bluuebunny/crossref_metadata_2025 huggingface dataset:
First Release of PLATO Consortium Stellar Limb-darkening Coefficients
Authors: Morello Giuseppe , Gerber Jeffrey , Plez Bertrand , Bergemann Maria , Cabrera Juan , Ludwig Hans-Günter , Morel Thierry
Published: November 2022

We release the first grid of stellar limb-darkening coefficients (LDCs) and intensity profiles (IPs) computed by the consortium of the PLAnetary Transits and Oscillations of stars (PLATO), the next medium-class (M3) mission under development by the European Space Agency to be launched in 2026. We have performed spectral synthesis with TurboSpectrum on a grid of MARCS model atmospheres. Finally, we adopted ExoTETHyS to convolve the high-resolution spectra (R = 2 × 105) with the state-of-the-art response functions for all the PLATO cameras, and computed the LDCs that best approximate the convolved IPs. In addition to the PLATO products, we provide new LDCs and IPs for the Kepler mission, based on the same grid of stellar atmospheric models and calculation procedures. The data can be downloaded from the following link: https://doi.org/10.5281/zenodo.7339706.
3D Radiative-transfer Simulations of the Ejecta Plume Anticipated from DART Impact
Authors: Kolokolova Ludmilla , Li 李 Jian-Yang 荐扬 , van Selous Mark , Farnham Tony , Nagdimunov Lev
Published: November 2022

We present a modeling approach that allows the reconstruction of detailed images of the ejecta plume produced by the DART impact on asteroid Dimorphos. The approach is based on the 3D radiative-transfer technique and uses the publicly available HYPERION software package. The modeling automatically includes all interactions of light with the asteroid and ejecta plume, including transmission of the light through a hollow cone, reflection from the asteroid, and second transmission through the ejecta for each point within the ejecta plume and asteroid surface. The results of the modeling are the FITS images of the asteroid and ejecta as they will be seen by the LEIA camera on board LICIACube for any specific moments after impact. The parameters of the modeling are preimpact assumptions regarding Dimorphos’ shape and composition, the presumed impact location, and the planned flyby trajectory of LICIACube. The results of the modeling show the readiness of our modeling tools to simulate the realistic ejecta plume after impact with the purpose to model the LICIACube images at different moments after impact and estimate the properties of the dust ejecta particles and the ejecta mass.
How Fundamental Conditioning Factors Affect the Quality of Life of Older Adults Indonesians
Authors: Nurhasanah Nurhasanah , Juanita Juanita , Febriana Dara , Jufrizal Jufrizal , Putra Ardia
Published: November 2022

Introduction: Older adults experience deterioration of body functions which hinders them in carrying out daily activities, thus affecting their quality of life. The quality of life of older adults also reflects their health and well-being status.
Objective: The purpose of this study was to determine the influence of fundamental conditioning factors on the quality of life of Indonesian older adults.
Materials and Methods: Data were analyzed using descriptive statistics. Simple logistic regression was also used to estimate the odds ratio of good health-related quality of life. Two research instruments, a demographic data form and the SF-36 Indonesian version were utilized for data collection.
Results: Findings showed that the total score of the quality of life was at an appropriate level. The logistic regression of occupation and illness duration revealed a relationship with the quality of life of the older adults (p < .05); however, age, gender, marital status, and morbidity did not affect the quality of life (p > .05).
Conclusion: Older adults with jobs are highly likely to improve their quality of life as they can carry out activities, have social interactions, and make ends meet. However, the length of illness suffered by older adults may affect their quality of life. It is expected that nursing intervention provided, such as teaching proper health behavior for disease management, may help prevent complications and enhance the quality of life of older adults.
Blood Disorders in Patients with Obstructive Jaundice: A Literature Review
Authors: Aimagambetov Meirbek Zh. , Orazgalieva Meruert T. , Omarov Nazarbek B. , Zhanybekov Serik D. , Orazalina Ainash S.
Published: November 2022

BACKGROUND: Mechanical jaundice is a pathological syndrome consisting in a violation of the outflow of hepatic bile through the bile ducts into the duodenum due to mechanical obstacles. The most common causes of mechanical jaundice are gallstone disease, malignant tumors, as well as cicatricial stricture of the bile duct or the large duodenal papilla of the duodenum. All this leads to the development of renal-hepatic insufficiency. Thrombohemorrhagic changes develop in the vascular bed, leading to the development of disseminated intravascular coagulation syndrome. Prevention and treatment of cholemic bleeding in case of mechanical jaundice remains one of the complex problems of hepatobiliary surgery. This article is an overview of the causes and pathophysiological changes affecting hemostasis in mechanical jaundice, as well as the main points of treatment of hemostasis disorders in patients with mechanical jaundice.

