Publication Type

Journal Article

Version

Publisher’s Version

Publication Date

4-2018

Abstract

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets.

Keywords

Cell segmentation, Self-supervised learning, Support vector machine, White blood cell, Automatic labeling of training data

Discipline

Health Information Technology | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Micron

Volume

107

First Page

55

Last Page

71

ISSN

0968-4328

Publisher

Elsevier

Embargo Period

3-28-2021

Additional URL

https://doi.org/10.1016/j.micron.2018.01.010

Download

Share

COinS