Children learning of programming: Learn-Play-Do approach

Writing computer programs is a skill that can beintroduced to children and adolescents since early ages. Althoughchildren can gain skills in coding, there is a lack of motivationand easiness at the time to write logic structures. It raises thequestion, how can children be encouraged to code in a successfulenvironment of learning and fun?. To address this question,this paper shows an experimental approach called ”Learn-Play-Do” for introducing children in the programming. It shows that(1) it is feasible for children to learn about programming byfollowing the proposed approach with (2) encouraging levels oflearning, usefulness content and self-learning programming in(3) a developing country context. The results of an empiricalexperimentation with forty-one children are reported. This workwas implemented as a social project linking the university withthe community. &nbsp

A Review of Algorithms for Retinal Vessel Segmentation

oai:ojs.pkp.sfu.ca:article/41This paper presents a review of algorithms for extracting blood vessels network from retinal images. Since retina is a complex and delicate ocular structure, a huge effort in computer vision is devoted to study blood vessels network for helping the diagnosis of pathologies like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma.  To carry out this process many works for normal and abnormal images have been proposed recently. These methods include combinations of algorithms like Gaussian and Gabor filters, histogram equalization, clustering, binarization, motion contrast, matched filters, combined corner/edge detectors, multi-scale line operators, neural networks, ants, genetic algorithms, morphological operators. To apply these algorithms pre-processing tasks are needed. Most of these algorithms have been tested on publicly retinal databases. We have include a table summarizing algorithms and results of their assessment

Procesamiento de imágenes de fondo de ojo para mejorar el diagnóstico de Retinopatía de la Prematuridad