AIM: This study aims to study the literature on homeostasis in patients with mechanical jaundice.

SEARCH STRATEGY: To conduct a systematic search for scientific information and to achieve this goal, an analysis of scientific publications in evidence-based medicine databases (PubMed), using specialized search engines (Google Scholar) and in electronic scientific libraries (CyberLeninka, e-library) was carried out from 2005 to 2020.

INCLUSION CRITERIA: Research of high methodological quality: Meta-analysis, systematic review and cohort studies, as well as publications with clearly formulated and statistically proven conclusions in English, Russian, and Kazakh.

EXCLUSION CRITERIA: Summaries of reports, reports in the form of abstracts, and advertising articles.

RESULTS: The mechanisms that affect hemostasis in obstructive jaundice can be considered from four perspectives: The first relates to Vitamin K deficiency in obstructive jaundice, the second describes the effect of ongoing fibrosis and cirrhosis of the liver on hemostasis, the third analyzes the relationship between infectious-septic mechanisms and the hemostasis system, their clinical significance in patients with obstructive jaundice, and the latter involves the analysis of specific factors that manifest obstructive jaundice and may themselves affect the blood coagulation system.

CONCLUSION: Understanding the pathophysiology of hemostatic changes in patients with cholestasis and, more generally, liver disease is a clear way to accurate diagnosis and treatment. The combination of good knowledge with careful examination of each patient can lead to the most promising result.
Social Relevance and Post-stroke Epilepsy Prevention
Authors: Naydenov Christian , Parashkevova-Simeonova Boryana , Mancheva Velina
Published: November 2022

Abstract:  Stroke is an important cerebrovascular disease resulting in long-term disability and death. A rare consequence of stroke is structural epilepsy. Here we discuss the importance of post-stroke epilepsy prevention. We look into the occurrence of stroke and epilepsy in the general population, the risk factors and severity of those conditions and the significance of early seizure prevention after the stroke. We analyze published research papers and reviews dealing with this problem. In brief, there is a diagnostic problem leading to ineffective treatment. On one hand preventing seizures in patients with no history of epilepsy, treating them with medications with known side effects. On the other hand by not treating them, risk seizures and future repercussions for their health and wellbeing. Here we propose ways to select patients for treatment and refine the diagnostic considerations for early start of anti-seizure therapy.
Usage du craving dans la psychothérapie des addictions à l’alcool
Authors: Marchin Élodie , Gaudriault Pierre
Published: November 2022

Le craving , besoin impérieux de consommer de l’alcool, est l’objet d’une lutte répétée pour la plupart des personnes qui tentent de se soigner de leur addiction. Considérant l’addiction comme une solution qui efface simultanément toute représentation de la souffrance initiale, une écoute psychanalytique du craving  permettrait un travail de figurabilité psychique souvent nécessaire à la séparation de cette addiction. Cette forte envie de boire peut être considérée comme une trace psychique grâce à laquelle, au cours de la psychothérapie, les enjeux et la signification de ce recours au produit peuvent se dessiner.
L’usage du corps dans le contre-investissement des traumas à l’adolescence
Authors: Paoli Catherine
Published: November 2022

Partant du cas d’une adolescente de 17 ans qui se scarifie, l’auteure interroge la place et la fonction de la scarification à partir de la clinique de la parole et du transfert, dans l’après-coup du traumatisme. Dans cette histoire, la disparition prématurée de l’objet (maternel) du champ perceptif a empêché qu’il se constitue en objet interne. L’agir se substitue au fantasme qui le sous-tend. Cet usage spécifique du corps est ici porteur d’un potentiel réorganisateur et tente de figurer des angoisses primitives qui n’ont pas encore été intégrées.
La perception des innovations : une approche par les représentations sociales
Authors: Hetet Blandine
Published: November 2022

La perception des innovations représente un enjeu pour les entreprises car elle permet de comprendre leur adoption et la culture partagée par le groupe social étudié. Il est donc important de s’assurer que les consommateurs les perçoivent bien comme telles. Cette recherche utilise les représentations sociales pour analyser la perception des consommateurs de la génération Y et proposer un cadre de référence pour les entreprises. Les résultats de l’analyse par le biais de la théorie du noyau central mettent en évidence la prépondérance des notions de « nouveauté » et de « technologie et digital » dans la représentation et permettent de proposer des pistes de réflexion et des clés d’analyse pour les entreprises.
Commoning with blockchain. The Ğ1 libre currency/Duniter case
Authors: Malafosse Maxime , Pascal Amandine , Amabile Serge
Published: November 2022

