Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method
Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is conside...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-12-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-ipr.2020.0383 |
| _version_ | 1818517508399824896 |
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| author | Prasun Chandra Tripathi Soumen Bag |
| author_facet | Prasun Chandra Tripathi Soumen Bag |
| author_sort | Prasun Chandra Tripathi |
| collection | DOAJ |
| description | Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations. |
| first_indexed | 2024-12-11T00:57:11Z |
| format | Article |
| id | doaj.art-7fee9c8c6aeb4824b52d6f0f3a6eb09b |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-12-11T00:57:11Z |
| publishDate | 2020-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-7fee9c8c6aeb4824b52d6f0f3a6eb09b2022-12-22T01:26:26ZengWileyIET Image Processing1751-96591751-96672020-12-0114153705371710.1049/iet-ipr.2020.0383Segmentation of brain magnetic resonance images using a novel fuzzy clustering based methodPrasun Chandra Tripathi0Soumen Bag1Department of Computer Science and EngineeringIndian Institute of Technology (ISM)DhanbadIndiaDepartment of Computer Science and EngineeringIndian Institute of Technology (ISM)DhanbadIndiaSegmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer‐aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non‐uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C‐means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel‐induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.https://doi.org/10.1049/iet-ipr.2020.0383brain magnetic resonance imagescomputer‐aided diagnosisbrain diseasesbrain MR imagesLIFCMMR brain images |
| spellingShingle | Prasun Chandra Tripathi Soumen Bag Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method IET Image Processing brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images |
| title | Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method |
| title_full | Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method |
| title_fullStr | Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method |
| title_full_unstemmed | Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method |
| title_short | Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method |
| title_sort | segmentation of brain magnetic resonance images using a novel fuzzy clustering based method |
| topic | brain magnetic resonance images computer‐aided diagnosis brain diseases brain MR images LIFCM MR brain images |
| url | https://doi.org/10.1049/iet-ipr.2020.0383 |
| work_keys_str_mv | AT prasunchandratripathi segmentationofbrainmagneticresonanceimagesusinganovelfuzzyclusteringbasedmethod AT soumenbag segmentationofbrainmagneticresonanceimagesusinganovelfuzzyclusteringbasedmethod |