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Image feature extraction for mass detection in digital mammography: Influence of wavelet analysis
Author(s) -
Qian Wei,
Li Lihua,
Clarke Laurence P.
Publication year - 1999
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598531
Subject(s) - artificial intelligence , pattern recognition (psychology) , feature extraction , preprocessor , feature (linguistics) , wavelet , digital mammography , computer science , segmentation , mammography , computer vision , ground truth , image segmentation , linear discriminant analysis , region of interest , wavelet transform , breast cancer , medicine , philosophy , linguistics , cancer
Rationale and objectives. The objective of this work is to evaluate the importance of image preprocessing, using multiresolution and multiorientation wavelet transforms (WTs), on the performance of a previously reported computer assisted diagnostic (CAD) method for breast cancer screening, using digital mammography. Method: An analysis of the influence of WTs on image feature extraction for mass detection is achieved by comparing the discriminant ability of features extracted with and without the wavelet‐based image preprocessing using computed ROC. Three indexes are proposed to assess the segmentation of the mass area with comparison to the ground truth. Data was analyzed on the region‐of‐interest (ROI) database that included mass and normal regions from digitized mammograms with the ground truth. Results: The metrics for the measurement of segmentation of the mass clearly demonstrated the importance of image preprocessing methods. Similarly, the relative improvement in performance was observed in feature extraction based on the evaluation of the ROC curves, where the Az values are increased, for example, from 0.71 to 0.75 for a pixel intensity feature and from 0.72 to 0.85 for a morphological feature of the Normalized Deviation of Radial Length. The improvement, therefore, depends on the feature characteristics, being large for boundary‐related features while small for intensity‐related features. Conclusion: The use of image preprocessing modules using wavelet transforms results in a significant improvement in feature extraction for the previously proposed CAD detection method. We are therefore exploring additional improvement in wavelet‐based image preprocessing methods, including adaptive methods, to achieve a further improvement in performance and an evaluation on larger image databases.