Statistical textural features for detection of microcalcifications in digitized mammograms

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Clustered microcalcifications on X-ray mammograms are an important sign for early detection of breast cancer. Texture-analysis methods can be applied to detect clustered microcalcifications in digitized mammograms. In this paper, a comparative study of texture-analysis methods is performed for the surrounding region-dependence method, which has been proposed by the authors, and conventional texture-analysis methods, such as the spatial gray-level dependence method, the gray-level run-length method, and the gray-level difference method. Textural features extracted by these methods are exploited to classify regions of interest (ROI's) into positive ROI's containing clustered microcalcifications and negative ROI's containing normal tissues. A three-layer backpropagation neural network is used as a classifier. The results of the neural network for the texture-analysis methods are evaluated by using a receiver operating-characteristics (ROC) analysis. The surrounding region-dependence method is shown to be superior to the conventional texture-analysis methods with respect to classification accuracy and computational complexity.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
1999-03
Language
English
Article Type
Article
Keywords

COMPUTER-AIDED DIAGNOSIS; CLUSTERED MICROCALCIFICATIONS; DIGITAL MAMMOGRAMS; AUTOMATED DETECTION; ROC

Citation

IEEE TRANSACTIONS ON MEDICAL IMAGING, v.18, no.3, pp.231 - 238

ISSN
0278-0062
URI
http://hdl.handle.net/10203/9879
Appears in Collection
EE-Journal Papers(저널논문)
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