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Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI
Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and reg...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293518/ https://www.ncbi.nlm.nih.gov/pubmed/35919500 http://dx.doi.org/10.1155/2022/1541980 |
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author | Ejaz, Khurram Mohd Rahim, Mohd Shafry Arif, Muhammad Izdrui, Diana Craciun, Daniela Maria Geman, Oana |
author_facet | Ejaz, Khurram Mohd Rahim, Mohd Shafry Arif, Muhammad Izdrui, Diana Craciun, Daniela Maria Geman, Oana |
author_sort | Ejaz, Khurram |
collection | PubMed |
description | Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries. |
format | Online Article Text |
id | pubmed-9293518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92935182022-08-01 Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI Ejaz, Khurram Mohd Rahim, Mohd Shafry Arif, Muhammad Izdrui, Diana Craciun, Daniela Maria Geman, Oana Contrast Media Mol Imaging Review Article Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries. Hindawi 2022-07-11 /pmc/articles/PMC9293518/ /pubmed/35919500 http://dx.doi.org/10.1155/2022/1541980 Text en Copyright © 2022 Khurram Ejaz et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Ejaz, Khurram Mohd Rahim, Mohd Shafry Arif, Muhammad Izdrui, Diana Craciun, Daniela Maria Geman, Oana Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI |
title | Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI |
title_full | Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI |
title_fullStr | Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI |
title_full_unstemmed | Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI |
title_short | Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI |
title_sort | review on hybrid segmentation methods for identification of brain tumor in mri |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293518/ https://www.ncbi.nlm.nih.gov/pubmed/35919500 http://dx.doi.org/10.1155/2022/1541980 |
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