Automatic algorithm for determining bone and soft-tissue factors in dual-energy subtraction chest radiography

Lung cancer is currently the first leading cause of worldwide cancer deaths since the early stage of lung cancer detection is still a challenge. In lung diagnosis, nodules sometimes overlap with ribs and tissues on lung chest radiographic images, which are complex for doctors and radiologists. Dual-energy subtraction (DES) is a suitable solution to solve those issues. This article will develop an efficient iterative DES for lung chest radiographic images. Moreover, we propose an automatic algorithm for accurately determining bone and soft-tissue factors for subtraction. The proposed algorithm for determining the bone and soft-tissue factors is based on window/level ratio and radiographic histogram analysis. First, we take the image sampling from the original size 3072 × 3072 to 512 × 512 to reduce the processing time while achieving the bone and soft-tissue factors. Next, we compute the window/level ratio on the soft-tissue image. Finally, we determine the minimum value of the ratio to obtain the optimal soft-tissue and bone factors. Our experimental results show that our proposed algorithm achieves a minimized runtime of 200 ms, outperforming the GE algorithm’s time of 4 s. The runtime of our DES of 6.066 s is shorter than the Fujifilm algorithm of 10 s while visualizing nodules on soft-tissue images and obtaining a similar quality of the soft-tissue images compared with the other algorithms. The academic contributions include the proposed algorithm for determining bone and soft-tissue factors and the optimized iterative DES algorithm to minimize time and dose consumption

Computer-aided diagnosis of tuberculosis in paediatric chest X-rays using local textural analysis

Includes abstract.Includes bibliographical references (leaves 99-103).This report presents a computerised tool to analyse the appearance of the lung fields in paediatric chest X-rays to detect the presence of tuberculosis. The computer aided diagnosis (CAD) tool consists of 4 phases: 1) lung field segmentation; 2) lung field subdivision; 3) feature extraction and 4) classification. Lung field segmentation is performed using a semi-automatic implementation of the active shape model algorithm. Two approaches to subdividing the lung fields into regions of interest are compared. The first divides each lung field into 21 overlapping regions of varying sizes, resulting in a total of 42 regions per image; this approach is called the big region approach. The second approach divides the lung fields into a large number of overlapping circular regions of interest. The circular regions have a radius of 32 pixels and are placed on an 8 x 8 pixel grid. This approach is called the circular region approach. Textural features are extracted from each of the regions using the moments of responses to a multiscale bank of Gaussian filters. Additional positional features are added to the circular regions

Нейронна мережа для виявлення відхилень грудної клітини на рентгенівських знімках

