38 research outputs found
Uncertainty as key element in the analysis of X–ray angiography images
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
New anisotropic diffusion operator in images filtering
The anisotropic di usion lters have become in the fundamental bases to address
the medical images noise problem. The main attributes of these lters are: the noise removal
e ectiveness 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 di usion operator and the Lorentz operator, proposed by the pioneers
of anisotropic di usion. For this, a strategy consisting of two phases is designed. In the rst,
called operator construction, the composition of functions is used to generate a new di usion
operator that meets with the conditions reported for this kind of the mathematical object. In the
second phase, denominated ltering, a synthetic cardiac images database, based on computed
tomography, is ltered 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
The rubric as an assessment strategy in the mathematical argumentation process
The article shares the proposal of an analytical rubric as a strategy for the assessment and monitoring of learning outcomes in students who develop an argumentative plot from the mathematics field, to solve any problem situation in daily life. The study was based on the theory of mathematical argumentation proposed by Duval and the contributions of LeĂłn and CalderĂłn, as well as the dimensions presented to us by the logical frameworks in the design of analytical rubrics. The research was developed under the social critical paradigm through the design of pedagogical action research, and the focus group technique was used for the collection of information composed by five professors from the department of basic sciences. As a result, a collective rubric that, in addition to generating processes of self-assessment and self-training in teachers, evidences a decrease in the existent subjectivity of the evaluation processes, thus strengthening its objectivity
Parallel methods for linear systems solution in extreme learning machines: an overview
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
Pulmonary adenocarcinoma characterization using computed tomography images
Lung cancer is one of the pathologies that sensitively affects the health of human
beings. Particularly, the pathology called pulmonary adenocarcinoma represents 25% of all lung
cancers. In this research, we propose a semiautomatic technique for the characterization of a
tumor (adenocarcinoma type), present in a three-dimensional pulmonary computed tomography
dataset. Following the basic scheme of digital image processing, first, a bank of smoothing filters
and edge detectors is applied allowing the adequate preprocessing over the dataset images. Then,
clustering methods are used for obtaining the tumor morphology. The relative percentage error
and the accuracy rate were the metrics considered to determine the performance of the proposed
technique. The values obtained from the metrics used reflect an excellent correlation between
the morphology of the tumor, generated manually by a pneumologist and the values obtained by
the proposed technique. In the clinical and surgical contexts, the characterization of the detected
lung tumor is made in terms of volume occupied by the tumor and it allows the monitoring of
this disease as well as the activation of the respective protocols for its approach
Usefulness of digital images segmentation in pulmonary transplantation
In the presence of pulmonary pathologies such as chronic obstructive pulmonary
disease, diffuse pulmonary disease and cystic fibrosis, among others, it is common to require
the removal or replacement of a portion of lungs. There are several requirements for both
donors and organ receivers (recipients) established in the literature. May be the main one is the
volume that the donor's lungs occupy in the thoracic cavity. This parameter is vital because if
the volume of the lungs exceeds the thoracic cavity of the recipients the transplant, logically, is
unfeasible for physical reasons such as the incompatibility between the receiver lung volume
and the donor lung volume. In this sense, the present paper proposes the creation of a hybrid
technique, based on digital image processing techniques application to raise the quality of the
information related to lungs captured in three-dimensional sequences of computed tomography
and for generating the morphology and the volumes of the lungs, belonging to a patient. During
the filtering stage median, saturated and gradient magnitude filters are applied with the purpose
of addressing the noise and artefacts images problems; whereas during the segmentation stage,
methods based on clustering processes are used to extract the lungs from the images. The
values obtained for the metric that assesses the quality of the hybrid computational technique
reflect its good performance. Additionally, these results are very important in clinical processes
where both the shapes and volumes of lungs are vital for monitoring some lung diseases that
can affect the normal lung physiology
Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
Leishmaniasis is a complex group of diseases caused by obligate unicellular and
intracellular eukaryotic protozoa of the leishmania genus. Leishmania species generate diverse
syndromes ranging from skin ulcers of spontaneous resolution to fatal visceral disease. These
syndromes belong to three categories: visceral leishmaniasis, cutaneous leishmaniasis and