L’essor d’Internet et des réseaux pair-à-pair ont favorisé la constitution de communautés autour de nouveaux projets collectifs qui remettent au premier plan les travaux sur les communs d’Ostrom (1990). Dans cette perspective, un nouveau champ de recherches s’intéresse au rôle de la technologie blockchain comme support de la gouvernance des communs. Ces recherches, encore peu nombreuses, sont essentiellement théoriques. Cet article se fixe ainsi comme objectif d’étudier l’utilisation concrète de cette technologie dans le processus de faire commun. Pour ce faire, cet article s’appuie sur l’étude du cas de la monnaie libre Ğ1. Nos résultats présentent en détail les modalités de gouvernance d’un nouveau type de commun développé par les membres de la monnaie libre Ğ1 : le dispositif socio-technique de création monétaire. Ce cas est intéressant car il permet de mettre en exergue les attributs de la blockchain Duniter spécifiquement développée pour les besoins de l’écosystème Ğ1. Il souligne également le rôle de cette blockchain pour soutenir l’auto-organisation du projet et, notamment, les faisceaux de droits que les membres ont mis en place afin d’allouer le dividende universel et le processus de faire commun.
Multi-source transfer learning with Graph Neural Network for excellent modelling the bioactivities of ligands targeting orphan G protein-coupled receptors
Authors: Huang Shizhen , Zheng ShaoDong , Chen Ruiqi
Published: November 2022

G protein-coupled receptors (GPCRs) have been the targets for more than 40% of the currently approved drugs. Although neural networks can effectively improve the accuracy of prediction with the biological activity, the result is undesirable in the limited orphan GPCRs (oGPCRs) datasets. To this end, we proposed Multi-source Transfer Learning with Graph Neural Network, called MSTL-GNN, to bridge this gap. Firstly, there are three ideal sources of data for transfer learning, oGPCRs, experimentally validated GPCRs, and invalidated GPCRs similar to the former one. Secondly, the SIMLEs format GPCRs convert to graphics, and they can be the input of Graph Neural Network (GNN) and ensemble learning for improving prediction accuracy. Finally, our experiments show that MSTL-GNN remarkably improves the prediction of GPCRs ligand activity value compared with previous studies. On average, the two evaluation indexes we adopted, R2 and Root-mean-square deviation (RMSE). Compared with the state-of-the-art work MSTL-GNN increased up to 67.13% and 17.22%, respectively. The effectiveness of MSTL-GNN in the field of GPCR Drug discovery with limited data also paves the way for other similar application scenarios.
Developing and Validating an Instrument to Measure Students’ Perceptions of the use of ICTs and Educational Technologies in Times of the COVID-19 Pandemic
Authors: Galarce-Miranda Claudia , Gormaz-Lobos Diego , Kersten Steffen , Hortsch Hanno
Published: November 2022

The main goals of the study were, on the one hand, to develop and validate an instrument to measure students' perceptions of the use of ICTs and educational technologies in times of the COVID-19 pandemic, and on the other hand, to know about the participants' attitudes towards the educational use of ICTs and other educational software. Some specific objectives of the research were (i) to identify the disposition of the students towards the use of ICTs, (ii) to characterize the Interaction with others through the use of ICTs, (iii) to identify the interaction of the students with LMS and ICTs, and to (iv) validate the instrument to measure students' perceptions about the use of ICTs and educational technologies, among others. An in-depth theoretical review allowed the authors of this paper to construct an instrument to measure students' perceptions about the use of ICTs and educational technologies in times of the COVID-19 pandemic. Concerning the statistical analysis, descriptive techniques were used to find out the perceptions of a group of Chilean university students about the online learning process through the use of ICTs, and then, the characteristics of the instrument itself were examined in-depth with exploratory factor analysis. Finally, in order to make the factor analysis more complex and to find proposals for improving the instruments for future research, the percentages of the variables grouped into factors were analyzed, as well as the reliability of each one of them. This research was financially supported by the German Academic Exchange Service (DAAD) as part of the project “Praxispartnerschaften zwischen Hochschulen und Unternehmen in Deutschland und in Entwicklungsländern ab 2017” (Project Nr. 57334905).
L’islam du milieu ou l’islam des puissants : des usages de la bonne religiosité à N’Djamena
Authors: Petitdemange Cécile
Published: November 2022

