A multiresolution wavelet analysis of digital mammograms

Author

Chen, C.H. ; Lee, G.G.

Author_Institution

Dept. of Electr. & Comput. Eng., Massachusetts Univ., North Dartmouth, MA, USA

Volume

2

fYear

1996

fDate

25-29 Aug 1996

Firstpage

710

Abstract

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

Keywords

Bayes methods; Markov processes; diagnostic radiography; fuzzy set theory; image segmentation; medical image processing; wavelet transforms; Bayesian framework; Gaussian Markov random fields; Gibbs distribution; clustered image labels; constrained optimization; digital mammograms; expected maximization; fuzzy c-means algorithm; image segmentation; localized features; multiresolution wavelet analysis; nonstatistical methods; statistical methods; Bayesian methods; Clustering algorithms; Constraint optimization; Image analysis; Image edge detection; Image resolution; Image segmentation; Image texture analysis; Spatial resolution; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1996., Proceedings of the 13th International Conference on

Conference_Location

Vienna

ISSN

1051-4651

Print_ISBN

0-8186-7282-X

Type

conf

DOI

10.1109/ICPR.1996.546915

Filename

546915