Quantification and segmentation of progenitor cells in time-lapse microscopy

Suresh, R. M.; Jayalakshmi, N.

doi:10.1145/1722024.1722071

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Mathematical morphology is a non-linear process that operates the image with a sub image called morphological structuring element [36]. The morphological operations realized in this study are open, close, top hat and bottom hat [37]. These are used to enhance the image and then they find the disk shaped objects with disk size 15.…”

Section: Preprocessingmentioning

confidence: 99%

Cell Lineage Construction of Neural Progenitor Cells

Jayalakshmi¹

2014

IJCA

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This study aims at automatic construction of cell lineage from time-lapse images of progenitor cells. In order to construct the cell lineage it is very useful to have an efficient cell tracking system. In this paper we have described a system for tracking neural progenitor cells in a sequence of images using multiple matching object method based on modified mahalanobis algorithm. This system produces the results including the position, shape, motility and ancestry of each cell in every frame, which helps in construction of cell lineage. The proposed method has been implemented to the sequence of image frames and the computational results of cell tracking are presented.

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Section: Preprocessingmentioning

confidence: 99%

Cell Lineage Construction of Neural Progenitor Cells

Jayalakshmi¹

2014

IJCA

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“…36 The morphological operations realized in this study are open, close, top hat, and bottom hat. 37 These are used to enhance the image and then they find the disk-shaped objects with disk size 15. The disk shape is chosen for structuring element because the cells are curved in nature.…”

Section: Preprocessingmentioning

confidence: 99%

Segmentation and Tracking of Progenitor Cells in Time Lapse Microscopy

Suresh¹,

Jayalakshmi²

2012

Int. J. Image Grap.

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In this paper the neural progenitor cells in a time-lapse microscopic sequence are analyzed to find position, shape, motility, and ancestor of each cell in the frame. Because of the complex nature of cells, the ability to distinguish a cell from the background of an image for automatic quantification remains a challenging task. By using morphological techniques, the blob-like objects are selected. The cells are detected using h-maxima transform and the cell contours are selected using watershed algorithm. The cells between image sequences are tracked using multiple matching object method based on modified Mahalanobis algorithm. The proposed method has been successfully applied to a large number of images and showed promising results for tracking cells between consecutive images.

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Analysis of segmentation and tracking algorithms for time lapse microscopic progenitor cell images

Suresh¹,

Jayalakshmi²

2010

2010 International Conference on Signal and Image Processing

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Quantification and segmentation of progenitor cells in time-lapse microscopy

Cited by 3 publications

References 7 publications

Cell Lineage Construction of Neural Progenitor Cells

Cell Lineage Construction of Neural Progenitor Cells

Segmentation and Tracking of Progenitor Cells in Time Lapse Microscopy

Analysis of segmentation and tracking algorithms for time lapse microscopic progenitor cell images

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