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An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering

Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation...

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Autores principales: Dalal, Surjeet, Lilhore, Umesh Kumar, Manoharan, Poongodi, Rani, Uma, Dahan, Fadl, Hajjej, Fahima, Keshta, Ismail, Sharma, Ashish, Simaiya, Sarita, Raahemifar, Kaamran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537273/
https://www.ncbi.nlm.nih.gov/pubmed/37765873
http://dx.doi.org/10.3390/s23187816
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author Dalal, Surjeet
Lilhore, Umesh Kumar
Manoharan, Poongodi
Rani, Uma
Dahan, Fadl
Hajjej, Fahima
Keshta, Ismail
Sharma, Ashish
Simaiya, Sarita
Raahemifar, Kaamran
author_facet Dalal, Surjeet
Lilhore, Umesh Kumar
Manoharan, Poongodi
Rani, Uma
Dahan, Fadl
Hajjej, Fahima
Keshta, Ismail
Sharma, Ashish
Simaiya, Sarita
Raahemifar, Kaamran
author_sort Dalal, Surjeet
collection PubMed
description Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.
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spelling pubmed-105372732023-09-29 An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering Dalal, Surjeet Lilhore, Umesh Kumar Manoharan, Poongodi Rani, Uma Dahan, Fadl Hajjej, Fahima Keshta, Ismail Sharma, Ashish Simaiya, Sarita Raahemifar, Kaamran Sensors (Basel) Article Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods. MDPI 2023-09-12 /pmc/articles/PMC10537273/ /pubmed/37765873 http://dx.doi.org/10.3390/s23187816 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dalal, Surjeet
Lilhore, Umesh Kumar
Manoharan, Poongodi
Rani, Uma
Dahan, Fadl
Hajjej, Fahima
Keshta, Ismail
Sharma, Ashish
Simaiya, Sarita
Raahemifar, Kaamran
An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
title An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
title_full An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
title_fullStr An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
title_full_unstemmed An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
title_short An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
title_sort efficient brain tumor segmentation method based on adaptive moving self-organizing map and fuzzy k-mean clustering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537273/
https://www.ncbi.nlm.nih.gov/pubmed/37765873
http://dx.doi.org/10.3390/s23187816
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