A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation
Abstract The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brain segmentation can result in identifying...
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| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81648-9 |
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| author | Saritha Saladi Karuna Yepuganti Ravikumar Chinthaginjala Tae-hoon Kim Shafiq Ahmad |
| author_facet | Saritha Saladi Karuna Yepuganti Ravikumar Chinthaginjala Tae-hoon Kim Shafiq Ahmad |
| author_sort | Saritha Saladi |
| collection | DOAJ |
| description | Abstract The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brain segmentation can result in identifying region boundaries as a set of contours. However, segmenting brain images poses several challenges, including noise, bias field, and partial volume effect (PVE). Removing noise, accurately segmenting tissues and tumors are crucial for effective evaluation. To enhance tissue and tumor segmentation, a new machine learning-based method called as Gaussian-Kernelized Enhanced Intuitionistic Fuzzy-C-Means (GKEIFCM) has been proposed. Approach enhances Improved Intuitionistic Fuzzy-C-Means Algorithm (IIFCM) by utilizing Gaussian kernelized distance between pixels, resulting in uncomplicated segmentation with reduced computational times and improved efficiency. This proposed novel method proved to be expertise in tissue and tumor classification and identification respectively. The results demonstrate the effectiveness of GKEIFCM interms of Dice, Jaccard-similarity-index, Accuracy and Execution time. |
| format | Article |
| id | doaj-art-30d201da1a744b80bccbcec274faa18f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-30d201da1a744b80bccbcec274faa18f2024-12-01T12:20:13ZengNature PortfolioScientific Reports2045-23222024-11-0114111310.1038/s41598-024-81648-9A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentationSaritha Saladi0Karuna Yepuganti1Ravikumar Chinthaginjala2Tae-hoon Kim3Shafiq Ahmad4School of Electronics Engineering, VIT-AP UniversitySchool of Electronics Engineering, VIT-AP UniversitySchool of Electronics Engineering, Vellore Institute of TechnologySchool of Information and Electronic Engineering and Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Zhejiang University of Science and TechnologyIndustrial Engineering Department, College of Engineering, King Saud UniversityAbstract The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brain segmentation can result in identifying region boundaries as a set of contours. However, segmenting brain images poses several challenges, including noise, bias field, and partial volume effect (PVE). Removing noise, accurately segmenting tissues and tumors are crucial for effective evaluation. To enhance tissue and tumor segmentation, a new machine learning-based method called as Gaussian-Kernelized Enhanced Intuitionistic Fuzzy-C-Means (GKEIFCM) has been proposed. Approach enhances Improved Intuitionistic Fuzzy-C-Means Algorithm (IIFCM) by utilizing Gaussian kernelized distance between pixels, resulting in uncomplicated segmentation with reduced computational times and improved efficiency. This proposed novel method proved to be expertise in tissue and tumor classification and identification respectively. The results demonstrate the effectiveness of GKEIFCM interms of Dice, Jaccard-similarity-index, Accuracy and Execution time.https://doi.org/10.1038/s41598-024-81648-9MRIBrain segmentationTissue classificationTumor identificationMachine learningIntuitionistic –FCM |
| spellingShingle | Saritha Saladi Karuna Yepuganti Ravikumar Chinthaginjala Tae-hoon Kim Shafiq Ahmad A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation Scientific Reports MRI Brain segmentation Tissue classification Tumor identification Machine learning Intuitionistic –FCM |
| title | A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation |
| title_full | A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation |
| title_fullStr | A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation |
| title_full_unstemmed | A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation |
| title_short | A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation |
| title_sort | unique unsupervised enhanced intuitionistic fuzzy c means for mr brain tissue segmentation |
| topic | MRI Brain segmentation Tissue classification Tumor identification Machine learning Intuitionistic –FCM |
| url | https://doi.org/10.1038/s41598-024-81648-9 |
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