Digital mammography: wavelet transform and Kalman-filtering neural network in mass segmentation and detection.
OBJECTIVE
The authors developed a new adaptive module to improve their computer-assisted diagnostic (CAD) method for mass segmentation and classification. The goal was an adaptive module that used a novel four-channel wavelet transform with neural network rather than a two-channel wavelet transform with manual subimage selection. The four-channel wavelet transform is used for image decomposition and reconstruction, and a novel Kalman-filtering neural network is used for adaptive subimage selection.


METHODS
The adaptive CAD module was compared with the nonadaptive module by comparing receiver operating characteristic curves for the whole CAD system. An image database containing 800 regions of interest enclosing all mass types and normal tissues was used for the relative comparison of system performance, with electronic ground truth established in advance.


RESULTS
The receiver operating characteristic curves yield Az values of 0.93 and 0.86 with and without the adaptive module respectively, suggesting that overall CAD performance is improved with the adaptive module.


CONCLUSIONS
The results of this study confirm the importance of using a new class of adaptive CAD methods that allow a more generalized application for larger image databases or images generated from different sensors or by means of direct x-ray detection, as required for clinical trials.
Qian
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Department of Interdisciplinary Oncology, College of Medicine, H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa 33612-9497, USA.
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Song
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Clark
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D006801:Humans; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*