article

Improved graph-cut segmentation for ultrasound liver cyst image

Authors:

Zhanhong Zhuang,

Wenhua XuAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 21

Pages 28905 - 28923

https://doi.org/10.1007/s11042-018-6076-4

Published: 01 November 2018 Publication History

Abstract

An optimal contour segmentation for ultrasonic liver cyst image is presented through combining graph-based method with particle swarm optimization (PSO) in this paper. After automatic selecting the region of interest (ROI) for ultrasonic liver cyst image, our method developed firstly a kind of multiple classes merging scheme by jointing the graph-based segmented result with the intensity of original ultrasound image. Then the evaluation function in the PSO was modified to optimize the parameter. Finally, the liver cysts were segmented according to the optimized parameter. In the experiment, we tested the influence of weight value on the improved method. And five indicators, which included Hausdorff distance (HD), mean absolute distance (MD), true positive volume fraction (TPVF), false-negative volume fraction (FNVF) and false-positive volume fraction (FPVF), were estimated to verify the improved method. Experimental results have validated that the improved method may extract successfully and accurately the contour of liver cyst.

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Cited By

Rafique AGochoo MJalal AKim K(2023)Maximum entropy scaled super pixels segmentation for multi-object detection and scene recognition via deep belief networkMultimedia Tools and Applications10.1007/s11042-022-13717-y82:9(13401-13430)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13717-y

Improved graph-cut segmentation for ultrasound liver cyst image
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 21

November 2018

1452 pages

ISSN:1380-7501

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2018

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Cited By

Rafique AGochoo MJalal AKim K(2023)Maximum entropy scaled super pixels segmentation for multi-object detection and scene recognition via deep belief networkMultimedia Tools and Applications10.1007/s11042-022-13717-y82:9(13401-13430)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13717-y

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