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10.1186/s12938-015-0020-x http://scihub22266oqcxt.onion/10.1186/s12938-015-0020-x C4380112!4380112!25884695     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Eng+Online 2015 ; 14 (ä): ä Nephropedia Template TP {{tp|p=25884695|t=2015. Histological image segmentation using fast mean shift clustering method |pdf=|usr=}}{{25884695}} gab.com Text Histological image segmentation using fast mean shift clustering method JO: Biomed Eng Online http://www.kidney.de/pget.php?&tw=1&pmid=25884695 #FOAvip 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. Twit Text FOAVip Histological image segmentation using fast mean shift clustering method ????Biomed Eng Online http://www.kidney.de/pget.php?&tw=1&pmid=25884695 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25884695 #FOAvip English Wikipedia <ref>{{Cite pmid|25884695}}</ref> Histological image segmentation using fast mean shift clustering method #MMPMID25884695 Wu G; Zhao X; Luo S; Shi H Biomed Eng Online 2015[]; 14 (ä): ä PMID25884695show ga 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. äHistological image segmentation using fast mean shift clustering metho(epmc).pdf DeepDyve Pubget Overpricing