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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Detector of 3-D salient points based on the dual-tree complex wavelet transform for the positioning of hippocampi meshes in magnetic resonance images

Full text
Author(s):
Souza, Breno da Silveira [1] ; Poloni, Katia M. [1] ; Ferrari, Ricardo J. [1]
Total Authors: 3
Affiliation:
[1] Univ Fed Sao Carlos, Dept Comp, Rod Washington Luis, Km 235, BR-13565905 Sao Carlos, SP - Brazil
Total Affiliations: 1
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