On Digital Mammogram Segmentation and Microcalcification Detection Using Multiresolution Wavelet Analysis

C. H. Chen, G. G. Lee

研究成果: Article同行評審

54 引文 斯高帕斯(Scopus)

摘要

In this paper a multiresolution wavelet analysis (MWA) and nonstationary Gaussian Markov random field (GMRF) technique is introduced for the detection of microcalcifications with high accuracy. The hierarchical multiresolution wavelet information in conjunction with the contextual information of the images extracted from GMRF provides an efficient technique for microcalcification detection. A Bayesian learning paradigm realized via the expectation maximization (EM) algorithm was also introduced for edge detection or segmentation of mass regions recorded on the mammograms. The strength of the technique is in the effective utilization of the rich contextural information in the images considered. The effectiveness of the approach has been tested with a number of mammographic images for which the microcalcification detection algorithm achieved a sensitivity (true positive rate) of 94% and specificity (true negative rate) of 88%. Considerably good results were also obtained for the segmentation algorithm. In addition, the results for both the detected microcalcifications and the segmented mass regions were superimposed for an interesting case under the methods introduced.

原文English
頁(從 - 到)349-364
頁數16
期刊Graphical Models and Image Processing
59
發行號5
DOIs
出版狀態Published - 1997 九月

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Vision and Pattern Recognition
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design

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