Classification of Linear Structures in Mammograms Using Random Forests

被引：0

作者：

Chen, Zezhi ^{[1
]}

Berks, Michael ^{[1
]}

Astley, Susan ^{[1
]}

Taylor, Chris ^{[1
]}

机构：

[1] Univ Manchester, Sch Canc & Enabling Sci, Manchester M13 9PT, Lancs, England

来源：

DIGITAL MAMMOGRAPHY | 2010年 / 6136卷

基金：

英国工程与自然科学研究理事会;

关键词：

mammography; linear structures; classification; random forests; dual-tree complex wavelet;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area A(z) = 0.764 under the receiver operating characteristic.

引用

页码：153 / 160

页数：8

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