An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure
Joint image segmentation and registration of multi-modality images is a crucial step in the field of image prepossessing. The sensitivity of joint segmentation and registration models to noise is a significant challenge. During the registration process of multi-modal images, the similarity measure p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822003921 |
| _version_ | 1828088586464919552 |
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| author | Nasra Begum Noor Badshah Lavdie Rada Adela Ademaj Muniba Ashfaq Hadia Atta |
| author_facet | Nasra Begum Noor Badshah Lavdie Rada Adela Ademaj Muniba Ashfaq Hadia Atta |
| author_sort | Nasra Begum |
| collection | DOAJ |
| description | Joint image segmentation and registration of multi-modality images is a crucial step in the field of image prepossessing. The sensitivity of joint segmentation and registration models to noise is a significant challenge. During the registration process of multi-modal images, the similarity measure plays a vital role in measuring the results as a standard. Accordingly, an improved joint model for registering and segmenting multi-modality images is proposed, by utilising the Bhattacharyya distance measure to achieve improved noise robustness of the proposed model as compared to the existing model using the mutual information metric. The proposed model is applied to various medical and synthetic noisy images of multiple modalities. Moreover, the dataset images used in this study have been obtained from well-known, freely available BRATS 2015 and CHAOS datasets, where the proposed model produces satisfactory results as compared to the existing model. Experimental results show that the proposed model outperforms the existing model in terms of the Bhattacharyya distance measure in noisy images. Statistical analysis and comparison are performed through the relative reduction of the new distance measure, Dice similarity coefficient, Jaccard similarity coefficient and Hausdorff distance. |
| first_indexed | 2024-04-11T05:27:48Z |
| format | Article |
| id | doaj.art-0d58f22484a54ac4af9e8a1406bab26d |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-11T05:27:48Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-0d58f22484a54ac4af9e8a1406bab26d2022-12-23T04:39:33ZengElsevierAlexandria Engineering Journal1110-01682022-12-0161121235312365An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measureNasra Begum0Noor Badshah1Lavdie Rada2Adela Ademaj3Muniba Ashfaq4Hadia Atta5Department of Basic Sciences, University of Engineering and Technology, Peshawar, PakistanFaculty Of Engineering And Natural Sciences Bahcesehir University, Istanbul, TurkeyFaculty Of Engineering And Natural Sciences Bahcesehir University, Istanbul, TurkeyMedical Faculty of University of Zurich, SwitzerlandDepartment of Computer Systems Engineering, University of Engineering and Technology, Peshawar, PakistanDepartment of Mathematics, Islamia College Peshawar, PakistanJoint image segmentation and registration of multi-modality images is a crucial step in the field of image prepossessing. The sensitivity of joint segmentation and registration models to noise is a significant challenge. During the registration process of multi-modal images, the similarity measure plays a vital role in measuring the results as a standard. Accordingly, an improved joint model for registering and segmenting multi-modality images is proposed, by utilising the Bhattacharyya distance measure to achieve improved noise robustness of the proposed model as compared to the existing model using the mutual information metric. The proposed model is applied to various medical and synthetic noisy images of multiple modalities. Moreover, the dataset images used in this study have been obtained from well-known, freely available BRATS 2015 and CHAOS datasets, where the proposed model produces satisfactory results as compared to the existing model. Experimental results show that the proposed model outperforms the existing model in terms of the Bhattacharyya distance measure in noisy images. Statistical analysis and comparison are performed through the relative reduction of the new distance measure, Dice similarity coefficient, Jaccard similarity coefficient and Hausdorff distance.http://www.sciencedirect.com/science/article/pii/S1110016822003921Image registrationImage segmentationLinear curvature (LC)Bhattacharyya (BC) distanceMutual information (MI)Dice similarity coefficient (DSC) |
| spellingShingle | Nasra Begum Noor Badshah Lavdie Rada Adela Ademaj Muniba Ashfaq Hadia Atta An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure Alexandria Engineering Journal Image registration Image segmentation Linear curvature (LC) Bhattacharyya (BC) distance Mutual information (MI) Dice similarity coefficient (DSC) |
| title | An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure |
| title_full | An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure |
| title_fullStr | An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure |
| title_full_unstemmed | An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure |
| title_short | An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure |
| title_sort | improved multi modal joint segmentation and registration model based on bhattacharyya distance measure |
| topic | Image registration Image segmentation Linear curvature (LC) Bhattacharyya (BC) distance Mutual information (MI) Dice similarity coefficient (DSC) |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822003921 |
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