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
Schematic flow chart of the proposed method. The green arrow points to the result of the respective operation and the red arrow indicates the input/output data flow from one operation to another.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Schematic flow chart of the proposed method. The green arrow points to the result of the respective operation and the red arrow indicates the input/output data flow from one operation to another.

Mentions: MAMLE cell segmentation method comprises 7 steps: i) image denoising, ii) foreground and background segmentation, iii) multi-scale morphological edge detection, iv) threshold decomposition and initial segmentation, v) shape learning form the initial segmentation, vi) likelihood optimization based splitting and vii) maximum likelihood based merging. A flow chart of the algorithm is illustrated in Figure 1. Next, we describe each step in detail:


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

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

Schematic flow chart of the proposed method. The green arrow points to the result of the respective operation and the red arrow indicates the input/output data flow from one operation to another.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Schematic flow chart of the proposed method. The green arrow points to the result of the respective operation and the red arrow indicates the input/output data flow from one operation to another.
Mentions: MAMLE cell segmentation method comprises 7 steps: i) image denoising, ii) foreground and background segmentation, iii) multi-scale morphological edge detection, iv) threshold decomposition and initial segmentation, v) shape learning form the initial segmentation, vi) likelihood optimization based splitting and vii) maximum likelihood based merging. A flow chart of the algorithm is illustrated in Figure 1. Next, we describe each step in detail:

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