31 research outputs found
Problem solving strategy in the teaching and learning processes of quantitative reasoning
The study presents an analysis of Polya's problem-solving strategy used in the training
processes of quantitative reasoning competence in students of the Universidad SimĂłn BolĂvar,
San JosĂ© de CĂșcuta, Colombia. The research was based on a descriptive design and had an
intentional sample of 58 students who were studying the sciences and general competencies
elective. For the collection of information, a diagnostic test (pre-test) and a final test (post-test)
were applied, in order to check the incidence of the applied strategy. The results showed a
significant improvement in the final results obtained by the students in each of the processes
formed: interpretation, representation and modeling, and argumentation
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
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
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
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
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
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
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
Left atrial appendage automatic segmentation, in computed tomography images
The left atrial appendage is one of the anatomical places where most frequently
blood thrombi occur. When migrating from the appendage, these thrombi, become blood
emboli that, potentially, can compromise the physiology and/or anatomy of cardiac or cerebral
blood vessels, being able to generate cerebrovascular events. The left atrial appendage
segmentation is very difficult due, mainly, to its location and the identical densitometric
information presents into of this appendage and around of the left atrium. In this paper, an
automatic technique is proposed to segment this appendage with the purpose of generating
important information to the procedure called left atrial appendage surgical closure. This
information is linked to the volume and the diameters of the left atrial appendage. The
technique consists of a digital pre-processing stage, based on filtering processes and definition
of a region of interest and, of one segmentation stage that considers a clustering method. The
results are promising and they allow us to calculate useful quantitative variables when
characterizing the most lethal appendix of the human body represented by the mentioned
appendage. These results are very important in clinical processes where both the shape and
volume of this appendage are vital for detecting and monitoring some vascular diseases such as
cardiac embolism, arterial hypertension and stroke, among others