TY - JOUR
T1 - A statistical 3-D segmentation algorithm for classifying brain tissues in multiple sclerosis
AU - Ge, Zhanyu
AU - Venkatesan, Vikram
AU - Mitra, Sunandra
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2001
Y1 - 2001
N2 - Deterministic annealing (DA) algorithm has been successfully used to segment the simulated magnetic resonance normal brain images [1]. This paper presents the results of applying it to simulated and actual clinical multiple sclerosis (MS) magnetic resonance (MR) brain data with the objective of developing a computer-aided diagnostic (CAD) tool for early detection and follow-up for MS lesions. Multiple sclerosis lesions on T1 simulated brain images [2] can be obtained by segmenting the image data using deterministic annealing algorithm and then performing further arithmetic manipulations on these segmented images. Lesions in clinical T2 multiple sclerosis MR images are isolated entities in the segmented images of white matter, gray matter and cerebrospinal fluid. The achieved results demonstrate the ability of deterministic annealing algorithm to isolate MS lesions from clinical MR data, thus providing a potential CAD tool for the clinicians.
AB - Deterministic annealing (DA) algorithm has been successfully used to segment the simulated magnetic resonance normal brain images [1]. This paper presents the results of applying it to simulated and actual clinical multiple sclerosis (MS) magnetic resonance (MR) brain data with the objective of developing a computer-aided diagnostic (CAD) tool for early detection and follow-up for MS lesions. Multiple sclerosis lesions on T1 simulated brain images [2] can be obtained by segmenting the image data using deterministic annealing algorithm and then performing further arithmetic manipulations on these segmented images. Lesions in clinical T2 multiple sclerosis MR images are isolated entities in the segmented images of white matter, gray matter and cerebrospinal fluid. The achieved results demonstrate the ability of deterministic annealing algorithm to isolate MS lesions from clinical MR data, thus providing a potential CAD tool for the clinicians.
UR - http://www.scopus.com/inward/record.url?scp=0034865074&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2001.941761
DO - 10.1109/CBMS.2001.941761
M3 - Article
AN - SCOPUS:0034865074
SP - 455
EP - 460
JO - Proceedings of the IEEE Symposium on Computer-Based Medical Systems
JF - Proceedings of the IEEE Symposium on Computer-Based Medical Systems
SN - 1063-7125
ER -