Una metodología basada en espiral aplicada al análisis de células en una imagen

The advances in technology, microscopy and computing have allowed the development of new fields in cell image analysis. However, the usability of these platforms is adequate to expert users only. Many software tools are oriented to expert users in image processing, likewise the use of bioinformatics require a basic knowledge in programming. The development of research in cell imaging requires the joint work of computer Scientifics and biologist. In this paper we present a methodology to develop a software solution applied to the analysis of cell images.Los avances en tecnología, microscopía y computación han permitido el desarrollo de nuevos campos en el análisis de imágenes celulares. Sin embargo, la usabilidad de estas plataformas es adecuada solo para usuarios expertos. Muchas herramientas software están orientadas a usuarios expertos en el procesamiento de imágenes y así mismo el uso de herramientas bioinformáticas requiere un conocimiento básico en programación. El desarrollo de investigaciones en imágenes celulares requiere el trabajo conjunto de biólogos y de expertos en computación. En este artículo se presenta una metodología para desarrollar una solución de software aplicada al análisis de imágenes celulares

Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks

The recent advancements in the Internet of Things (IoT) and Wireless Multimedia Sensor Networks (WMSN) made high-speed multimedia streaming, data processing, and essential analytics processes with minimal delay. Multimedia sensors used in WMSN-based surveillance applications are beneficial helpful in attaining accurate and elaborate details. However, it has become essential to design an effective and lightweight solution for data traffic management in WMSN owing to the massive quantities of data, generated by multimedia sensors. The development of Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to investigate, collect, store, and process multimedia streaming data for decision-making in real-time scenarios. In this aspect, the current study develops an Integrated AI technique for Video Surveillance in IoT-enabled WMSN, called IAIVS-WMSN. The proposed IAIVS-WMSN technique aims to design a practical scheme for object detection and data transmission in WMSN. The proposed IAIVS-WMSN approach encompasses three stages: object detection, image compression, and clustering. The Mask Regional Convolutional Neural Network (Mask RCNN) technique is primarily utilized for object detection in the target region. Besides, Neighbourhood Correlation Sequence-based Image Compression (NCSIC) technique is applied to reduce data transmission. Finally, Artificial Flora Algorithm (AFA)-based clustering technique is designed for the election of Cluster Heads (CHs) and construction clusters. The design of object detection with compression and clustering techniques for WMSN shows the novelty of the work. These three processes’ designs enable one to accomplish effective data transmission in IoT-enabled WMSN. The researchers conducted multiple simulations to highlight the supreme performance of the IAIVS-WMSN approach. The simulation outcomes inferred the enhanced performance of the IAIVS-WMSN algorithm to the existing approaches

Revisión de técnicas de Deep Learning y Machine Learning para la detección y localización de micro aneurismas, exudados y hemorragias en imágenes de fondo de ojo

The loss of vision is one of the most unfortunate cases of loss of the senses; it is no secret that not being able to see considerably diminishes the quality of life of a person regardless of his or her age range. According to the WHO, vision impairment negatively impacts young children and school children as it greatly affects their developmental stage; as for adults and older adults, vision impairment is reflected in the rates of participation in the labor market and productivity of this population is usually lower. One of the causes of vision loss is diabetic retinopathy, an eye disease resulting from diabetes mellitus for a prolonged period that causes partial or total vision loss. This document is a compilation and analysis of different studies taken from various specialized databases and taking into account that they are classified between the quartiles Q1 and Q2 and that their impact factor is above 2.5 for their selection, these around the detection and localization of the three most important anomalies that are present in people suffering from DR, These are microaneurysms, exudates and hemorrhages, through fundus images of patients who suffer or are prone to diabetic retinopathy using algorithms with deep learning and machine techniques and guided by metrics such as accuracy, sensitivity and specificity to measure the efficiency of the developed algorithm.La pérdida de la visión ha sido uno de los casos más desafortunados de pérdida de los sentidos. Es sabido que la perdida de la visión disminuye considerablemente la calidad de vida de una persona, sin importar la edad. De acuerdo con la OMS, el deterioro de la visión impacta negativamente a niños pequeños en etapa escolar, dado que afecta en gran medida su etapa de desarrollo; en cuanto a los adultos, el deterioro de la visión se ve reflejado a partir de las tasas de participación en el mercado laboral y de productividad de esta población, no obstante, suele ser más baja. Una de las causas de la pérdida de la visión es la retinopatía diabética, que consiste en una enfermedad ocular producto de la diabetes mellitus por un prolongado periodo de tiempo que causa la pérdida parcial o total de la visión. En ese orden de ideas, el objetivo de la presente investigación consiste en recopilar y analizar los diferentes estudios sobre el tema en cuestión; tal información fue tomada de diversas bases de datos especializadas, teniendo en cuenta una clasificación de los cuartiles Q1 y Q2, y cuyo factor de impacto estuvo por encima 2,5 para su selección. Estos en torno a la detección y localización de las tres más importantes anomalías que hacen presencia en personas que sufren de RD, estas son micro aneurismas, exudados y hemorragias. Para ello se necesitaron imágenes de fondo de ojo de pacientes que padecen o están propensos a padecer retinopatía diabética, haciendo uso de algoritmos con técnicas de deep learning y machine. Dichos algoritmos estuvieron guiados por métricas tales como exactitud, sensibilidad y especificad con el fin de medir la eficiencia de algoritmo desarrollado

