Paper Info
Title
3d Brain Magnetic Resonance Imaging Segmentation By Using Bitplane And Adaptive Fast Marching
Abstract
Diagnosis using medical images helps doctors detect diseases and treat patients effectively. A system that segments objects automatically from magnetic resonance imaging (MRI) plays an important role when doctors diagnose injuries and brain diseases. This article presents a method for automatic brain, scalp, and skull segmentation from MRI that uses Bitplane and the Adaptive Fast Marching method (FMM). We focus on the segmentation of these tissues, especially the brain, because they are the essential objects, and their segmentation is the first step in the segmentation of other tissues. First, the type of each slice is set based on the shape of the brain, and the head region is segmented by removing its background. Second, the sure region and the unsure region are segmented based on the Bitplane method. Finally, this work proposes an approach for classification that is based on the Adaptive FMM. This approach is evaluated with the BrainWeb and Neurodevelopmental MRI databases and compared with other methods. The Dice Averages for brain, scalp, and skull segmentation are 96%, 80%, and 93%, respectively, on the BrainWeb database and 91%, 67%, and 80%, respectively, on the Neurodevelopmental MRI database.
Year
DOI
Venue
2018
10.1002/ima.22273
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Keywords
Field
DocType
adaptive fast marching method, bit plane, brain, scalp and skull segmentation
Computer vision,Computer science,Fast marching method,Segmentation,Artificial intelligence,Magnetic resonance imaging
Journal
Volume
Issue
ISSN
28
3
0899-9457
Citations 
PageRank 
References 
0
0.34
1
Authors
3
Name
Order
Citations
PageRank
Tran Anh Tuan1213.58
Jin Young Kim249781.76
Pham The Bao3227.70