A quantum-clustering optimization method for COVID-19 CT scan image segmentation

作者：

Highlights：

• This study introduces the novel fast forward quantum optimization algorithm (FFQOA).

• The FFQOA is hybridized with the K-means clustering (KMC) algorithm.

• The FFQOAK (FFQOA+KMC) is applied in the segmentation of chest CT images of COVID-19 patients.

• Performance evaluation metrics indicate the effectiveness of the proposed algorithm.

• The proposed algorithm is able to recognize the infected regions effectively.

摘要

•This study introduces the novel fast forward quantum optimization algorithm (FFQOA).•The FFQOA is hybridized with the K-means clustering (KMC) algorithm.•The FFQOAK (FFQOA+KMC) is applied in the segmentation of chest CT images of COVID-19 patients.•Performance evaluation metrics indicate the effectiveness of the proposed algorithm.•The proposed algorithm is able to recognize the infected regions effectively.

论文关键词：Coronavirus Disease 2019 (COVID-19),K-means clustering (KMC) algorithm,Fast forward quantum optimization algorithm (FFQOA),Computed tomography (CT) images,Image segmentation

论文评审过程：Received 17 November 2020, Revised 25 April 2021, Accepted 18 July 2021, Available online 28 July 2021, Version of Record 3 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115637