NDLI: 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed

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3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed

Content Provider	MDPI
Author	Atta-Fosu, Thomas Guo, Weihong Jeter, Dana Mizutani, Claudia Stopczynski, Nathan Sousa-Neves, Rui
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.
File Size	3579904
File Format	PDF
e-ISSN	2313433X
DOI	10.3390/jimaging2040031
Journal	Journal of Imaging
Issue Number	4
Volume Number	2
Language	English
Publisher Date	2016-11-05
Access Restriction	Open
Subject Keyword	watershed transform watershed manifold Weingarten map shape operator Gaussian curvature mean curvature catchment basin topographic distance
Content Type	Text
Resource Type	Article

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