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Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces.

Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial diffe...

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Published in:Journal of Digital Imaging Vol. 31; no. 4; pp. 490 - 505
Main Authors: Siriapisith, Thanongchai, Kusakunniran, Worapan, Haddawy, Peter
Format: algorithm diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Aug2018
Online Access:View this record in EBSCOhost
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      dt: Aug2018
      vid: 31
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-018-0049-z
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        atl: Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces.
      aug:
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          Siriapisith, Thanongchai
          Kusakunniran, Worapan
          Haddawy, Peter
        affil: Department Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
      sug:
        subj:
          Aortic Aneurysm, Abdominal Diagnosis
          Signal Processing, Computer Assisted Methods
          Automation
          Diagnostic Imaging
      ab: Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.
      pubtype: Academic Journal
      doctype:
        algorithm
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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