ESTUDIO DE COBERTURA DE RADIOFRECUENCIA Y POTENCIA PARA EL REDISEÑO DEL SISTEMA DE TRANSMISIÓN: UN CASO ESTUDIO EN LA EMISORA MARINA STEREO 90.7 FM EN LA CIUDAD DE BARRANQUILLA

El presente artículo describe el estudio e implementación de una mejora realizada a la estación de radio Marina Stereo 90,7 FM, la cual presenta fallas en su potencia de operación del sistema de transmisión, generando baja cobertura en la potencia de recepción, por tal motivo el objetivo de la mejora implementada es el rediseño del sistema de transmisión por medio de herramientas de diagnóstico, medición y validación de datos en campo, análisis de estos y toma de decisiones para determinar las innovaciones necesarias en materia de infraestructura para hacer más optima la señal transmitida y por consecuente, hacer que el sistema de radio en materia de cobertura en potencia tenga mayor alcance y no se vea afectado por factores como irregularidades del terreno, hacia el cual se proyecta la señal, desarrollo urbanístico de edificios y vivienda vertical

Image Segmentation Methods for Automatic Detection of the Anatomical Structure of the Eye in People with Diabetic Retinopathy

Aquesta tesi s'emmarca dins del pla integral de prevenció precoç de la Retinopatia Diabètica (RD) posat en marxa pel govern espanyol seguint les recomanacions de l'Organització Mundial de la Salut de promoure iniciatives que consciencien sobre la importància de fer revisions oculars regulars entre les persones amb diabetis. Per tal de poder determinar el nivell de retinopatia diabètica cal localitzar i identificar diferents tipus de lesions a la retina. Per poder fer-ho, cal que primer s'eliminin de la imatge les estructures anatòmiques normals de l'ull (vasos sanguinis, disc òptic i fòvea) a fi de fer més visibles les anomalies. Aquesta tesi s'ha centrat en aquest pas de neteja de la imatge. En primer lloc, aquesta tesi proposa un nou marc per a la segmentació ràpida i automàtica del disc òptic basat en la Teoria del Portafoli de Markowitz. En base a aquesta teoria es proposa un model innovador de fusió de colors capaç d'admetre qualsevol metodologia de segmentació en el camp de la imatge mèdica. Aquest enfoc s'estructura com una etapa de pre-processament potent i en temps real que es podria integrar-se a la pràctica clínica diària, permetent accelerar el diagnòstic de la DR a causa de la seva simplicitat, rendiment i velocitat. La segona contribució d'aquesta tesi és un mètode per fer simultàniament una segmentació dels vasos sanguinis i la detecció de la zona avascular foveal, reduint considerablement el temps de processament d'imatges. A més a més, el primer component de l'espai de color xyY (que representa els valors de crominància) és el que predomina en l'estudi dels diferents components de color desenvolupat en aquesta tesi, centrat en la segmentació dels vasos sanguinis i la detecció de fòvea. Finalment, es proposa una recopilació automàtica de mostres per fer una interpolació estadística del color i que són utilitzades en l'algorisme de segmentació de Convexity Shape Prior. La tesi també proposa un altre mètode de segmentació dels vasos sanguinis que es basa en una selecció de característiques efectiva basada arbres de decisions. S'ha aconseguit trobar les 5 característiques més rellevants per segmentar aquestes estructures oculars. La validació mitjançant tres tècniques de classificació diferents (arbres de decisions, xarxes neuronals i màquines de suport vectorial).Esta tesis se enmarca dentro del plan integral de prevención contra la Retinopatía Diabética (RD), ejecutado por el Gobierno de España alineado a las políticas de la Organización Mundial de la Salud para promover iniciativas que conciencien a la población con diabetes sobre la importancia de exámenes oculares de manera periódica. Para poder determinar el nivel de retinopatía diabética hace falta localizar e identificar diferentes tipos de lesiones en la retina. Para conseguirlo primero se han de eliminar de la imagen las estructures anatómicas normales del ojo (vasos sanguíneos, disco óptico y fóvea) para hacer visibles las anomalías. Esta tesis se ha centrado en este paso de limpieza de la imagen. En primer lugar, esta tesis propone un novedoso enfoque para la segmentación rápida y automática del disco óptico basado en la Teoría de Portafolio de Markowitz. En base a esta teoría se propone un innovador modelo de fusión de color capaz de soportar cualquier metodología de segmentación en el campo de las imágenes médicas. Este enfoque se estructura como una etapa de preprocesamiento potente y en tiempo real que podría integrarse en la práctica clínica diaria para acelerar el diagnóstico de RD debido a su simplicidad, rendimiento y velocidad. La segunda contribución de esta tesis es un método para segmentar simultáneamente los vasos sanguíneos y detectar la zona avascular foveal, reduciendo considerablemente el tiempo de procesamiento para tal tarea. Adicionalmente, la primera componente del espacio de color xyY (que representa los valores de crominancia) es la que predomina del estudio de las diferentes componentes de color realizado en esta tesis para la segmentación de vasos sanguíneos y la detección de la fóvea. Finalmente, se propone una recolección automática de muestras para interpolarlas basadas en la información estadística de color y que a su vez son la base del algoritmo Convexity Shape Prior. La tesis también propone otro método de segmentación de vasos sanguíneos basado en una selección efectiva de características soportada en árboles de decisión. Se ha conseguido encontrar las 5 características más relevantes para la segmentación de estas estructuras oculares. La validación utilizando tres técnicas de clasificación (árbol de decisión, red neuronal artificial y máquina de soporte vectorial).This thesis is framed within the comprehensive plan for early prevention of Diabetic Retinopathy (DR) launched by the Spain government following the World Health Organization to promote initiatives that raise awareness of the importance of regular eye exams among people with diabetes. To determine the level of diabetic retinopathy, we need to find and identify different types of lesions in the eye fundus. First, the normal anatomic structures of the eye (blood vessels, optic disc and fovea) must be removed from the image, in order to make visible the abnormalities. This thesis has focused on this step of image cleaning. This thesis proposes a novel framework for fast and fully automatic optic disc segmentation based on Markowitz's Modern Portfolio Theory to generate an innovative color fusion model capable of admitting any segmentation methodology in the medical imaging field. This approach acts as a powerful and real-time pre-processing stage that could be integrated into daily clinical practice to accelerate the diagnosis of DR due to its simplicity, performance, and speed. This thesis's second contribution is a method to simultaneously make a blood vessel segmentation and foveal avascular zone detection, considerably reducing the required image processing time. In addition, the first component of the xyY color space representing the chrominance values is the most supported according to the approach developed in this thesis for blood vessel segmentation and fovea detection. Finally, several samples are collected for a color interpolation procedure based on statistic color information and are used by the well-known Convexity Shape Prior segmentation algorithm. The thesis also proposes another blood vessel segmentation method that relies on an effective feature selection based on decision tree learning. This method is validated using three different classification techniques (i.e., Decision Tree, Artificial Neural Network, and Support Vector Machine)

