Abdominal wall extraction using constrained deformable model and abdominal context

Abdominal wall extraction using constrained deformable model and abdominal context
Title:
Abdominal wall extraction using constrained deformable model and abdominal context
Other Titles:
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords:
Publication Date:
26 August 2014
Citation:
W. Huang et al., "Abdominal wall extraction using constrained deformable model and abdominal context," 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, 2014, pp. 4699-4702. doi: 10.1109/EMBC.2014.6944673
Abstract:
Information about abdominal wall can be used for many applications from organ segmentation, registration, and surgical simulation. The challenges exist in abdominal wall extraction due to its varieties in shapes, connection to the internal organs and anterior layer edge formed between the muscle and fascia/fatty layer, which may distract the shape model. In this paper we present an approach to the posterior abdominal wall extraction using the shape model and other abdominal context, particularly with the rib-spine bone information and the wall image features. The shape model is constructed based on the training abdominal walls that are delineated manually. After bone information being extracted, the wall shape deforms from the prior shape model using the snake, which is constrained by the bone context and guided by the processed image energy map with the aim of removing distracted image features of anterior abdominal wall and the outer region from the original map. Meanwhile, an overall convex shape is maintained by limiting the angles of the contour points. The proposed approach is tested on abdominal CT data which provides encouraging results.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1557-170X
ISBN:
978-1-4244-7929-0
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