Authors:
Eudoxia Moura
1
;
2
;
Gleidson Costa
3
;
Tiago Borchartt
3
and
Aura Conci
2
Affiliations:
1
Federal Institute of Education, Science and Technology of Rondônia, Ariquemes, Brazil
;
2
Institute of Computing, Federal Fluminense University, Brazil
;
3
Department of Informatics, Federal University of Maranhão, Brazil
Keyword(s):
3D Breast Models, Thermography Images, Edge Detection.
Abstract:
This study enhances the three-dimensional reconstruction of the breast surface from two-dimensional thermal images at five different angles. The process involves the use of a convolutional neural network U-net for image segmentation, extraction of relevant points and curves, geometric transformations to add a dimension to the curves, and modeling with Rational Non-uniform B-splines Surface (NURBS). The work aims to improve the previously proposed algorithm by making modifications to the rotation points and cutting regions to achieve a more comprehensive breast contour. In a quantitative evaluation of the results, comparing thermal images and Structured Light Scanner images with the Costa’s and new algorithm, we observed that, concerning scanning, the Costa’s algorithm has an average of 37.07% with a standard deviation of 6.27, while the new algorithm exhibits an average of 14.06% with a standard deviation of 7.28. When compared to thermal images, the Costa’s algorithm has an average
of 37.47% with a standard deviation of 7.24, whereas the new algorithm shows an average of 14.77% with a standard deviation of 7.37. These results indicate that the new algorithm significantly improves the representation of the breast shape compared to the Costa’s algorithm.
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