NDLI: Brain Image Segmentation Based on Hypergraph Modeling

Content Provider	IEEE Xplore Digital Library
Author	Jicheng Hu Xiaofeng Wei Honglin He
Copyright Year	2014
Description	Author affiliation: State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China (Jicheng Hu; Xiaofeng Wei) \|\| Wuhan Gen. Hosp. of Guangzhou Mil. Area Command, Wuhan, China (Honglin He)
Abstract	In this paper a new framework for medical image segmentation is presented based on the hypergraph decomposition theory. Each frame of the clinical image atlas is first over-segmented into a series of patches which assigned some clustering attribute values. The patches that satisfy some conditions are chosen to be hypergraph vertices, and those clusters of vertices share some attributes form hyperedges of the hypergraph. The task of extracting objects from the scanned brain images atlas is thus converted to be a hypergraph partition problem. The distributed multilevel partition algorithm is then employed to split the hypergraph into clusters, each of the clusters is assigned a modularity attribute to indicate the compactness of the cluster. Experiment shows that these modularity attributes are generally of large values for those clusters formed by organs such as tumor, which demonstrates the effectiveness of our proposed scheme and algorithm.
Sponsorship	IEEE Comput. Soc.
Starting Page	327
Ending Page	332
File Size	564576
Page Count	6
File Format	PDF
e-ISBN	9781479950799
DOI	10.1109/DASC.2014.65
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	multilevel-partition Image segmentation modularity Image edge detection image segmentation medical image Brain modeling hyper-graph Partitioning algorithms Tumors Biomedical imaging
Content Type	Text
Resource Type	Article

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