À travers le parcours de femmes musulmanes appartenant aux classes moyennes et bourgeoises de la société n’djamenoise, il s’agira de saisir les multiples réappropriations du marqueur islamique, utilisé comme stratégie de mobilité sociale et de reproduction de classe. La référence à l’islam perd alors substantiellement sa capacité à être un projet spécifiquement religieux pour devenir non pas une pratique totalisante mais plastique aux contextes et aux stratégies individuelles.
“Madame est au niveau 2, et moi…?” Gendered Dynamics of Qur’an Reading Courses in Dakarois Francophone Educated Middle-Class Milieus
Authors: Sieveking Nadine
Published: November 2022

This article focuses on the gendered dynamics of an adult religious education programme offered in Dakar since 2015. The programme is increasingly successful, especially among women, who constitute the majority of participants. It specifically targets a social group identified as “intellectuals,” and located within francophone educated middle-class milieus. Responding to the needs of this target group, the Qur’an reading courses not only reinforce the social status of the participants but also fulfil their desire for autonomy in the religious domain. Moreover, the secular environment of the courses allows for gendered interactions adhering to egalitarian norms that correspond to the habitus of the target group.
Religiosité musulmane en temps de Covid-19 au Burkina Faso et en Côte d’Ivoire : Un dialogue entre normes sanitaires et pratiques religieuses
Authors: Binaté Issouf , Gosselin Louis Audet , Soré Zakaria
Published: November 2022

La pandémie de Covid-19 a entraîné la mise en place de mesures exceptionnelles qui ont fortement transformé les pratiques religieuses à la fin du premier trimestre de 2020. Ces transformations, qui ont inclus la fermeture des lieux de culte puis l’adoption de mesures barrières sévères (distanciation physique et limitation du nombre de personnes dans les lieux publics), ont conduit à l’adoption de pratiques créatives de la part des autorités religieuses musulmanes au Burkina Faso et en Côte d’Ivoire. Cependant, ces pratiques ont été diversement reçues, les organisations regroupant les musulmans fonctionnaires et de l’élite politique et administrative adhérant plus strictement aux règles qui étaient fortement contestées par les musulmans de milieu commerçant ou populaire, en particulier les jeunes.
Un intellectuel soufi engagé face à un « élitisme salafiste ». Entretien avec Souleymane Bachir Diagne
Authors: Samson Fabienne
Published: November 2022

Souleymane Bachir Diagne, philosophe sénégalais enseignant à l’Université de Columbia à New York, revient dans cet entretien sur son parcours intellectuel de penseur de l’islam, inspiré par un enseignement religieux familial soufi et ouvert à la réflexion. Il explique ses divers engagements en tant qu’enseignant voulant transmettre l’importance du dialogue entre les diverses philosophies du monde, le pluralisme et la tolérance face aux idéologies de l’exclusion. Il livre également quelques réflexions personnelles sur les dynamiques contemporaines de l’islam, issues des mouvements afro-américains, d’intellectuels ouest-africains, ou encore de mouvements féministes diversifiés.
Les « Illuminati » du pamphlet au roman : circulations d’un discours complotiste à grande échelle depuis le tournant du xixe siècle
Authors: Chaudet Chloé
Published: November 2022

Cet article analyse les circulations textuelles internationales d’un discours complotiste emblématique de l’époque contemporaine : le complot des « Illuminati ». Après avoir dégagé les traits topiques, lexicaux, structurels et stylistiques de ce discours (géo)politique dans deux pamphlets fondateurs de la fin du xviiie siècle, il aborde ses reprises dans le roman à partir de trois périodes ultérieures de prolifération du complotisme.
Les discours complotistes sur Twitter à propos de la vaccination contre la Covid-19 en France : communautés et analyse sémio-linguistique des hashtags
Authors: Brachotte Gilles , Frame Alexander , Gautier Laurent , Nazarov Waldemar , Selmi Afef
Published: November 2022

