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J. Imaging 2016, 2(4), 31; doi:10.3390/jimaging2040031

3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed

1
Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
2
Department of Biology, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
3
Department of Genetics and Genome Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
Current address: Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA.
*
Author to whom correspondence should be addressed.
Academic Editors: Philip Morrow, Kenji Suzuki and Gonzalo Pajares Martinsanz
Received: 7 September 2016 / Revised: 23 October 2016 / Accepted: 27 October 2016 / Published: 5 November 2016
(This article belongs to the Special Issue Image and Video Processing in Medicine)
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Abstract

Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method. View Full-Text
Keywords: watershed transform; watershed; manifold; Weingarten map; shape operator; Gaussian curvature; mean curvature; catchment basin; topographic distance watershed transform; watershed; manifold; Weingarten map; shape operator; Gaussian curvature; mean curvature; catchment basin; topographic distance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Atta-Fosu, T.; Guo, W.; Jeter, D.; Mizutani, C.M.; Stopczynski, N.; Sousa-Neves, R. 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed. J. Imaging 2016, 2, 31.

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