Segmentation of clustered nuclei based on curvature weighting

Authors:
Chao Zhang;Changming Sun;Ran Su;Tuan D. Pham
Affiliations:
The University of New South Wales, Canberra, ACT, Australia;CSIRO Mathematics, Informatics and Statistics, NSW, Australia;The University of New South Wales, Canberra, ACT, Australia;The University of Aizu, Fukushima, Japan
Venue:
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Year:
2012

Citing 5
Cited 0

Random Walks for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A rule-based approach for robust clump splitting

Pattern Recognition
Splitting touching cells based on concave points and ellipse fitting

Pattern Recognition
Clump splitting via bottleneck detection and shape classification

Pattern Recognition
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cluster of nuclei are frequently observed in thick tissue section images. It is very important to segment overlapping nuclei in many biomedical applications. Many existing methods tend to produce under segmented results when there is a high overlap rate. In this paper, we present a curvature weighting based algorithm which weights each pixel using the curvature information of its nearby boundaries to extract markers, each of which represents an object, from input images. Then we use marker-controlled watershed to obtain the final segmentation. Test results using both synthetic and real cell images are presented in the paper.