.png){kind=icon}
.png){kind=icon}
Return
An optimal nuclei segmentation method based on enhanced multi-objective GWO
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-021-00547-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In breast cancer image analysis, reliable segmentation of the nuclei is still an open-ended research problem. In this paper, a new clustering-based nuclei segmentation method is presented. First, the proposed method pre-processes the histopathology image through SLIC method. Then, a novel variant of multi-objective grey wolf optimizer is employed to group the obtained super-pixels into optimal clusters. Lastly, the optimal cluster with minimum value is segmented as the nuclei region. The experimental results demonstrates that the proposed variant of multi-objective grey wolf algorithm surpasses the existing multi-objective algorithms over ten standard multi-objective benchmark functions belonging to different categories. Particularly, the proposed variant has achieved best fitness value of more than 0.90 on 90% of the considered functions. Further, the nuclei segmentation accuracy of the proposed method is validated on H&E-stained estrogen receptor positive (ER+) breast cancer images. Experimental results illustrates that the proposed method has attained dice-coefficient value of more than 0.52 on 80% of the images. This illustrates that the proposed method is efficient in producing efficacious segmenting over histology images of Breast cancer.
Keywords:
Breast cancer diagnosis
Nuclei segmentation
Multi-objective grey wolf optimizer
Journal
IF:
4.6
Papers:
2.1K
Citations:
6.6K