Volume 8, Issue 2
Multi-threshhold Ultrasound Image Segmentation Based on Potential Function Clustering

Bo Peng, Fuliang Zhang & Xianfeng Yang

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 277-284.

Published online: 2015-08

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  • Abstract

Ultrasound image segmentation is an important task for clinical diagnosis. In this study, a multi- threshhold segmentation approach was proposed to enhance ultrasound image segmentation accuracy. More specifically, the proposed multi-threshhold segmentation approach, combining an opening-closing morphological filter and potential function clustering theory, attempted to provide better ultrasound image segmentation visibility. This proposed approach was tested using computer-simulated images and in vivo images. Computer simulation results demonstrated that the method significantly improved the accuracy of image segmentation. From in vivo images investigation, we have found that, as compared with the original images, better segmentation visibility were obtained. Our initial results demonstrated that this method could be useful for improving the segmentation quality of ultrasound images as a post-processing tool.

  • Keywords

Ultrasound Image Segmentation Morphological Reconstruction Filter Histogram Potential Function Multi-threshhold Segmentation

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-277, author = {}, title = {Multi-threshhold Ultrasound Image Segmentation Based on Potential Function Clustering}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {2}, pages = {277--284}, abstract = {Ultrasound image segmentation is an important task for clinical diagnosis. In this study, a multi- threshhold segmentation approach was proposed to enhance ultrasound image segmentation accuracy. More specifically, the proposed multi-threshhold segmentation approach, combining an opening-closing morphological filter and potential function clustering theory, attempted to provide better ultrasound image segmentation visibility. This proposed approach was tested using computer-simulated images and in vivo images. Computer simulation results demonstrated that the method significantly improved the accuracy of image segmentation. From in vivo images investigation, we have found that, as compared with the original images, better segmentation visibility were obtained. Our initial results demonstrated that this method could be useful for improving the segmentation quality of ultrasound images as a post-processing tool.}, issn = {2617-8699}, doi = {https://doi.org/doi:10.3993/jfbim00101}, url = {http://global-sci.org/intro/article_detail/jfbi/4707.html} }
TY - JOUR T1 - Multi-threshhold Ultrasound Image Segmentation Based on Potential Function Clustering JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 277 EP - 284 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/doi:10.3993/jfbim00101 UR - https://global-sci.org/intro/article_detail/jfbi/4707.html KW - Ultrasound Image Segmentation KW - Morphological Reconstruction Filter KW - Histogram Potential Function KW - Multi-threshhold Segmentation AB - Ultrasound image segmentation is an important task for clinical diagnosis. In this study, a multi- threshhold segmentation approach was proposed to enhance ultrasound image segmentation accuracy. More specifically, the proposed multi-threshhold segmentation approach, combining an opening-closing morphological filter and potential function clustering theory, attempted to provide better ultrasound image segmentation visibility. This proposed approach was tested using computer-simulated images and in vivo images. Computer simulation results demonstrated that the method significantly improved the accuracy of image segmentation. From in vivo images investigation, we have found that, as compared with the original images, better segmentation visibility were obtained. Our initial results demonstrated that this method could be useful for improving the segmentation quality of ultrasound images as a post-processing tool.
Bo Peng, Fuliang Zhang & Xianfeng Yang. (2019). Multi-threshhold Ultrasound Image Segmentation Based on Potential Function Clustering. Journal of Fiber Bioengineering and Informatics. 8 (2). 277-284. doi:doi:10.3993/jfbim00101
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