A parallel application for 3D reconstruction of coronal loops using image morphing

Faria, Lilian N.; Mascarenhas, Nelson D. A.; Moron, C.E.; Saito, José Hiroki; Rosa, Reinaldo R.; Sawant, H. S.

doi:10.1016/j.imavis.2005.12.007

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Gradient Pattern Analysis (GPA) is a technique for analyzing the dynamics of nonlinear 2D-spatiotemporal systems, which is based on the gradient symmetry-breaking properties of a matrix snapshot sequence. Originally, GPA has found numerous applications in 2D dynamic systems [Rosa et al, 1999, da Silva et al, 2000, Faria et al, 2007Baroni et al, 2006, Freitas et al, 2010. GPA was originally inspired by 2D turbulence analysis in space (Andrade et al, 2006) and environmental physics (Velho et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Gradient Pattern Analysis Applied for Computer Vision in Medical Ultrasound Diagnosis

2023

Proceedings of the 17 Th International Conference on Interfaces and Human Computer Interaction 2023 (IHCI 2023), the 17th Inter

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This paper describes a new application of the technique known as Gradient Pattern Analysis (GPA), focused here on computer vision. In the GPA domain, the image is translated into a tessellation triangulation field based on the vectors positions that make up the gradient lattice of the matrix image. The GPA version considered here generates three attributes (G1, G2 and G3) that can be used as labels for a supervised machine-learning model. The case study presented here shows that GPA is a useful tool for real-time fetal biometry from 2D ultrasound images. The application in obstetrics indicates that the technique can also be useful for learning diagnostic imaging in gynecology, hepatology and oncology. The generalization of the technique to other applications in practical learning in health is discussed.

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