Limits...
Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE).

Chowdhury S, Kandhavelu M, Yli-Harja O, Ribeiro AS - BMC Bioinformatics (2013)

Bottom Line: From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function.The new method attained very high (above 90%) cell segmentation accuracy in all cases.Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters.

Methods: MAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained.

Conclusions: We performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.

Show MeSH

Related in: MedlinePlus

Parametric robustness. Top: Number of detected cells (color encoded, encoding scheme is shown on right side color bar) for different combinations of edge detection resolution level (X-axis) and number of maximum threshold decomposition level (Y-axis). Bottom: Number of cells detected for different values of the free parameter 'length of the cell'.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3750476&req=5

Figure 6: Parametric robustness. Top: Number of detected cells (color encoded, encoding scheme is shown on right side color bar) for different combinations of edge detection resolution level (X-axis) and number of maximum threshold decomposition level (Y-axis). Bottom: Number of cells detected for different values of the free parameter 'length of the cell'.

Mentions: The parametric robustness of MAMLE is studied in 10 sample images containing approximately 8000 cells. It considers cell count as an objective metric for evaluation. The result is shown in Figure 6. Figure 6 (top) illustrates the effect of varying the scale level and the decomposition level. The cell count is stable across a wide range of the respective parameters. The results in the Figure 6 (bottom) are obtained by varying the maximum cell size. They indicate that this parameter affects the cell count in a linear fashion. Note that the coefficient of variation of cell count was much lower (0.057) than the coefficient of variation of the input parameter 'maximum cell size' (0.32). Thus, it can be stated that the algorithm obtains robust cell detection results within a wide range of numerical settings of the free parameters. The parameter, 'threshold window size' depends on the spatial distribution of cell background and foreground illumination levels. The largest possible window with homogeneous illumination level is the optimum for this parameter.


Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE).

Chowdhury S, Kandhavelu M, Yli-Harja O, Ribeiro AS - BMC Bioinformatics (2013)

Parametric robustness. Top: Number of detected cells (color encoded, encoding scheme is shown on right side color bar) for different combinations of edge detection resolution level (X-axis) and number of maximum threshold decomposition level (Y-axis). Bottom: Number of cells detected for different values of the free parameter 'length of the cell'.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750476&req=5

Figure 6: Parametric robustness. Top: Number of detected cells (color encoded, encoding scheme is shown on right side color bar) for different combinations of edge detection resolution level (X-axis) and number of maximum threshold decomposition level (Y-axis). Bottom: Number of cells detected for different values of the free parameter 'length of the cell'.
Mentions: The parametric robustness of MAMLE is studied in 10 sample images containing approximately 8000 cells. It considers cell count as an objective metric for evaluation. The result is shown in Figure 6. Figure 6 (top) illustrates the effect of varying the scale level and the decomposition level. The cell count is stable across a wide range of the respective parameters. The results in the Figure 6 (bottom) are obtained by varying the maximum cell size. They indicate that this parameter affects the cell count in a linear fashion. Note that the coefficient of variation of cell count was much lower (0.057) than the coefficient of variation of the input parameter 'maximum cell size' (0.32). Thus, it can be stated that the algorithm obtains robust cell detection results within a wide range of numerical settings of the free parameters. The parameter, 'threshold window size' depends on the spatial distribution of cell background and foreground illumination levels. The largest possible window with homogeneous illumination level is the optimum for this parameter.

Bottom Line: From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function.The new method attained very high (above 90%) cell segmentation accuracy in all cases.Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters.

Methods: MAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained.

Conclusions: We performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.

Show MeSH
Related in: MedlinePlus