An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis
Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/3/475 |
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| author | Suresh Kanniappan Duraimurugan Samiayya Durai Raj Vincent P M Kathiravan Srinivasan Dushantha Nalin K. Jayakody Daniel Gutiérrez Reina Atsushi Inoue |
| author_facet | Suresh Kanniappan Duraimurugan Samiayya Durai Raj Vincent P M Kathiravan Srinivasan Dushantha Nalin K. Jayakody Daniel Gutiérrez Reina Atsushi Inoue |
| author_sort | Suresh Kanniappan |
| collection | DOAJ |
| description | Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different <i>k</i>. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information. |
| first_indexed | 2024-04-11T21:59:55Z |
| format | Article |
| id | doaj.art-da002d0e4aeb420e84a81119f977bbe7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T21:59:55Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-da002d0e4aeb420e84a81119f977bbe72022-12-22T04:00:58ZengMDPI AGElectronics2079-92922020-03-019347510.3390/electronics9030475electronics9030475An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor DiagnosisSuresh Kanniappan0Duraimurugan Samiayya1Durai Raj Vincent P M2Kathiravan Srinivasan3Dushantha Nalin K. Jayakody4Daniel Gutiérrez Reina5Atsushi Inoue6Department of Information Technology, St. Joseph’s College of Engineering, Chennai, Tamil Nadu 600119, IndiaDepartment of Information Technology, St. Joseph’s College of Engineering, Chennai, Tamil Nadu 600119, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, IndiaSchool of Computer Science and Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, RussiaDepartment of Electronic Engineering, University of Seville, 41092 Sevilla, SpainInformation Systems and Business Analytics Department, Eastern Washington University, Spokane, WA 99202, USABrain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different <i>k</i>. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information.https://www.mdpi.com/2079-9292/9/3/475mr brain segmentationfuzzy clusteringobject extractionsilhouette analysisdicom processing3d modeling |
| spellingShingle | Suresh Kanniappan Duraimurugan Samiayya Durai Raj Vincent P M Kathiravan Srinivasan Dushantha Nalin K. Jayakody Daniel Gutiérrez Reina Atsushi Inoue An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis Electronics mr brain segmentation fuzzy clustering object extraction silhouette analysis dicom processing 3d modeling |
| title | An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis |
| title_full | An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis |
| title_fullStr | An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis |
| title_full_unstemmed | An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis |
| title_short | An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis |
| title_sort | efficient hybrid fuzzy clustering driven 3d modeling of magnetic resonance imagery for enhanced brain tumor diagnosis |
| topic | mr brain segmentation fuzzy clustering object extraction silhouette analysis dicom processing 3d modeling |
| url | https://www.mdpi.com/2079-9292/9/3/475 |
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