Automatic Segmentation Approach Based Data Aggregation for the Classification of Brain Tissues
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@Article{JICS-8-209,
author = {Lamiche Chaabane and Moussaoui Abdelouahab},
title = {Automatic Segmentation Approach Based Data Aggregation for the Classification of Brain Tissues},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {8},
number = {3},
pages = {209--216},
abstract = { The paper presents a study and an evaluation of a novel unsupervised segmentation technique
based aggregation approach and some of possibility theory concepts. Firstly, the MPFCM (Modified
Possibilistic Fuzzy C-Means) algorithm is used to extract information from each of MR images modalities. In
second step, an obtained data are combined with an operator in order to exploiting the uncertainty and
ambiguity in the images. Finally, the segmented image is constructed using a decision rule. The efficiency of
the proposed method is demonstrated by segmentation experiments using simulated MR images with
different noise levels.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22612.html}
}
TY - JOUR
T1 - Automatic Segmentation Approach Based Data Aggregation for the Classification of Brain Tissues
AU - Lamiche Chaabane and Moussaoui Abdelouahab
JO - Journal of Information and Computing Science
VL - 3
SP - 209
EP - 216
PY - 2024
DA - 2024/01
SN - 8
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22612.html
KW - aggregation, possibility theory, segmentation, MPFCM, MR images.
AB - The paper presents a study and an evaluation of a novel unsupervised segmentation technique
based aggregation approach and some of possibility theory concepts. Firstly, the MPFCM (Modified
Possibilistic Fuzzy C-Means) algorithm is used to extract information from each of MR images modalities. In
second step, an obtained data are combined with an operator in order to exploiting the uncertainty and
ambiguity in the images. Finally, the segmented image is constructed using a decision rule. The efficiency of
the proposed method is demonstrated by segmentation experiments using simulated MR images with
different noise levels.
Lamiche Chaabane and Moussaoui Abdelouahab. (2024). Automatic Segmentation Approach Based Data Aggregation for the Classification of Brain Tissues.
Journal of Information and Computing Science. 8 (3).
209-216.
doi:
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