LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues

作者：

Highlights：

• We present a method based on curvelet transform, LBP, ANOVA and PL classifier.

• We validate the proposed approach considering the metrics accuracy and AUC.

• The features was evaluated by applying the DT, RaF, SVM and PL classifiers.

• The proposed approach achieved AC values among 91% and 100%.

• The method was tested on the datasets: DDSM, BCDR-FMR, BCDR-DMR and UCSB-BB.

摘要

•We present a method based on curvelet transform, LBP, ANOVA and PL classifier.•We validate the proposed approach considering the metrics accuracy and AUC.•The features was evaluated by applying the DT, RaF, SVM and PL classifiers.•The proposed approach achieved AC values among 91% and 100%.•The method was tested on the datasets: DDSM, BCDR-FMR, BCDR-DMR and UCSB-BB.

论文关键词：Breast cancer tissues,Texture analysis,Local binary pattern,Curvelet transform,Computer aided diagnosis,Polynomial classifier

论文评审过程：Received 23 July 2015, Revised 10 February 2016, Accepted 14 February 2016, Available online 18 February 2016, Version of Record 4 March 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.02.019