Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier
Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining thei...
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MDPI AG
2024-10-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/13/10/260 |
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| author | Tushar Hrishikesh Jaware Chittaranjan Nayak Priyadarsan Parida Nawaf Ali Yogesh Sharma Wael Hadi |
| author_facet | Tushar Hrishikesh Jaware Chittaranjan Nayak Priyadarsan Parida Nawaf Ali Yogesh Sharma Wael Hadi |
| author_sort | Tushar Hrishikesh Jaware |
| collection | DOAJ |
| description | Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods. |
| format | Article |
| id | doaj-art-58d1c8d8aa454214b7bacb07e41ea90d |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-58d1c8d8aa454214b7bacb07e41ea90d2025-08-20T02:11:15ZengMDPI AGComputers2073-431X2024-10-01131026010.3390/computers13100260Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled ClassifierTushar Hrishikesh Jaware0Chittaranjan Nayak1Priyadarsan Parida2Nawaf Ali3Yogesh Sharma4Wael Hadi5Department of Electronics and Communication Engineering, R C Patel Institute of Technology, Shirpur 425405, Maharashtra, IndiaDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, GIET University, Gunupur 765022, Odisha, IndiaCollege of Engineering and Technology, American University of the Middle East, Eqaila 54200, KuwaitDepartment of Physics, Faculty of Applied and Basic Sciences, Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram 122505, Haryana, IndiaInformation Security Department, University of Petra, Amman 961343, JordanAutomatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods.https://www.mdpi.com/2073-431X/13/10/260Brier score coupled shrunken centroid classifierminimum spanning tree segmentationManhattan distanceintensity inhomogeneity correctionWiener filteringIsomap technique |
| spellingShingle | Tushar Hrishikesh Jaware Chittaranjan Nayak Priyadarsan Parida Nawaf Ali Yogesh Sharma Wael Hadi Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier Computers Brier score coupled shrunken centroid classifier minimum spanning tree segmentation Manhattan distance intensity inhomogeneity correction Wiener filtering Isomap technique |
| title | Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier |
| title_full | Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier |
| title_fullStr | Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier |
| title_full_unstemmed | Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier |
| title_short | Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier |
| title_sort | enhanced neonatal brain tissue analysis via minimum spanning tree segmentation and the brier score coupled classifier |
| topic | Brier score coupled shrunken centroid classifier minimum spanning tree segmentation Manhattan distance intensity inhomogeneity correction Wiener filtering Isomap technique |
| url | https://www.mdpi.com/2073-431X/13/10/260 |
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