Renal Cortex Segmentation Using Optimal Surface Search with Novel Graph Construction View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2011

AUTHORS

Xiuli Li , Xinjian Chen , Jianhua Yao , Xing Zhang , Jie Tian

ABSTRACT

In this paper, we propose a novel approach to solve the renal cortex segmentation problem, which has rarely been studied. In this study, the renal cortex segmentation problem is handled as a multiple-surfaces extraction problem, which is solved using the optimal surface search method. We propose a novel graph construction scheme in the optimal surface search to better accommodate multiple surfaces. Different surface sub-graphs are constructed according to their properties, and inter-surface relationships are also modeled in the graph. The proposed method was tested on 17 clinical CT datasets. The true positive volume fraction (TPVF) and false positive volume fraction (FPVF) are 74.10% and 0.08%, respectively. The experimental results demonstrate the effectiveness of the proposed method. More... »

PAGES

387-394

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-23626-6_48

DOI

http://dx.doi.org/10.1007/978-3-642-23626-6_48

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029215201

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22003723


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