and constrained optimization is applied to a Gibbs distribution defined from the FCM clustered image labels under a Bayesian framework. The effectiveness of this novel algorithm has been clearly illustrated by real mammographic images.

Original languageEnglish
Title of host publicationTrack B
Subtitle of host publicationPattern Recognition and Signal Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)081867282X, 9780818672828
Publication statusPublished - 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: 1996 Aug 251996 Aug 29

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other13th International Conference on Pattern Recognition, ICPR 1996

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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