Paper Info
Title
Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images.
Abstract
Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and fuzzy object models. The proposed method evaluates object affinity and homogeneity using the MR signal, and employs a fuzzy object model, which is built from training datasets. We have evaluated the proposed method based on 10 newborn MR images with subject revised age between -1 month and 2 months. These studies indicate that the use of a fuzzy object model is effective in improving the segmentation accuracy.
Year
DOI
Venue
2012
10.1109/ICSMC.2012.6377934
SMC
Keywords
Field
DocType
biomedical MRI,brain,diseases,fuzzy set theory,image segmentation,medical image processing,MR signal,cerebral parenchyma segmentation,computer-aided diagnosis systems,fuzzy object model based fuzzy connectedness image segmentation,newborn brain MR images,newborn cerebral diseases,newborn magnetic resonance images,object affinity,object homogeneity,scale-based fuzzy connected image segmentation,fuzzy connectedness,fuzzy object model,intensity scale,magnetic resonance image,newborrn brain
Computer vision,Scale-space segmentation,Segmentation,Computer science,Fuzzy logic,Segmentation-based object categorization,Object model,Fuzzy set,Image segmentation,Fuzzy connectedness,Artificial intelligence,Machine learning
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Syoji Kobashi122165.08
Jayaram K. Udupa22481322.29