Cet article étudie les mécanismes communicationnels et linguistiques permettant la circulation des discours à tendance complotistes sur Twitter à partir d’un corpus de plus de 55,5 millions de tweets, en langue française et évoquant la vaccination contre la Covid-19. Il identifie, à partir d’une méthode innovante de décomposition tensorielle, les « clusters » de comptes structurés au cours du temps autour de faits sociétaux et d’actualité. Des analyses quantitatives et qualitatives des tweets d’un cluster spécifique mettent en avant l’influence sur sa circulation des constituants d’un tweet, et du niveau constructionnel et pragma-sémantique des hashtags constitutifs du cluster.
Circular Economy at the Core of Levis & Co.'s Success
Authors: De Lucia Riccardo , Iavazzi Armando , Combatti Ilaria , Quarcoo Benedicta
Published: November 2022

The chapter aims to gather information and analyze the new business model developed by Levi Strauss and Co. First, it analyses the network that LS&Co. has set up and how the business works. Then, it will investigate the context in which LS&Co. operates by looking at values, drivers, stakeholders, barriers. Subsequently, a careful analysis of the performance indicators that LS&Co. has adopted including the GRIs material report will be conducted. The technologies and developments that LS&Co. has adopted in its business will be explained; particular attention will be given to emerging partners in the new business model and the value they bring. There will be space for an ambitious project based on blockchain technology as a tangible tool for their products and suppliers. Some recommendations on how LS&Co. could improve its circular economy in the context of the growing smart cities will be given in the last paragraph.
Acqua Minerale San Benedetto
Authors: Alarcon Louis , Arangiaro Valentina , Rabe Adrian Fernandez , Elekes Felicián , Gallersdörfer Juliane , Gramiller Katharina , Rotundo Gloria , Tölgyesi Kristóf
Published: November 2022

Sustainability is an extremely urgent and universal concern; however, becoming sustainable doesn't happen overnight. It is not an immediate transition, and in order to really take sustainability to the next level, experience and know-how must be upgraded over time. San Benedetto is the perfect example of a company that developed a strong commitment to the environment since its origins. It has embraced the circular economy paradigm since it has shifted from the traditional linear economic model, which is based on a take-make-consume-throw away pattern toward a closed loop business model in which waste becomes an input in the production process. Over the years, San Benedetto has reached important achievements and prizes as far as environmental protection effort, proving to the world its responsibility and its proactive attitude over the matter, that in turn led to increase customer loyalty and displace the competition.
Sustainable Fast Fashion
Authors: Pastore Giammarco , Frangiosa Alessandro , Loria Vincenzo , Bellomo Matteo , Di Mezza Paolo Bernardo , Falcone Maria Francesca , Romeo Maria , De Cazotte Lucie Marie
Published: November 2022

Sustainability is significantly important to the fashion business because of consumers' growing awareness of the environment. When a fashion company aims to promote sustainability, the main link is to develop a sustainable circular system. This chapter contributes understanding of how a historical fashion company has managed to evolve over time by implementing circular technology that can give it a competitive advantage in the market. The authors firstly describe the structure of H&M, the value it distributes in the market, and the future goals it has set for itself. Next, they introduce the methodology by which the company has managed to make its circular model impact on its performance, giving an overview of the relationship the company has with its stakeholders and consumers. Furthermore, based on secondary data and analysis, they learn how the Swedish fast fashion company has built its sustainable strength by developing eco-friendly materials, monitoring sustainable production, reducing carbon emissions in distribution, and promoting circular fashion.
The Fundamentals of Biomedical Image Processing
Authors: Bhatele Kirti Raj , Gupta Vivek , Gupta Kamlesh , Shrivastava Prashant
Published: November 2022

This chapter provides a brief introduction to the various fundamentals and concepts related to the basics of the biomedical image processing. Medical imaging processing comprises various techniques and processes that are used to create images of human body for clinical purposes and medical procedures for the purpose of diagnosis or examination of disease. Digital image processing along with its suitable components and computer-simulated algorithms are implemented using computers to perform the image analysis of digital images. The study of normal anatomy and physiology of human body is made as a part of diagnosis process. Though medical imaging of various organs and tissues can be performed for medical examination purposes, the impact of digital images on modern society is tremendous and image processing has become a critical component of science and technology related to the biomedical image processing.
An Adaptive Algorithm for Detection of Exudates Based on Localized Properties of Fundus Images
Authors: Chanda Katha , Issac Ashish , Dutta Malay Kishore
Published: November 2022

