Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization

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In this letter, we present a novel watershed-based method for segmentation of cervical and breast cell images. We formulate the segmentation of clustered nuclei as an optimization problem. A hypothesis concerning the nuclei, which involves a priori knowledge with respect to the shape of nuclei, is tested to solve the optimization problem. We first apply the distance transform to the clustered nuclei. A marker extraction scheme based on the H-minima transform is introduced to obtain the optimal segmentation result from the distance map. In order to estimate the optimal h-value, a size-invariant segmentation distortion evaluation function is defined based on the fitting residuals between the segmented region boundaries and fitted models. Ellipsoidal modeling of contours is introduced to adjust nuclei contours for more effective analysis. Experiments on a variety of real microscopic cell images show that the proposed method yields more accurate segmentation results than the state-of-the-art watershed-based methods.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2010-10
Language
English
Article Type
Article
Keywords

SEGMENTATION; IMAGES

Citation

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.57, no.10, pp.2600 - 2604

ISSN
0018-9294
DOI
10.1109/TBME.2010.2060336
URI
http://hdl.handle.net/10203/20847
Appears in Collection
EE-Journal Papers(저널논문)
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