Medical image classification based on multi-scale non-negative sparse coding

作者：

Highlights：

• We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms.

• We utilize multi-scale decomposition method to decompose images into multiple scale layers and extract more representative image features.

• We introduce fisher discriminative analysis algorithm to non-negative sparse coding model thus to exploit more contextual spatial information.

• Our model performs superior to other related algorithms in terms of efficiency and classification accuracy.

摘要

•We propose a multi-scale non-negative sparse coding model to construct visual dictionary thus to overcome the defects of BoVW-based algorithms.•We utilize multi-scale decomposition method to decompose images into multiple scale layers and extract more representative image features.•We introduce fisher discriminative analysis algorithm to non-negative sparse coding model thus to exploit more contextual spatial information.•Our model performs superior to other related algorithms in terms of efficiency and classification accuracy.

论文关键词：Medical image classification,The semantic gap,Multi-scale decomposition,Non-negative sparse coding,Fisher discriminative analysis

论文评审过程：Received 31 December 2016, Revised 21 April 2017, Accepted 11 May 2017, Available online 27 May 2017, Version of Record 17 November 2017.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.05.006