Paper Info
Title
Coupling Oriented Hidden Markov Random Field Model with Local Clustering for Segmenting Blood Vessels and Measuring Spatial Structures in Images of Tumor Microenvironment
Abstract
Interactions between cancer cells and factors within the tumor microenvironment (mE) are essential for understanding tumor development. The spatial relationships between blood vessel cells and cancer cells, e.g. tumor initiating cells (TICs), are an important parameter. Accurate segmentation of blood vessel is necessary for the quantization of their spatial relationships. However, this remains an open problem due to uneven intensity and low signal to noise ratio (SNR). To overcome these challenges, we propose a novel approach that integrates an oriented hidden Markov random field model (Ori-HMRF) with local clustering. The local clustering delineates boundaries of blood vessel segments with low SNR. Then blood vessel segments are viewed as random variables in the Ori-HMRF and their spatial dependence is defined based on directional information. The Ori-HMRF model suppresses noise and generates accurate blood vessel segmentation results. Experimental validations were conducted on both normal mammary and breast cancer tissues.
Year
DOI
Venue
2011
10.1109/BIBM.2011.104
BIBM
Keywords
Field
DocType
noise suppression,hidden markov random field,superpixel,pattern clustering,coupling oriented hidden markov random,random processes,random field model,local clustering,spatial dependence,blood vessels,breast cancer tissue,image segmentation,tumor initiating cells,quantization,quantisation (signal),signal to noise ratio,blood vessel,measuring spatial structures,cancer,hidden markov random field model,blood vessel segmentation,blood vessel segment,coupling oriented hidden markov,blood vessel cell,tumor development,tumor microenvironment,accurate blood vessel segmentation,tumours,spatial relationship,ori-hmrf model suppresses noise,hidden markov models,segmenting blood vessels,ori-hmrf,medical image processing,cancer cell,spatial relationships,random variable,breast cancer
Computer vision,Spatial dependence,Pattern recognition,Hidden Markov random field,Computer science,Segmentation,Signal-to-noise ratio,Image segmentation,Artificial intelligence,Quantization (signal processing),Cluster analysis,Hidden Markov model
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-4577-1799-4
1
PageRank 
References 
Authors
0.37
6
12
Name
Order
Citations
PageRank
Yanqiao Zhu161.79
Fuhai Li224420.68
Derek Cridebring320.80
Jinwen Ma484174.65
Stephen T. C. Wong51081134.56
Tegy J. Vadakkan610.37
Mei Zhang764.56
John Landua810.71
Wei Wei910.37
Mary E. Dickinson1072.62
Jeffrey M. Rosen1110.71
Michael T. Lewis1210.71