This article presents an algorithm to detect exudates, which can be considered as one of the many abnormalities, to identify diabetic retinopathy from fundus images. The algorithm is invariant to illumination and works well on poor contrast images with high reflection noise. The artefacts are correctly rejected despite their colour, intensity and contrast being almost similar to that of exudates. Optic disc is localized and segmented using average filter of specially determined size which is an important step in the rejection of false positives. Exudates are located by generating candidate regions using variance and median filters followed by morphological reconstruction. The strategic selection of local properties to decide the threshold, makes this approach novel and adaptive, that is highly accurate for detection of exudates. The proposed method was tested on two publicly available labelled databases (DIARETDB1 and MESSIDOR) and a database from a local hospital and achieved a sensitivity of 96.765% and a positive predictive value of 93.514%.
A Block-Based Arithmetic Entropy Encoding Scheme for Medical Images
Authors: Sharma Urvashi , Sood Meenakshi , Puthooran Emjee , Kumar Yugal
Published: November 2022

The digitization of human body, especially for treatment of diseases can generate a large volume of data. This generated medical data has a large resolution and bit depth. In the field of medical diagnosis, lossless compression techniques are widely adopted for the efficient archiving and transmission of medical images. This article presents an efficient coding solution based on a predictive coding technique. The proposed technique consists of Resolution Independent Gradient Edge Predictor16 (RIGED16) and Block Based Arithmetic Encoding (BAAE). The objective of this technique is to find universal threshold values for prediction and provide an optimum block size for encoding. The validity of the proposed technique is tested on some real images as well as standard images. The simulation results of the proposed technique are compared with some well-known and existing compression techniques. It is revealed that proposed technique gives a higher coding efficiency rate compared to other techniques.
Brain Tumor Detection Based on Multilevel 2D Histogram Image Segmentation Using DEWO Optimization Algorithm
Authors: Kumar Sumit , Vig Garima , Varshney Sapna , Bansal Priti
Published: November 2022

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.
Medical Image Lossy Compression With LSTM Networks
Authors: Nithin Prabhu G.  , Nagavi Trisiladevi C. , Mahesha P.
Published: November 2022

Medical images have a larger size when compared to normal images. There arises a problem in the storage as well as in the transmission of a large number of medical images. Hence, there exists a need for compressing these images to reduce the size as much as possible and also to maintain a better quality. The authors propose a method for lossy image compression of a set of medical images which is based on Recurrent Neural Network (RNN). So, the proposed method produces images of variable compression rates to maintain the quality aspect and to preserve some of the important contents present in these images.
Relevance of Machine Learning to Cardiovascular Imaging
Authors: Sasidharan Sumesh , Salmasi M. Yousuf , Pirola Selene , Jarral Omar A.
Published: November 2022

Artificial intelligence (AI) broadly concerns analytical algorithms that iteratively learn from big datasets, allowing computers to find concealed insights. These encompass a range of operations comprising several terms, including machine learning(ML), cognitive learning, deep learning, and reinforcement learning-based methods that can be used to incorporate and comprehend complex biomedical and healthcare data in scenarios where traditional statistical approaches cannot be implemented. For cardiovascular imaging in particular, machine learning guarantees to be a transformative tool that can address many unmet needs for patient-specific management, accurate prediction of disease progression, and the tracking of identifiable biomarkers of disease processes. In this chapter, the authors discuss fundamentals of machine learning algorithms for image analysis in the cardiovascular system by evaluating the need for ML in this field and examining the potential obstacles and challenges of implementation in the context of three common imaging modalities used in cardiovascular medicine.
Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique
Authors: Ziyad Shabana R. , Radha V.  , Vaiyapuri Thavavel
Published: November 2022

Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.
A Hybrid Approach for 3D Lung Segmentation in CT Images Using Active Contour and Morphological Operation
Authors: Sahu Satya Praksh , Kamble Bhawna
Published: November 2022

Lung segmentation is the initial step for detection and diagnosis for lung-related abnormalities and disease. In CAD system for lung cancer, this step traces the boundary for the pulmonary region from thorax in CT images. It decreases the overhead for a further step in CAD system by reducing the space for finding the ROIs. The major issue and challenging task for the segmentation is the inclusion of juxtapleural nodules in the segmented lungs. The chapter attempts 3D lung segmentation of CT images using active contour and morphological operations. The major steps in the proposed approach contain: preprocessing through various techniques, Otsu's thresholding for the binarizing the image; morphological operations are applied for elimination of undesired region and, finally, active contour for the segmentation of the lungs in 3D. For experiment, 10 subjects are taken from the public dataset of LIDC-IDRI. The proposed method achieved accuracies 0.979 Jaccard's similarity index value, 0.989 Dice similarity coefficient, and 0.073 volume overlap error when compared to ground truth.
M2UNet++
Authors: Kushnure Devidas Tulshiram , Talbar Sanjay Nilkanth
Published: November 2022

Liver segmentation is instrumental for decision making in the medical realm for the diagnosis and treatment planning of hepatic diseases. However, the manual segmentation of the hundreds of CT images is tedious for medical experts. Thus, it hampers the segmentation accuracy and is reliant on opinion of the operator. This chapter presents the deep learning-based modified multi-scale UNet++ (M2UNet++) approach for automatic liver segmentation. The multi-scale features were modified channel-wise using adaptive feature recalibration to improve the representation of the high-level semantic information of the skip pathways and improved the segmentation performance with fewer computational overheads. The experimental results proved the model's efficacy on the publicly available 3DIRCADb dataset, which offers significant complexity and variations. The model's dice coefficient value is 97.28% that is 7.64%, and 2.24% improved from the UNet and UNet++ model. The quantitative result analysis shows that the M2UNet++ model outperforms the state-of-the-art methods proposed for liver segmentation.
Building an IPFS and Blockchain-Based Decentralized Storage Model for Medical Imaging
Authors: Kumar Randhir , Tripathi Rakesh
Published: November 2022

Currently, sharing and access of medical imaging is a significant element of present healthcare systems, but the existing infrastructure of medical image sharing depends on third-party approval. In this chapter, the authors have proposed a framework in order to provide a decentralized storage model for medical image sharing through IPFS and blockchain technology that remove the hurdle of third-party dependency. In the proposed model, the authors are sharing the imaging and communications in medicine (DICOM) medical images, which consist of various information related to disease, and hence, the framework can be utilized in the real-time application of the healthcare system. Moreover, the framework maintains the feature of immutability, privacy, and availability of information owing to the blockchain-based decentralized storage model. Furthermore, the authors have also discussed how the information can be accessed by the peers in the blockchain network with the help of consensus. To implement the framework, they have used the python ask and anaconda python.
Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion
Authors: Khairuzzaman Abdul Kayom Md , Chaudhury Saurabh
Published: November 2022

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.
Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction
Authors: Kavya N  , Sriraam N  , Usha N  , Hiremath Bharathi , Suresh Anusha , Sharath D  , Venkatraman B  , Menaka M
Published: November 2022

Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.
Detection and Classification of Leukocytes in Blood Smear Images
Authors: Tali Renuka Veerappa , Borra Surekha , Mahmud Mufti
Published: November 2022

Manual analysis of microscopic blood smears by highly expert pathologists is labor-intensive, time-consuming, and is subject to inter-observer variations. Recent innovations in image processing and computer vision techniques have improvised digital pathology in terms of objectivity and reproducibility. Traditional computer vision-based methods of recognition of white blood cell (WBC) from a pathological blood smear image includes the process of detection, segmentation, and classification. This paper presents a review of state-of-the-art detection, segmentation, and classification techniques for white blood cell analysis. The goal of this work is to present an introduction to the field, provide enough information about the analysis methods developed so far, and to be an appropriate reference for the researchers looking forward in this field. The methods under review are classified into intensity and feature based. The crucial steps involved in these techniques, mathematical foresights, performance evaluation techniques, issues, and future directions are discussed.
Digital Image Analysis for Early Diagnosis of Cancer
Authors: Majumder Durjoy , Das Madhumita
Published: November 2022

Cancer diagnoses so far are based on pathologists' criteria. Hence, they are based on qualitative assessment. Histopathological images of cancer biopsy samples are now available in digital format. Such digital images are now gaining importance. To avoid individual pathologists' qualitative assessment, digital images are processed further through use of computational algorithm. To extract characteristic features from the digital images in quantitative terms, different techniques of mathematical morphology are in use. Recently several other statistical and machine learning techniques have developed to classify histopathological images with the pathologists' criteria. Here, the authors discuss some characteristic features of image processing techniques along with the different advanced analytical methods used in oncology. Relevant background information of these techniques are also elaborated and the recent applications of different image processing techniques for the early detection of cancer are also discussed.
Towards an Effective Imaging-Based Decision Support System for Skin Cancer
Authors: Vardasca Ricardo , Magalhaes Carolina
Published: November 2022

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct applications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suffers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.
Computer-Aided Diagnosis of Knee Osteoarthritis From Radiographic Images Using Random Forest Classifier
Authors: Pavithra D.  , Vanithamani R.  , Justin Judith
Published: November 2022

Knee osteoarthritis (OA) is a degenerative joint disease that occurs due to wear down of cartilage. Early diagnosis has a pivotal role in providing effective treatment and in attenuating further effects. This chapter aims to grade the severity of knee OA into three classes, namely absence of OA, mild OA, and severe OA, from radiographic images. Pre-processing steps include CLAHE and anisotropic diffusion for contrast enhancement and noise reduction, respectively. Niblack thresholding algorithm is used to segment the cartilage region. GLCM features like contrast, correlation, energy, homogeneity, and cartilage features such as area, medial, and lateral thickness are extracted from the segmented region. These features are fed to random forest classifier to assess the severity of OA. Performance of random forest classifier is compared with ANFIS and Naïve Bayes classifier. The classifiers are trained with 120 images and tested with 45 images. Experimental results show that random forest classifier achieves a higher accuracy of 88.8% compared to ANFIS and Naïve Bayes classifier.
Deep Convolutional Neural Networks in Detecting Lung Mass From Chest X-Ray Images
Authors: Mohan Arun Prasad
Published: November 2022

There are more than one million cases of lung cancer per year in India alone. Early detection is vital in increasing the survival rate and decreasing treatment costs. This research is aimed at building a deep convolutional neural network which uses chest x-rays to identify lung mass, and then make a comparative study by tuning the hyperparameters. NIH Chest X-Ray Dataset containing more than 112,000 images were used for training and testing. The data was analysed and then fed to the neural network. Accuracy of over 96% was obtained in all the trials. A comparative study by varying the number of inputs and varying the number of hidden layers was carried out. The accuracies obtained were compared and was found that the accuracy increased with the increase in the number of hidden layers. A complete product was then ideated which when implemented would be a vital diagnostic tool and can be used in the remote locations of a country having just x-ray facilities and no other advanced medical equipment like CT.
CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis
Authors: Adegun Adekanmi Adeyinka , Ogundokun Roseline Oluwaseun , Adebiyi Marion Olubunmi , Asani Emmanuel Oluwatobi
Published: November 2022

Machine learning techniques such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor-related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyzes and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in the diagnosis and monitoring of some diseases. Diseases such as skin cancer, age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis, and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images.
The Added Value of 3D Imaging and 3D Printing in Head and Neck Surgeries
Authors: Parioti Evgenia , Pitoglou Stavros , Filntisi Arianna , Anastasiou Athanasios , Petropoulou Ourania , Koutsouris Dimitris Dionisios
Published: November 2022

3D imaging and 3D printing are two methods that have been proven very useful in medicine. The objective of 3D medical imaging is to recreate the static and functional anatomy of the inner body. The development of computational systems for image processing and multidimensional monitoring of medical data is important for diagnosis and treatment planning. The technique of 3D printing has enabled the materialization of anatomical models and surgical splints using medical imaging data. The methods of 3D imaging and 3D printing have been utilized in various medical fields such as neuroimaging, neurosurgery, dentistry, otolaryngology and facial plastic surgery. This review aims to evaluate the use of 3D imaging and 3D printing techniques in head and neck surgery and concludes that these technologies have revolutionized medicine. However, improvements in healthcare systems and further research still have to be made to establish their use in everyday medical practices.
Central serous chorioretinopathy
Authors: Park Jong Beom , Kim Kiyoung , Kang Min Seok , Kim Eung Suk , Yu Seung-Young
Published: November 2022

Central serous chorioretinopathy (CSC) is a pachychoroid spectrum disease characterized by serous detachment of the neurosensory retina with subretinal fluid in young and middle-aged adults. The pathogenesis of CSC is not yet fully understood. However, it is considered a multifactorial disease that is strongly associated with choroidal dysfunction or vascular engorgement. Although there is no consensus on the treatment of CSC, photodynamic therapy has been effectively used to manage serous retinal detachment (SRD) in CSC. Moreover, micropulse diode laser photocoagulation and focal laser treatment have also been used. Recently, oral medications, including mineralocorticoid receptor antagonists, have been proposed for the management of CSC. Multimodal imaging plays a significant role in the diagnosis and treatment of CSC. Optical coherence tomography angiography (OCTA) has the advantage of detecting vascular flow in the retina and choroid layer, allowing for a better understanding of the pathology, severity, prognosis, and chronicity of CSC. In addition, early detection of choroidal neovascularization in CSC is possible using OCTA. This review article aims to provide a comprehensive and updated understanding of CSC, focusing on treatment.