Paper Info
Title
Increasing lesion specificity with fusion of manually and automatically segmented liver MR images.
Abstract
In this study, it is aimed to analyze the magnetic resonance (MR) images used in the diagnosis of liver focal lesions using image fusion methods and to help diagnosis by adding automatic segmentation results to the manual segmentation process preferred by experts. For this aim fusions of liver MR images, segmented by a fuzzy method and segmented manually. 120 T1-weighted dynamic contrast-enhanced liver MR images of pre-contrast phase, arterial phase, portal vein phase and late venous phase, taken from 30 different patients, were used. Each phase image is also fused with images segmented by the fuzzy c-means algorithm in the same phase, so that the lesion surfaces and contours are displayed on the segmented image manually. Thus, the significance of the lesion was increased before the information in the MR image in which the liver function information was displayed was lost. The resulting new image contains more useful information for automatic decision systems. The results obtained were evaluated using structural similarity index, peak signal-to-noise ratio and fusion factor quality metrics.
Year
Venue
Keywords
2018
Signal Processing and Communications Applications Conference
discrete wavelet transform,fuzzy c-means,segmentation,image fusion
Field
DocType
ISSN
Computer vision,Pattern recognition,Lesion,Image fusion,Computer science,Segmentation,Decision system,Fusion,Fuzzy method,Image segmentation,Artificial intelligence,Magnetic resonance imaging
Conference
2165-0608
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Saim Ervural100.68
Murat Ceylan2808.37