mucosal leishmaniasis. The visceral leishmaniasis is based on the reticuloendothelial system
producing hepatomegaly, splenomegaly and lymphadenopathy. In the present article, a semiautomatic
segmentation strategy is proposed to obtain the segmentations of the evolutionary
shapes of visceral leishmaniasis called parasites, specifically of the type amastigote and
promastigote. For this purpose, the optical microscopy images containing said evolutionary
shapes, which are generated from a blood smear, are subjected to a process of transformation
of the color intensity space into a space of intensity in gray levels that facilitate their
subsequent preprocessing and adaptation. In the preprocessing stage, smoothing filters and
edge detectors are used to enhance the optical microscopy images. In a complementary way, a
segmentation technique that groups the pixels corresponding to each one of the parasites,
presents in the considered images, is applied. The results reveal a high correspondence between
the available manual segmentations and the semi-automatic segmentations which are useful for
the characterization of the parasites. The obtained segmentations let us to calculate areas and
perimeters associated with the parasites segmented. These results are very important in clinical
context where both the area and perimeter calculated are vital for monitoring the development
of visceral leishmaniasis
Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures
A spatial geometric plane is defined by the three-dimensional coordinates of a pair of
spatial points and the direction that the normal vector establishes, which is formed by joining
those points by means of an oriented line segment. This type of planes, in three-dimensional
images, is extremely useful as an alternative solution to the problem of low contrast that exhibit
the anatomical structures present in cardiac computed tomography images. To do this, after using
a predetermined filter bank and in order to define a region of interest, a smart operator based on
least squares support vector machines is trained and validated in order to detect the
aforementioned coordinates which enables the location of the plane, in the three-dimensional
space that contains the considered images. Once the structure that is required to segment is
identified, a discriminant function is used that cancels all information not linked to this structure.
In this work, the segmentation of the left ventricle, based on region growing technique, is firstly
considered and then the left atrium is segmented considering region growing technique and an
inverse discriminant function. The results show an excellent correspondence relationship when
the spatial union of both structures is made
Large cells cancer volumetry in chest computed tomography pulmonary images
Lung cancer is the leading oncological cause of death in the world. As for
carcinomas, they represent between 90% and 95% of lung cancers; among them, non-small cell
lung cancer is the most common type and the large cell carcinoma, the pathology on which this
research focuses, is usually detected with the computed tomography images of the thorax.
These images have three big problems: noise, artifacts and low contrast. The volume of the
large cell carcinoma is obtained from the segmentations of the cancerous tumor generated, in a
semi-automatic way, by a computational strategy based on a combination of algorithms that, in
order to address the aforementioned problems, considers median and gradient magnitude filters
and an unsupervised grouping technique for generating the large cell carcinoma morphology.
The results of high correlation between the semi-automatic segmentations and the manual ones,
drawn up by a pulmonologist, allow us to infer the excellent performance of the proposed
technique. This technique can be useful in the detection and monitoring of large cell carcinoma
and if it is considering this kind of computational strategy, medical specialists can establish the
clinic or surgical actions oriented to address this pulmonary pathology
Use of computational realistic models for the cardiac ejection fraction calculation
Ejection fraction is one of the most useful clinical descriptors to determine the cardiac
function of a subject. For this reason, obtaining the value of this descriptor is of vital importance
and requires high precision. However, in the clinical routine, to generate the mentioned
descriptor value, a geometric hypothesis is assumed, obtaining an approximate value for this
fraction, usually by excess, and which is a dependent-operator. The aim of the present work is
to propose the accurate calculation of the ejection fraction from realistic models, obtained
computationally, of the cardiac chamber called right ventricle. Normally, the geometric
hypothesis that makes this ventricle coincide with a pyramidal type geometric shape, is not
usually, fulfilled in subjects affected by several cardiac pathologies, so as an alternative to this
problem, the computational segmentation process is used to generate the morphology of the right
ventricle and from it proceeds to obtain, accurately, the ejection fraction value. In this sense, an
automatic strategy based on no-lineal filters, smart operator and region growing technique is
propose in order to generate the right ventricle ejection fraction. The results are promising due
we obtained an excellent correspondence between the manual segmentation and the automatic
one generated by the realistic models