Kernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentation
Abstract This paper addresses the challenge of accurately segmenting complex regions in medical images, where traditional clustering methods often struggle due to noise sensitivity and unclear boundaries. Our objective is to develop a robust clustering approach for medical image segmentation. We pre...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
|
| Series: | Discover Computing |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s10791-025-09697-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849342113618067456 |
|---|---|
| author | Kanika Bhalla Sonika Dahiya Anjana Gosain |
| author_facet | Kanika Bhalla Sonika Dahiya Anjana Gosain |
| author_sort | Kanika Bhalla |
| collection | DOAJ |
| description | Abstract This paper addresses the challenge of accurately segmenting complex regions in medical images, where traditional clustering methods often struggle due to noise sensitivity and unclear boundaries. Our objective is to develop a robust clustering approach for medical image segmentation. We present Kernelized Type 2 Intuitionistic Fuzzy C Means (KT2IFCM), which integrates a Radial Basis Function (RBF) kernel with Type 2 Intuitionistic Membership and hesitation degree. This method improves boundary definition, centroid placement, and handles non-linear structures. Results from tests on 14 datasets (4 simulated, 10 real brain MRI scans) show that KT2IFCM achieves superior noise resilience and segmentation accuracy compared to FCM, IFCM, KIFCM, and T2IFCM. The novelty lies in combining kernel mapping with intuitionistic fuzzy membership to deliver more reliable segmentation in medical imaging. Statistical analysis using the Friedman test further confirms KT2IFCM’s improved accuracy over competing methods on synthetic datasets. |
| format | Article |
| id | doaj-art-054f0084b101493ca9da063fb9e2e73b |
| institution | Kabale University |
| issn | 2948-2992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Computing |
| spelling | doaj-art-054f0084b101493ca9da063fb9e2e73b2025-08-20T03:43:30ZengSpringerDiscover Computing2948-29922025-08-0128113010.1007/s10791-025-09697-wKernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentationKanika Bhalla0Sonika Dahiya1Anjana Gosain2USICT, Guru Gobind Singh Indraprastha UniversityDTU (Formerly Delhi College of Engineering)USICT, Guru Gobind Singh Indraprastha UniversityAbstract This paper addresses the challenge of accurately segmenting complex regions in medical images, where traditional clustering methods often struggle due to noise sensitivity and unclear boundaries. Our objective is to develop a robust clustering approach for medical image segmentation. We present Kernelized Type 2 Intuitionistic Fuzzy C Means (KT2IFCM), which integrates a Radial Basis Function (RBF) kernel with Type 2 Intuitionistic Membership and hesitation degree. This method improves boundary definition, centroid placement, and handles non-linear structures. Results from tests on 14 datasets (4 simulated, 10 real brain MRI scans) show that KT2IFCM achieves superior noise resilience and segmentation accuracy compared to FCM, IFCM, KIFCM, and T2IFCM. The novelty lies in combining kernel mapping with intuitionistic fuzzy membership to deliver more reliable segmentation in medical imaging. Statistical analysis using the Friedman test further confirms KT2IFCM’s improved accuracy over competing methods on synthetic datasets.https://doi.org/10.1007/s10791-025-09697-wFuzzy clusteringMRI segmentationRadial basis function (RBF) kernel |
| spellingShingle | Kanika Bhalla Sonika Dahiya Anjana Gosain Kernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentation Discover Computing Fuzzy clustering MRI segmentation Radial basis function (RBF) kernel |
| title | Kernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentation |
| title_full | Kernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentation |
| title_fullStr | Kernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentation |
| title_full_unstemmed | Kernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentation |
| title_short | Kernelized Type 2 Intuitionistic Fuzzy C Means: a robust approach for brain MRI image segmentation |
| title_sort | kernelized type 2 intuitionistic fuzzy c means a robust approach for brain mri image segmentation |
| topic | Fuzzy clustering MRI segmentation Radial basis function (RBF) kernel |
| url | https://doi.org/10.1007/s10791-025-09697-w |
| work_keys_str_mv | AT kanikabhalla kernelizedtype2intuitionisticfuzzycmeansarobustapproachforbrainmriimagesegmentation AT sonikadahiya kernelizedtype2intuitionisticfuzzycmeansarobustapproachforbrainmriimagesegmentation AT anjanagosain kernelizedtype2intuitionisticfuzzycmeansarobustapproachforbrainmriimagesegmentation |