AN UNSUPERVISED PARAMETER-FREE NUCLEI SEGMENTATION METHOD FOR HISTOLOGY IMAGES

被引：1

作者：

Magoulianitis, Vasileios ^{[1
]}

Han, Peida ^{[1
]}

Yang, Yijing ^{[1
]}

Kuo, C. -C. Jay ^{[1
]}

机构：

[1] Univ Southern Calif, Los Angeles, CA USA

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP | 2022年

关键词：

Histology Images; Nuclear Segmentation; Color Transform; Unsupervised; Morphological Processing;

D O I：

10.1109/ICIP46576.2022.9897887

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An unsupervised nuclei segmentation method for histology images is proposed in this work. It consists of three modules applied to each of non-overlapping blocks: 1) data-driven color transform for dimension reduction, 2) fully-automated adaptive binarization, and 3) incorporation of geometric priors with morphological processing. The method is called CBM, which comes from the first letter of the three modules - "Color transform", "Binarization" and "Morphological processing". Experiments on the MoNuSeg dataset validate the effectiveness of the proposed CBM method. It outperforms all other unsupervised methods and offers a competitive standing among supervised models based on the Aggregated Jaccard Index (AJI) metric.

引用

页码：226 / 230

页数：5

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