| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Sao Carlos, Dept Comp, Rod Washington Luis, Km 235, BR-13565905 Sao Carlos, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | JOURNAL OF NEUROSCIENCE METHODS; v. 341, JUL 15 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Background: Brain Magnetic Resonance (MR) image segmentation methods based on deformable models depend on the initial positioning to maximize the chances of successful segmentation. To minimize this limitation, salient-point based registration is used to perform the initial positioning of brain structure meshes close to their image target representation. The analysis of brain structures (such as the hippocampus) can help in the diagnosis and follow-up of neurodegenerative diseases like Alzheimer's. Methods: We present a technique for detection and description of 3-D salient points, which combines filter response maps estimated for different scales and orientations of the dual-tree complex wavelet transform (DT-CWT). We apply our technique to detect salient points in volumetric brain MR images and use the detected points in a positioning methodology. To illustrate the applicability, we applied our method for the positioning of hippocampi meshes in 3-D brain MR images and assessed the results by overlapping the positioned meshes with manual annotations made by medical specialists. Results: Our method yielded mean values of normalized Dice Similarity Coefficient (nDSC) of 0.74/0.68 and Hausdorff Average Distance (HAD) of 0.73/0.75 for the left and right hippocampus, respectively. Comparison with Other Methods: The mean nDSC and HAD results of our detector were significantly better than the ones achieved by an Affine and a Phase Congruency (PC) guided positioning. Conclusions: The detection via DT-CWT decomposition is computationally less demanding than the detection via PC and represents a robust alternative for the positioning of mesh models. (AU) | |
| FAPESP's process: | 18/06049-5 - Automatic computational scheme for the detection, identification and classification of cerebral structural changes in magnetic resonance images to aid the diagnosis of patients with mild cognitive impairment and mild Alzheimer's disease |
| Grantee: | Katia Maria Poloni |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 18/08826-9 - Development of feature engineering and deep learning techniques applied to the classification of magnetic resonance images in healthy cognitive aging, mild cognitive impairment and Alzheimer's Disease |
| Grantee: | Ricardo José Ferrari |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 17/24391-0 - 3D salient point detector based on the dual-tree complex wavelet transform with application to the positioning of deformable meshes in MR brain images |
| Grantee: | Breno da Silveira Souza |
| Support Opportunities: | Scholarships in Brazil - Master |