NDLI: Surrounding region dependence method for detection of clustered microcalcifications on mammograms

Content Provider	IEEE Xplore Digital Library
Author	Jong Kook Kim Hyun Wook Park
Copyright Year	1997
Description	Author affiliation: Dept. of Inf. & Commun. Eng., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea (Jong Kook Kim)
Abstract	Clustered microcalcifications on X-ray mammograms are an important feature in the detection of breast cancer. For the detection of the clustered microcalcification on digitized mammograms, this paper proposes a texture analysis method called the surrounding region dependence method (SRDM), which is a statistical texture analysis based on the second-order histogram in two surrounding regions. Four textural features are extracted from the SRDM. These features are used to classify region of interests (ROIs) into positive ROIs containing clustered microcalcifications and negative ROIs of normal breast tissues. The three-layer backpropagation neural network is employed as a classifier with input data of four textural features. The classification performance of the proposed method is evaluated by using the round-robin method and the receiver operating-characteristics (ROC) analysis.
Starting Page	535
Ending Page	538
File Size	580313
Page Count	4
File Format	PDF
ISBN	0818681837
DOI	10.1109/ICIP.1997.635642
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Cancer detection X-ray detection X-ray detectors Breast cancer Histograms Feature extraction Data mining Breast tissue Backpropagation
Content Type	Text
Resource Type	Article

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