La Retinopatía de la Prematuridad (ROP) es una enfermedad ocular que puede desarrollarse en los niños nacidos prematuramente o con bajo peso. La ROP es diagnosticada, principalmente, con base en el análisis de las imágenes de fondo de ojo obtenidas en un examen de oftalmoscopia. La etapa más grave de esta patología se da cuando el prematuro sufre desprendimiento de la retina y pierde la visión. Por lo cual, un temprano diagnóstico permite a los médicos aplicar el tratamiento adecuado. Conociendo que, la ROP es diagnosticada mediante el análisis de las imágenes de fondo de ojo, esta tesis estudia la aplicación de la visión artifcial en el diagnóstico de ROP asistido por computador. La hipótesis de esta tesis plantea que, los métodos computacionales de visión artifcial pueden asistir a los médicos en la interpretación de las imágenes de fondo de ojo con presencia de ROP y facilitar su diagnóstico. La hipótesis ha sido comprobada a lo largo del desarrollo de esta tesis. Se han propuesto cuatro métodos: uno para la mejora adaptativa de brillo y contraste de las imágenes patológicas, dos métodos para la segmentación de la retina vascular y un último método para la detección automática del disco óptico. Adicionalmente, en el desarrollo de esta tesis se ha establecido como requisito fundamental que, los métodos propuestos puedan ser ejecutados en ordenadores de gama media. Este requisito tiene por objetivo permitir al personal médico utilizar estos métodos en sus entornos habituales. El método de mejora adaptativa del fondo de ojo patológico ha sido implementado utilizando una red neuronal de una capa oculta, logrando tiempos de ejecución en el orden de los milisegundos. Uno de los métodos de segmentación de la red vascular ha sido desarrollado mediante operaciones matriciales y fltros de coincidencia, los cuales demandan recursos de hardware de gama media. Así mismo, el método de detección del disco óptico ha requerido hardware promedio, para la creación y uso de la solución a través de un modelo de disco óptico basado en patrones del histograma. Solamente el segundo de los métodos de segmentación de la red vascular ha sido diseñado con una red neuronal convolucional (CNN), por lo cual, requiere un ordenador de altas prestaciones. Uno de los mayores retos en el desarrollo de esta tesis ha sido el analizar imágenes de nacidos prematuros con ROP. Estas imágenes generalmente son de difícil tratamiento automático por su condición de patología y además presentan bajo contraste y brillo. Por otro lado, no ha sido posible obtener imágenes de fondo de ojo con ROP de otras investigaciones, al no permitir el acceso público. Por lo cual, se ha tenido que utilizar principalmente un conjunto de imágenes de fondo de ojo proporcionado por la fundación Metrofraternidad, del Hospital Metropolitano de la ciudad de Quito, Ecuador. Este conjunto de imágenes ha sido ampliado para poder desarrollar y evaluar los métodos computacionales. Para lo cual, se han creado las imágenes de máscaras binarias y de segmentación manual de la red vascular. Este conjunto de imágenes compuesto por los fondos de ojo originales y sus correspondientes máscaras y segmentación vascular, ha sido denominado como ROPFI. Consecuentemente, los métodos desarrollados en esta tesis han sido validados, principalmente, con las imágenes de ROPFI. El rendimiento de los métodos propuestos en esta tesis ha sido evaluado utilizando las métricas adecuadas a cada método y ampliamente utilizadas en la comunidad informáticamédica, por ejemplo, el índice de contraste, la matriz de confusión, el índice de similitud estructural, entre otros. Así también, el rendimiento de los métodos propuestos en esta tesis ha sido sometido a un análisis comparativo con el rendimiento reportado en las propuestas de otros investigadores. Los resultados de rendimiento de los métodos propuestos en esta tesis permiten concluir la pertinencia e importantes aportes de esta investigación. ABSTRACT Retinopathy of Prematurity (ROP) is an eye disease that can develop in children born prematurely or with low birth weight. ROP is diagnosed primarily based on the analysis of fundus images obtained in an ophthalmoscopy examination. The most severe stage of this pathology occurs when the premature infant sufers retinal detachment and loses vision. Accordingly, an early diagnosis allows physicians to apply the appropriate treatment. Comprehending that ROP is diagnosed through the analysis of fundus images, this thesis studies the application of computer vision in the computer-aided diagnosis of ROP. This thesis hypothesizes that computational computer vision methods can assist physicians in interpreting fundus images in the presence of ROP and facilitate its diagnosis. The hypothesis has been proved throughout the development of this thesis. Four methods have been proposed: one for adaptive brightness and contrast enhancement of pathological images, two methods for vascular retinal segmentation, and the last method for automatic optic disc detection. Additionally, it has been established as a fundamental requirement that the proposals have to be executed on mid-range computers. This requirement is intended to allow medical personnel to use these methods in their usual environments. The method of adaptive enhancement of the pathological fundus has been implemented using a neural network of a hidden layer, achieving execution times in the order of milliseconds. One of the vascular network segmentation methods has been developed using matrix operations and matching flters, which also demand mid-range hardware resources. Similarly, the optical disk detection method has required mean hardware for creating and using the solution through an optical disk model based on histogram patterns. Only the second of the vascular network segmentation methods has been designed with a convolutional neural network (CNN), thus requiring a high-performance computer. One of the most signifcant challenges in developing this thesis has been analyzing images of pathological preterm infants, which are generally difcult to treat automatically due to their pathological condition and also present low contrast and brightness. On the other hand, it has not been possible to obtain fundus images with ROP from other investigations, as they do not allow public access. Therefore, preparing and using a set of fundus images provided by the Metrofraternidad Foundation of the Metropolitan Hospital of Quito, Ecuador, has been necessary. This image set has been augmented to develop and evaluate the computational methods. For this purpose, images of binary masks and manual vascular network segmentation have been created. The set of images composed of the original fundus images and their corresponding masks and vascular segmentation has been named ROPFI. Consequently, the methods developed in this thesis have been validated mostly with the ROPFI images. The performance of the methods proposed in this thesis has been evaluated using metrics appropriate to each method and widely used in the medical informatics community, for example, the contrast index, the confusion matrix, the structural similarity index, among others. Also, the performance of the proposals has been subjected to a comparative analysis with the performance of other investigations. Finally, the high performance of the proposals in this thesis allows concluding the relevance and important contributions of this research

Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network

In this paper, we describe the experimentation with a convolutional neural network for segmenting retinal net from pathological fundus images of preterm born children. Segmenting retinal net from pathological fundus images is a fundamental task to aid computer diagnosis. We used U-net architecture for training and testing. Testing with ROPFI dataset, we obtained an area under the receiver operating curve equal to 0.9180; when average sensitivity is equal to 0.700, the average specificity is equal to 0.9710. This performance is higher than prior works using a similar dataset

Quantitative Measures for Medical Fundus and Mammography Images Enhancement

Enhancing the visibility of medical images is part of the initial or preprocessing phase within a computer vision system. This image preparation is essential for subsequent system tasks such as segmentation or classification. Therefore, quantitative validation of medical image preprocessing is crucial. In this work, four metrics are studied: Contrast Improvement Index (CII), Enhancement Measurement Estimation (EME), Entropy EME (EMEE), and Entropy. The objective is to find the best parameters for each metric. The study is performed on five medical image datasets, three retinal fundus sets (DRIVE, ROPFI, HRF-POORQ), and two mammography image sets (MIAS, DDSM). Metrics are calculated using a binary mask image to discard the background. Using the fundus and mask datasets, the best results were obtained with the EMEE and EMEE metrics, which achieved mean improvements of up to 186% and 75%, respectively. For mammography datasets and using masks of the region of interest, the two metrics with the highest percentage improvement were CII and EMEE, which obtained means of up to 396% and 129%, respectively. Based on the experimental results provided, we can conclude that EMEE, EME, and CII metrics can achieve better enhancement assessment in this type of medical imaging