Literature DB >> 24290941

Gallbladder shape extraction from ultrasound images using active contour models.

Marcin Ciecholewski1, Jakub Chochołowicz.   

Abstract

Gallbladder function is routinely assessed using ultrasonographic (USG) examinations. In clinical practice, doctors very often analyse the gallbladder shape when diagnosing selected disorders, e.g. if there are turns or folds of the gallbladder, so extracting its shape from USG images using supporting software can simplify a diagnosis that is often difficult to make. The paper describes two active contour models: the edge-based model and the region-based model making use of a morphological approach, both designed for extracting the gallbladder shape from USG images. The active contour models were applied to USG images without lesions and to those showing specific disease units, namely, anatomical changes like folds and turns of the gallbladder as well as polyps and gallstones. This paper also presents modifications of the edge-based model, such as the method for removing self-crossings and loops or the method of dampening the inflation force which moves nodes if they approach the edge being determined. The user is also able to add a fragment of the approximated edge beyond which neither active contour model will move if this edge is incomplete in the USG image. The modifications of the edge-based model presented here allow more precise results to be obtained when extracting the shape of the gallbladder from USG images than if the morphological model is used.
© 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Edge-based active contour; Gallbladder; Medical image analysis; Region-based active contour; Self-crossings; Shape extraction; Ultrasonography

Mesh:

Year:  2013        PMID: 24290941     DOI: 10.1016/j.compbiomed.2013.10.009

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  1 in total

1.  Automatic gallbladder and gallstone regions segmentation in ultrasound image.

Authors:  Jing Lian; Yide Ma; Yurun Ma; Bin Shi; Jizhao Liu; Zhen Yang; Yanan Guo
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-01-06       Impact factor: 2.924

  1 in total

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