Journal article
Separating Touching Cells Using Pixel Replicated Elliptical Shape Models
IEEE transactions on medical imaging, v 38(4), pp 883-893
Apr 2019
PMID: 30296216
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
One of the most important and error-prone tasks in biological image analysis is the segmentation of touching or overlapping cells. Particularly for optical microscopy, including transmitted light and confocal fluorescence microscopy, there is often no consistent discriminative information to separate cells that touch or overlap. It is desired to partition touching foreground pixels into cells using the binary threshold image information only, and optionally incorporating gradient information. The most common approaches for segmenting touching and overlapping cells in these scenarios are based on the watershed transform. We describe a new approach called pixel replication for the task of segmenting elliptical objects that touch or overlap. Pixel replication uses the image Euclidean distance transform in combination with Gaussian mixture models to better exploit practically effective optimization for delineating objects with elliptical decision boundaries. Pixel replication improves significantly on commonly used methods based on watershed transforms, or based on fitting Gaussian mixtures directly to the thresholded image data. Pixel replication works equivalently on both 2-D and 3-D image data, and naturally combines information from multi-channel images. The accuracy of the proposed technique is measured using both the segmentation accuracy on simulated ellipse data and the tracking accuracy on validated stem cell tracking results extracted from hundreds of live-cell microscopy image sequences. Pixel replication is shown to be significantly more accurate compared with other approaches. Variance relationships are derived, allowing a more practically effective Gaussian mixture model to extract cell boundaries for data generated from the threshold image using the uniform elliptical distribution and from the distance transform image using the triangular elliptical distribution.
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Details
- Title
- Separating Touching Cells Using Pixel Replicated Elliptical Shape Models
- Creators
- Mark Winter - Drexel UniversityWalter Mankowski - Drexel UniversityEric Wait - Drexel UniversityEdgar Cardenas De La Hoz - Drexel UniversityAngeline Aguinaldo - Drexel UniversityAndrew R Cohen - Drexel University
- Publication Details
- IEEE transactions on medical imaging, v 38(4), pp 883-893
- Publisher
- IEEE
- Grant note
- R01NS076709 / National Institute of Neurological Disorders and Stroke (10.13039/100000065) R01AG041861 / National Institute on Aging (10.13039/100000049)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000463608000002
- Scopus ID
- 2-s2.0-85054495618
- Other Identifier
- 991019168971404721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology
- Radiology, Nuclear Medicine & Medical Imaging