Optimal synergic deep learning for COVID-19 classification using chest x-ray images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%

Experimental air conditioning energy evaluation under Caribbean climate conditions

A 23 factorial design was performed to analyze the performance of a mini-split air conditioning system under several psychrometric air conditions at the evaporator inlet, similar to Tropical Caribbean region conditions. In addition, a search for new energy-saving opportunities was performed. The results showed that interactions between the temperature of the air inlet, the humidity of the air inlet, and the fan speed level are significant in the mini-split energy performance under Caribbean climate conditions. Hence, working on an oriented energy savings control strategy is necessary. Therefore, this study recommends developing a fan speed control scheme, generating energy savings of around 10% in the air conditioning unit

Renal pathology images segmentation based on improved cuckoo search with diffusion mechanism and adaptive beta-hill climbing

Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images

Multi-threshold image segmentation based on an improved differential evolution: case study of thyroid papillary carcinoma

The scholarly world has demonstrated an immense enthusiasm for medical image segmentation due to its intricate nature and critical role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, due to its simplicity and straightforwardness. This paper presents an improved Differential Evolution (DE) algorithm called AGDE, which is based on MTIS and was used to evaluate its high capability at IEEE CEC 2017. Comparisons with classical and advanced algorithms were conducted as part of the experiments. An AGDE-based multi-threshold image segmentation method utilizing a non-local mean 2D histogram in combination with Rényi's entropy was applied to segment images from the Berkeley Segmentation Datasets 500 (BSDS500) and microscopic images of thyroid papillary carcinoma (TPC). The experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation

Privacy preserving blockchain with energy aware clustering scheme for iot healthcare systems

Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usually record the patient data and transfer it to the cloud for further processing. Energy efficiency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an effective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artificial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efficient data transmission in IoT networks. The IoT devices are primarily used to collect patients’ medical data. The AIBS- IoTH model involves a metaheuristic-based modified sunflower optimization-based clustering (MSFOC) technique to achieve energy efficiency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra-cluster communication. At last, the Classification Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods