Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform

作者：

Highlights：

• We proposed a novel microcalcification diagnosis method in digital mammography.

• The combined HMT and wavelet features are used to describe the microcalcifications.

• The evaluation is performed on the Nijmegen, MIAS and DDSM datasets.

• The results show the effectiveness of the proposed method in accuracy and stability.

摘要

•We proposed a novel microcalcification diagnosis method in digital mammography.•The combined HMT and wavelet features are used to describe the microcalcifications.•The evaluation is performed on the Nijmegen, MIAS and DDSM datasets.•The results show the effectiveness of the proposed method in accuracy and stability.

论文关键词：Microcalcification diagnosis,Digital mammography,Dual-tree complex wavelet transform,Hidden Markov tree model,Extreme learning machine,Feature extraction

论文评审过程：Received 1 December 2016, Revised 5 May 2017, Accepted 26 May 2017, Available online 26 May 2017, Version of Record 3 June 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.05.062