Brain hematoma computational segmentation
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Fecha
2018
Autores
Sáenz, F
Vera, M
Huerfano, Y
Molina, V
Martinez, L
Vera, M I
Salazar, W
Gelvez, E
Salazar, J
Valbuena, O
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Editor
IOP Publishing
Resumen
In computed tomography imaging, brain hematoma (BH) segmentation is a very
challenging process due to a high variability of BH morphology, low contrast and noisy
images. Because of this, BH segmentation is an open problem. In order to approach this
problem, we propose an automatic technique, named nonlinear technique (NLT), based on a
thresholding method, noise suppression filters, intelligent operators, a clustering strategy and a
binary morphological operator. NLT performance is assessed by Jaccard's similarity index
(JSI) used to compare automatic and manual BH segmentations. This assessment allows
developing a tuning process for establishing the optimal parameters of each of the algorithms
which constitute the proposed technique. The results indicate a good correlation, based on JSI,
between the manual segmentations and the automatic ones. Finally, the BH volume is
generated considering the automatic segmentation. This volume indicates whether or not the
patient must undergo a surgical intervention for BH treatment.