Limits...
Multi-environment model estimation for motility analysis of Caenorhabditis elegans.

Sznitman R, Gupta M, Hager GD, Arratia PE, Sznitman J - PLoS ONE (2010)

Bottom Line: We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method.Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined.Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and 'skeletonizing' across a wide range of motility assays.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America.

ABSTRACT
The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode 'skeletons' for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and 'skeletonizing' across a wide range of motility assays.

Show MeSH

Related in: MedlinePlus

Computing the nematode skeleton.Representation of the Chamfer distance transform field () applied to the segmented nematode of Fig. 4. The value associated at each pixel of the image is the Euclidean distance (in pix) to the closest point of the nematode boundary; the distance on the boundary is zero and higher distances lie towards the nematode medial axis. (Inset) Resulting skeleton is achieved by balancing geometric features (i.e. Chamfer distance) and global shape (i.e. nematode curvature).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2908547&req=5

pone-0011631-g005: Computing the nematode skeleton.Representation of the Chamfer distance transform field () applied to the segmented nematode of Fig. 4. The value associated at each pixel of the image is the Euclidean distance (in pix) to the closest point of the nematode boundary; the distance on the boundary is zero and higher distances lie towards the nematode medial axis. (Inset) Resulting skeleton is achieved by balancing geometric features (i.e. Chamfer distance) and global shape (i.e. nematode curvature).

Mentions: Here, is the distance computed when applying the Chamfer distance transform [51], [52] to . This transformation computes the Euclidean distance of each pixel in to its closest nematode boundary pixel. An example of this distance transform is shown in the contour plot of Fig. 5. Here, the boundary of the nematode has a distance of zero, while values of increase steadily for pixels approaching the medial axis of the nematode. Equation (4) then implies that skeleton locations are picked by (i) weighing how likely pixels are to be at the center of the segmented nematode, combined with (ii) the history of the chosen vector directions. This strategy is particularly useful in cases where the segmentation is noisy, as the history of vector directions guides where the following pixel location should be located. In order to remove the possibility of selecting the same pixel several times, is removed from possible future locations by setting .


Multi-environment model estimation for motility analysis of Caenorhabditis elegans.

Sznitman R, Gupta M, Hager GD, Arratia PE, Sznitman J - PLoS ONE (2010)

Computing the nematode skeleton.Representation of the Chamfer distance transform field () applied to the segmented nematode of Fig. 4. The value associated at each pixel of the image is the Euclidean distance (in pix) to the closest point of the nematode boundary; the distance on the boundary is zero and higher distances lie towards the nematode medial axis. (Inset) Resulting skeleton is achieved by balancing geometric features (i.e. Chamfer distance) and global shape (i.e. nematode curvature).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2908547&req=5

pone-0011631-g005: Computing the nematode skeleton.Representation of the Chamfer distance transform field () applied to the segmented nematode of Fig. 4. The value associated at each pixel of the image is the Euclidean distance (in pix) to the closest point of the nematode boundary; the distance on the boundary is zero and higher distances lie towards the nematode medial axis. (Inset) Resulting skeleton is achieved by balancing geometric features (i.e. Chamfer distance) and global shape (i.e. nematode curvature).
Mentions: Here, is the distance computed when applying the Chamfer distance transform [51], [52] to . This transformation computes the Euclidean distance of each pixel in to its closest nematode boundary pixel. An example of this distance transform is shown in the contour plot of Fig. 5. Here, the boundary of the nematode has a distance of zero, while values of increase steadily for pixels approaching the medial axis of the nematode. Equation (4) then implies that skeleton locations are picked by (i) weighing how likely pixels are to be at the center of the segmented nematode, combined with (ii) the history of the chosen vector directions. This strategy is particularly useful in cases where the segmentation is noisy, as the history of vector directions guides where the following pixel location should be located. In order to remove the possibility of selecting the same pixel several times, is removed from possible future locations by setting .

Bottom Line: We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method.Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined.Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and 'skeletonizing' across a wide range of motility assays.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America.

ABSTRACT
The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode 'skeletons' for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and 'skeletonizing' across a wide range of motility assays.

Show MeSH
Related in: MedlinePlus