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Nonnegative mixed-norm convex optimization for mitotic cell detection in phase contrast microscopy.

Liu A, Hao T, Gao Z, Su Y, Yang Z - Comput Math Methods Med (2013)

Bottom Line: First, we apply an imaging model-based microscopy image segmentation method that exploits phase contrast optics to extract mitotic candidates in the input images.Then, a convex objective function regularized by mix-norm with nonnegative constraint is proposed to induce sparsity and consistence for discriminative representation of deformable objects in a sparse representation scheme.At last, a Support Vector Machine classifier is utilized for mitotic cell modeling and detection.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China.

ABSTRACT
This paper proposes a nonnegative mix-norm convex optimization method for mitotic cell detection. First, we apply an imaging model-based microscopy image segmentation method that exploits phase contrast optics to extract mitotic candidates in the input images. Then, a convex objective function regularized by mix-norm with nonnegative constraint is proposed to induce sparsity and consistence for discriminative representation of deformable objects in a sparse representation scheme. At last, a Support Vector Machine classifier is utilized for mitotic cell modeling and detection. This method can overcome the difficulty in feature formulation for deformable objects and is independent of tracking or temporal inference model. The comparison experiments demonstrate that the proposed method can produce competing results with the state-of-the-art methods.

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Performance comparison for dictionary learning with respect to four kinds of visual features and different configurations of  γ1  and  γ2.
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fig2: Performance comparison for dictionary learning with respect to four kinds of visual features and different configurations of  γ1  and  γ2.

Mentions: The performances of different dictionary learning strategies with respect to four visual features and different configurations of γ1 and γ2 are shown in Figure 2. With the comparison in each row, we can achieve the best F1 score and accuracy when both γ1 and γ2 were 0.1 and the visual feature was fixed. It is implied that the stronger sparsity and consistence effects can benefit the decomposed coefficients for model learning. In our experiment, the maximum standard deviation (MSD) of F1 score by fixing γ1/γ2 and tuning γ2/γ1 is 0.044 (except the special case of 0.086 when using GIST and γ1 = 0.1 shown in Figure 2(c)) and the MSD of accuracy is 0.019. These results show that the proposed method has strong robustness with respect to different visual features and a broad range of parameters.


Nonnegative mixed-norm convex optimization for mitotic cell detection in phase contrast microscopy.

Liu A, Hao T, Gao Z, Su Y, Yang Z - Comput Math Methods Med (2013)

Performance comparison for dictionary learning with respect to four kinds of visual features and different configurations of  γ1  and  γ2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852582&req=5

fig2: Performance comparison for dictionary learning with respect to four kinds of visual features and different configurations of  γ1  and  γ2.
Mentions: The performances of different dictionary learning strategies with respect to four visual features and different configurations of γ1 and γ2 are shown in Figure 2. With the comparison in each row, we can achieve the best F1 score and accuracy when both γ1 and γ2 were 0.1 and the visual feature was fixed. It is implied that the stronger sparsity and consistence effects can benefit the decomposed coefficients for model learning. In our experiment, the maximum standard deviation (MSD) of F1 score by fixing γ1/γ2 and tuning γ2/γ1 is 0.044 (except the special case of 0.086 when using GIST and γ1 = 0.1 shown in Figure 2(c)) and the MSD of accuracy is 0.019. These results show that the proposed method has strong robustness with respect to different visual features and a broad range of parameters.

Bottom Line: First, we apply an imaging model-based microscopy image segmentation method that exploits phase contrast optics to extract mitotic candidates in the input images.Then, a convex objective function regularized by mix-norm with nonnegative constraint is proposed to induce sparsity and consistence for discriminative representation of deformable objects in a sparse representation scheme.At last, a Support Vector Machine classifier is utilized for mitotic cell modeling and detection.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China.

ABSTRACT
This paper proposes a nonnegative mix-norm convex optimization method for mitotic cell detection. First, we apply an imaging model-based microscopy image segmentation method that exploits phase contrast optics to extract mitotic candidates in the input images. Then, a convex objective function regularized by mix-norm with nonnegative constraint is proposed to induce sparsity and consistence for discriminative representation of deformable objects in a sparse representation scheme. At last, a Support Vector Machine classifier is utilized for mitotic cell modeling and detection. This method can overcome the difficulty in feature formulation for deformable objects and is independent of tracking or temporal inference model. The comparison experiments demonstrate that the proposed method can produce competing results with the state-of-the-art methods.

Show MeSH