The X-ray angiography images are routinely used to assess the blood vessels. The acquisition procedure considers a medical imaging system which allows obtaining views of the vessel while the blood flows thought them. The X-ray source is influenced on the region to be viewed and then, the projection of the all anatomical structures in the champ of view is shown through an image intensifier. The information of the blood vessel is impacted for the other structures. Additionally, the blood and the contrast product required in the acquisition are not mixed homogeneously, producing artifacts in the images. Finally, the noise is also an impact factor in the quality of the angiography images. In the coronary vessel case, the branches of the network are superposed. In this paper, an enhancement procedure to diminish the uncertainty associated to X-ray angiography images is reported. The relation between two versions of the angiograms is determined using a fuzzy connector considering that this relation diminishes the images intrinsic uncertainty. These versions correspond with images filtered with low-pass and high-pass image filters, respectively. The technique is tested with images of the coronary and kidney vessels. The qualitative results show a good enhanced of the angiography images.
The anisotropic diffusion filters have become in the fundamental bases to address the medical images noise problem. The main attributes of these filters are: the noise removal effectiveness and the preservation of the information belonging to the edges that delimit the objects of an image. Due to these excellent attributes, through this article, a comparative study is proposed between a new diffusion operator and the Lorentz operator, proposed by the pioneers of anisotropic diffusion. For this, a strategy consisting of two phases is designed. In the first, called operator construction, the composition of functions is used to generate a new diffusion operator that meets with the conditions reported for this kind of the mathematical object. In the second phase, denominated filtering, a synthetic cardiac images database, based on computed tomography, is filtered using the aforementioned operators. According with the value obtained for the peak of the signal-to-noise ratio, the new operator shows similar performance to the Lorentz operator. The implementation of this new operator contributes to the generation of new knowledge in digital image processing context.
This paper aims to present an updated review of parallel algorithms for solving square and rectangular single and double precision matrix linear systems using multi-core central processing units and graphic processing units. A brief description of the methods for the solution of linear systems based on operations, factorization and iterations was made. The methodology implemented, in this article, is a documentary and it was based on the review of about 17 papers reported in the literature during the last five years (2016-2020). The disclosed findings demonstrate the potential of parallelism to significantly decrease extreme learning machines training times for problems with large amounts of data given the calculation of the Moore Penrose pseudo inverse. The implementation of parallel algorithms in the calculation of the pseudo-inverse will allow to contribute significantly in the applications of diversifying areas, since it can accelerate the training time of the extreme learning machines with optimal results.
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 automatic strategy based on two phases. In the first phase, a digital filtering bank is used for diminishing the noise effect and the artifacts impact in the quality of images. In the second phase, called liver detection, we use a smart operator based on least squares support vector machines for generating both the morphology and the volume of liver. The application of this strategy allows generating the morphology of the liver in a precise and efficient manner as it was demonstrated by the metrics used to assess its performance. These results are very important in clinical-surgical processes where both the shape and volume of liver are vital for monitoring some liver diseases that can affect the normal liver physiology.
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