Limits...
Histological image segmentation using fast mean shift clustering method.

Wu G, Zhao X, Luo S, Shi H - Biomed Eng Online (2015)

Bottom Line: And an integral scheme is employed to reduce the computation cost of mean shift vector significantly.Experimental results demonstrate that Mean Shift clustering achieves more accurate results than k-means but is computational expensive, and the speed of the improved Mean Shift method is comparable to that of k-means while the accuracy of segmentation results is the same as that achieved using standard Mean Shift method.It employs improved Mean Shift clustering, which is speed up by using probability density distribution estimation and the integral scheme.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Engineering, Capital Medical University, Beijing, China. gemingwu@ccmu.edu.cn.

ABSTRACT

Background: Colour image segmentation is fundamental and critical for quantitative histological image analysis. The complexity of the microstructure and the approach to make histological images results in variable staining and illumination variations. And ultra-high resolution of histological images makes it is hard for image segmentation methods to achieve high-quality segmentation results and low computation cost at the same time.

Methods: Mean Shift clustering approach is employed for histological image segmentation. Colour histological image is transformed from RGB to CIE L*a*b* colour space, and then a* and b* components are extracted as features. To speed up Mean Shift algorithm, the probability density distribution is estimated in feature space in advance and then the Mean Shift scheme is used to separate the feature space into different regions by finding the density peaks quickly. And an integral scheme is employed to reduce the computation cost of mean shift vector significantly. Finally image pixels are classified into clusters according to which region their features fall into in feature space.

Results: Numerical experiments are carried on liver fibrosis histological images. Experimental results demonstrate that Mean Shift clustering achieves more accurate results than k-means but is computational expensive, and the speed of the improved Mean Shift method is comparable to that of k-means while the accuracy of segmentation results is the same as that achieved using standard Mean Shift method.

Conclusions: An effective and reliable histological image segmentation approach is proposed in this paper. It employs improved Mean Shift clustering, which is speed up by using probability density distribution estimation and the integral scheme.

No MeSH data available.


Related in: MedlinePlus

The relationship ofShandRhin two dimension case. Black points represent observations. The region R is split into tiny squares which are used to represent the observations located in them.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4380112&req=5

Fig1: The relationship ofShandRhin two dimension case. Black points represent observations. The region R is split into tiny squares which are used to represent the observations located in them.

Mentions: Notice that all points are located in a square R in Vab. Split R into tiny squares {r1,r2,…,rm whose sides have the length of 2e. For each tiny square ri, denote the frequency of points in it by wi and the center by ci. Denote the closest external square of Sh by Rh which consists of tiny squares (Figure 1 illustrates the relationship of Sh and Rh in Vab). We replace Rh with Rh and approximate mh at x withFigure 1


Histological image segmentation using fast mean shift clustering method.

Wu G, Zhao X, Luo S, Shi H - Biomed Eng Online (2015)

The relationship ofShandRhin two dimension case. Black points represent observations. The region R is split into tiny squares which are used to represent the observations located in them.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4380112&req=5

Fig1: The relationship ofShandRhin two dimension case. Black points represent observations. The region R is split into tiny squares which are used to represent the observations located in them.
Mentions: Notice that all points are located in a square R in Vab. Split R into tiny squares {r1,r2,…,rm whose sides have the length of 2e. For each tiny square ri, denote the frequency of points in it by wi and the center by ci. Denote the closest external square of Sh by Rh which consists of tiny squares (Figure 1 illustrates the relationship of Sh and Rh in Vab). We replace Rh with Rh and approximate mh at x withFigure 1

Bottom Line: And an integral scheme is employed to reduce the computation cost of mean shift vector significantly.Experimental results demonstrate that Mean Shift clustering achieves more accurate results than k-means but is computational expensive, and the speed of the improved Mean Shift method is comparable to that of k-means while the accuracy of segmentation results is the same as that achieved using standard Mean Shift method.It employs improved Mean Shift clustering, which is speed up by using probability density distribution estimation and the integral scheme.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Engineering, Capital Medical University, Beijing, China. gemingwu@ccmu.edu.cn.

ABSTRACT

Background: Colour image segmentation is fundamental and critical for quantitative histological image analysis. The complexity of the microstructure and the approach to make histological images results in variable staining and illumination variations. And ultra-high resolution of histological images makes it is hard for image segmentation methods to achieve high-quality segmentation results and low computation cost at the same time.

Methods: Mean Shift clustering approach is employed for histological image segmentation. Colour histological image is transformed from RGB to CIE L*a*b* colour space, and then a* and b* components are extracted as features. To speed up Mean Shift algorithm, the probability density distribution is estimated in feature space in advance and then the Mean Shift scheme is used to separate the feature space into different regions by finding the density peaks quickly. And an integral scheme is employed to reduce the computation cost of mean shift vector significantly. Finally image pixels are classified into clusters according to which region their features fall into in feature space.

Results: Numerical experiments are carried on liver fibrosis histological images. Experimental results demonstrate that Mean Shift clustering achieves more accurate results than k-means but is computational expensive, and the speed of the improved Mean Shift method is comparable to that of k-means while the accuracy of segmentation results is the same as that achieved using standard Mean Shift method.

Conclusions: An effective and reliable histological image segmentation approach is proposed in this paper. It employs improved Mean Shift clustering, which is speed up by using probability density distribution estimation and the integral scheme.

No MeSH data available.


Related in: MedlinePlus