Магістерська дисертація за темою «Система діагностики тромбоемболії легеневої артерії за даними комп‘ютерної томографії органів грудної клітини» виконана студентом кафедри біомедичної кібернетики ФБМІ Нерусом Владиславом Миколаєвичом зі спеціальності 122 «Комп’ютерні науки» за освітньо-професійною програмою «Комп’ютерні технології в біології та медицині», та складається зі: вступу; 4 розділів (літературний огляд, теоретична частина, практична частина, аналіз стартап-проекту), висновків до кожного з цих розділів; загальних висновків; списку використаних джерел, який налічує 68 джерел. Загальний обсяг роботи 105 сторінок. Актуальність теми. Респіраторні захворювання, такі як пневмонія, є поширеним станом легеневої інфекції, а хвороба COVID-19 стала небезпечною для життя хворобою, яка з’явилася наприкінці 2019 року та вразила весь світ. Пневмонія — це смертельна інфекція нижніх дихальних шляхів, яка відноситься до категорії гострих захворювань і, як повідомляється, є основною причиною смерті в усьому світі. У 2017 році на нього припадало 15% дитячих смертей, які трапилися протягом року [34] . Крім того, літні люди мають високий ризик захворіти на пневмонію, що призводить до критичних станів. Однак, якщо діагностувати та лікувати на ранній стадії, пов’язаний ризик можна мінімізувати [34]. Тому, система для автоматичного діагностування хвороб грудної клітини може бути використана як рекомендувальний інсрумент рентгенолога задля зменшення помилок та людських помилок. Мета і завдання дослідження. Метою роботи є розробка та навчання нейронної мережі для класифікації хвороб грудної клітини за рентгеном. Її досягнення передбачає вирішення наступних завдань: 1. Огляд літератури із обраної тематики. 2. Аналіз існуючих нейронних мереж. 3. Аугментація даних. 4. Нормалізація вибірки даних. 5. Розробка архітектури нейронної мережі. 6. Реалізація чи імпорт нейронної мережі та її тренування. Об’єкт дослідження. Рентгенівський знімок грудної клітини. Предмет дослідження. Згорткові нейронні мережі в задачах мульти-класифікації рентгенівських зображень, аугментація зображень. Методи дослідження. Методи розширення даних, методи боротьби з дисбалансом класів, операції згортки, пулінгу, згорткові нейронні мережі, оптимізація нейронних мереж.Master's thesis on " A neural network for detecting the chest abnormalities on X-ray images" is executed by the student of the department of biomedical cybernetics (Faculty of Biomedical Engineering) Nerus Vladyslav Nikovaevich in the specialty 122 "Computer science" on the educational and professional program "Computer and technology" consists of: introduction; 4 sections (literature review, theoretical part, practical part, analysis of a startup project), conclusions to each of these sections; general conclusions; a list of used sources, which includes 69 sources and applications. The total volume of the work is 105 pages. Relevance of the topic. Respiratory diseases such as pneumonia is a common lung infection condition and COVID-19 disease has become a life-threatening disease that emerged in later 2019 and has been impacted the entire world. Pneumonia is a fatal lower respiratory infection under the acute diseases category and has been reported to be a major cause of deaths around the world. In 2017, it was accountable for 15% of child deaths that happened during the year[34]. In addition, older people have a high risk of getting pneumonia that leads to critical conditions. However, if diagnosed and treated early, the associated risk can be minimized [34]. Therefore, the system for automatic diagnosis of chest diseases can be used as a recommended tool of the radiologist to reduce mistakes and human errors. Objective of the study. The purpose of the work is the development and training of a neural network for the classification of chest diseases based on X-rays. Its achievement involves solving the following tasks: 1. Review of the literature on the selected topic. 2. Analysis of existing neural networks. 3. Augmentation of data. 4. Normalization of ata sampling. 5. Development of neural network architecture. 6. Implementation or import of a neural network and its training. Object of study. X-ray images Subject of study. Convolutional neural networks in multi-classification of X-ray images. Research methods. Methods of data expansion, methods of combating class imbalance, convolution, pooling operations, convolutional neural networks, optimization of neural networks

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Methotrexate and bone formation and turnover in rheumatoid arthritis

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN025345 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Apport de l'imagerie par résonance magnétique dans la détermination de l'âge chez le sujet vivant

La détermination de l'âge est une problématique importante en anthropologie médico-légale. Chez le sujet vivant, elle peut être demandée dans certaines démarches juridiques ou administratives. L'objectif de ce travail était d'étudier l'apport de l'Imagerie par Résonance Magnétique (IRM) dans la détermination de l'âge chez l'individu vivant. Dans la première partie, les principales méthodes d'estimation de l'âge osseux sont rappelées. Puis, nous avons étudié des IRM de cheville et de pied à partir d'une classification en trois stades de la fusion métaphyso-épiphysaire de l'extrémité distale du tibia et du calcanéum. Enfin, nous avons développé une méthode automatisée de lecture des images d'IRM de l'extrémité distale du tibia. Cette méthode était basée sur les variations de niveaux de gris au sein de la jonction métaphyso-épiphysaire, après une standardisation des images pour corriger les inhomogénéités d'intensité. Les résultats de notre étude suggèrent que le développement de méthodes automatisées de lecture des images IRM peut représenter une nouvelle thématique de recherche dans le domaine de la détermination de l'âge chez le sujet vivant.Age estimation of living individuals has become an integral part of forensic practice. It is proceeded for people involved in criminal or asylum proceedings. The aim of this work was to study the contribution of Magnetic Resonance Imaging (MRI) in determining age of living subjects. In the first part of our work, the main age estimation methods are exposed. Then, we developed a MRI staging system for epiphyseal fusion of growth plate maturation of the distal tibial epiphysis and the calcaneum, and evaluated its reliability. Lastly, we proposed an automatic method based on the analysis of variations of grey levels within the epiphyseal-metaphyseal junction, after homogenisation of MR scans to correct artifactual intensity variation. Our results suggest that automated classification of MR scans could represent a new area of research in the fied of age estimation in living individuals

An investigation into the effects of commencing haemodialysis in the critically ill

<b>Introduction:</b> We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3 hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared to subsequent ones. <b>Methods:</b> Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology (HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable, then the nature of instability was examined by recording whether changes crossed defined physiological ranges. The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful, or beneficial/potentially beneficial. <b>Results:</b> Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%. Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes. This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisher’s exact test gives a result of p=0.68, reinforcing the lack of significant variance. <b>Conclusions:</b> Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are beneficial in nature