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Brain hematoma computational segmentation
(IOP Publishing, 2018)
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 ...
Smart operator for the human liver automatic segmentation, present in medical images
(IOP Publishing, 2019)
The segmentation of the human body organ called liver is a highly challenging problem due to the noise, artifacts and the low contrast exhibited by the anatomical structures located around the liver and that are present in digital images, generated by any modality of medical images. The main modalities are: ultrasound, nuclear emission, magnetic resonance and the gold standard called multi-slice computed tomography. In this paper, with the objective of to address this problem, we consider multi-slice computed tomography images and we propose an ...
Semi-automatic detection of hepatic tumor in computed tomography images
(IOP Publishing, 2019)
In this work, the main purpose is develop a computational segmentation strategy for
liver tumor semiautomatic detection. This strategy considers three-dimensional computed
tomography images and it consists of techniques application that, on the one hand, diminish the
noise and detect the edges of the objects present in those images and, on the other hand, generate
the liver tumor morphology. For this, the sequence of techniques composed of gaussian
smoothing, gradient magnitude, median filter, region growing and binary morphological dilation
are ...