Conference Paper/Proceeding/Abstract 871 views
A Multi-Stage Random Forest Classifier for Phase Contrast Cell Segmentation
Ehab Essa,
Rachel Errington,
Nick White,
Xianghua Xie 
37th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society
Swansea University Author:
Xianghua Xie
Abstract
We present a machine learning based approach to automatically detect and segment cells in phase contrast images. The proposed method consists of a multi-stage classification scheme based on random forest (RF) classifier. Both low level and mid level image features are used to determine meaningful ce...
| Published in: | 37th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society |
|---|---|
| Published: |
2015
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa22235 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
We present a machine learning based approach to automatically detect and segment cells in phase contrast images. The proposed method consists of a multi-stage classification scheme based on random forest (RF) classifier. Both low level and mid level image features are used to determine meaningful cell regions. Pixel-wise RF classification is first carried out to categorize pixels into 4 classes (dark cell, bright cell, halo artifact, and background) and generate a probability map for cell regions. K-means clustering is then applied on the probability map to group similar pixels into candidate cell regions. Finally, cell validation is performed by another RF to verify the candidate cell regions. The proposed method has been tested on U2-OS human osteosarcoma phase contrast images. The experimental results show better performance of the proposed method with precision 92.96% and recall 96.63% compared to a state-of-the-art segmentation technique. |
|---|---|
| Keywords: |
Cell segmentation, medical image analysis, random forests |
| College: |
Faculty of Science and Engineering |