Paper Info
Title
Image Segmentation Using Active Contours Driven by Bias Fitted Image Robust to Intensity Inhomogeneity.
Abstract
In this paper, a novel region-based active contour method is proposed based to both correct and segment the intensity inhomogeneous images. A phase stretch transform (PST) kernel is used to compute new intensity means and bias field, which are employed to define a bias fitted image. In the proposed energy function, a new signed pressure force (SPF) function is formulated with a bias image fitted difference, which helps to segment the intensity inhomogeneous objects. A Gaussian kernel is also used to regularize the level set curve, which also removes the computationally expensive re-initialization. Finally, the proposed method is compared with the state-of-the-art both qualitatively and quantitatively using the synthetic and real brain magnetic resonance (MR) images, which shows it yields the best segmentation and correction results.
Year
DOI
Venue
2017
10.3233/978-1-61499-806-8-146
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
Image segmentation,level set,phase stretch transform,region-based method,bias correction
Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Image segmentation,Artificial intelligence
Conference
Volume
ISSN
Citations 
300
0922-6389
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Farhan Akram193.63
Miguel Ángel Garcia222024.41
Vivek Kumar Singh327039.83
Nasibeh Saffari400.34
Mostafa Kamal Sarker500.34
Domenec Puig633254.33