Interpretation Of X-ray Images Research Articles

An X-ray image-based Pruned Dense Convolution Neural Network for Tuberculosis Detection

According to the Ministry of Health in Kenya, tuberculosis (TB) is the fifth greatest cause of death and the main infectious disease killer in Kenya and across the world. In Kenya and throughout Africa, TB continues to wreak havoc on many vulnerable populations, homes, and communities despite being preventable and treatable. Common TB diagnostics, like blood and skin tests, frequently fail to identify the precise kind of TB. As a result, the World Health Organization (WHO) advises expanding the use of X-rays, for screening. In TB-prevalent regions of Kenya, a shortage of radiologists hampers effective screening and diagnosis, highlighting the need for scalable solutions for accurate X-ray analysis.Recent advancements in deep learning techniques have shown promise in the healthcare sector, particularly in radiology. However, many deep convolutional neural network (CNN) architectures are computationally intensive due to their size and resource requirements. This study designed and developed a Pruned CNN to address this issue by applying pruning techniques to baseline architectures. This approach significantly reduced model sizes while maintaining accuracy levels. Specifically, the pruned version of the DenseNet model achieved an impressive 99% accuracy with a reduction rate of 65.8%. These results highlight the potential of this pruned CNN as an effective and efficient tool for TB detection, particularly in resource-constrained environments. This study addresses the shortage of radiological expertise in many regions by providing a tool that can assist in the interpretation of X-ray images. This capability can help healthcare providers deliver timely and accurate diagnoses, thereby improving patient care.

The perceptions and willingness of radiographers to extend their roles and become reporting radiographers in Abu Dhabi, the United Arab Emirates (UAE)

IntroductionThere has been an increase in the need for diagnostic imaging services globally, which is faster than the capacity of radiologists. The current workforce model is not sustainable due to the predicted aging of the population and the contraction of the healthcare workforce. By extending the role of radiographers to a specific range of radiological reporting tasks. It may be possible to meet demand and relieve some pressure on radiologists and increase radiographer satisfaction and retention. The purpose of this study is to investigate the perceptions, and willingness of the Abu Dhabi radiographers to extend their role as reporting radiographers in skeleton radiography. MethodsA cross-sectional study was conducted among radiographers in the UAE. The on-line questionnaire was designed to obtain demographic information about radiographers, their qualifications, their professional experience, their postgraduate training, their plans for professional development, as well as their interest in future career opportunities related to extended roles. ResultsThis study had a response rate of 50% (n = 60). The government sector accounted for 75.3% of the survey's participants, while the private sector accounted for 24.7%. The main gains for role advancement were identified by 58.3% of the participants who were confident to start interpreting the upper and lower extremities in the emergency room (ER), and 86.3% were willing to be reporting radiographers. ConclusionIn conclusion, the Abu Dhabi radiographers are willing to extend their role in the interpretation of general X-ray images of skeletons. Implications for practiceCurriculum development in radiography and medical imaging education jointly with licensing health authority bodies is required to plan for the new radiographer reporting role.

Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm

Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we use data augmentation to improve the model performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public dataset. The experimental results show that our model has reached the state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50 of our model is 0.638, which is significantly higher than the 0.634 and 0.636 of the improved YOLOv7 and original YOLOv8 models. To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application “Fracture Detection Using YOLOv8 App” to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery.

Detection of Pneumonia Using Chest X-Ray Images with Deep Learning Techniques-Review

Pneumonia is a prevalent respiratory infection that requires timely and accurate detection for effective treatment and improved patient outcomes. Traditional methods of pneumonia diagnosis, such as manual interpretation of chest X-ray images, are subjective and time-consuming. This research paper examines the utilization of deep learning techniques for the detection of pneumonia using chest X-ray images. The study delves into the challenges encountered within this domain, including the scarcity of annotated datasets, class imbalance, interpretability of model predictions, generalization, and integration into clinical practice. Various methodologies and solutions are discussed to mitigate these challenges and enhance the performance of deep learning models. The literature review encompasses investigations on CNN-based frameworks, transfer learning, dataset creation, and interpretability techniques. The paper underscores the significance of data preprocessing approaches, such as image resizing, normalization, and augmentation. In summary, this research paper provides valuable insights into the potential of deep learning in pneumonia detection and establishes a basis for further advancements in this field.

Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.

SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.

Bone age recognition based on mask R-CNN using xception regression model.

Background and Objective: Bone age detection plays an important role in medical care, sports, judicial expertise and other fields. Traditional bone age identification and detection is according to manual interpretation of X-ray images of hand bone by doctors. This method is subjective and requires experience, and has certain errors. Computer-aided detection can effectually enhance the validity of medical diagnosis, especially with the fast development of machine learning and neural network, the method of bone age recognition using machine learning has gradually become the focus of research, which has the advantages of simple data pretreatment, good robustness and high recognition accuracy. Methods: In this paper, the hand bone segmentation network based on Mask R-CNN was proposed to segment the hand bone area, and the segmented hand bone region was directly input into the regression network for bone age evaluation. The regression network is using an enhancd network Xception of InceptionV3. After the output of Xception, the convolutional block attention module is connected to refine the feature mapping from channel and space to obtain more effective features. Results: According to the experimental results, the hand bone segmentation network model based on Mask R-CNN can segment the hand bone region and eliminate the interference of redundant background information. The average Dice coefficient on the verification set is 0.976. The mean absolute error of predicting bone age on our data set was only 4.97 months, which exceeded the accuracy of most other bone age assessment methods. Conclusion: Experiments show that the accuracy of bone age assessment can be enhancd by using the Mask R-CNN-based hand bone segmentation network and the Xception bone age regression network to form a model, which can be well applied to actual clinical bone age assessment.

An improvement of the CNN-XGboost model for pneumonia disease classification.

X-ray images are viewed as a vital component in emergency diagnosis. They are often used by deep learning applications for disease prediction, especially for thoracic pathologies. Pneumonia, a fatal thoracic disease induced by bacteria or viruses, generates a pleural effusion where fluids are accumulated inside lungs, leading to breathing difficulty. The utilization of X-ray imaging for pneumonia detection offers several advantages over other modalities such as computed tomography scans or magnetic resonance imaging. X-rays provide a cost-effective and easily accessible method for screening and diagnosing pneumonia, allowing for quicker assessment and timely intervention. However, interpretation of chest X-ray images depends on the radiologist's competency. Within this study, we aim to suggest new elements leading to good interpretation of chest X-ray images for pneumonia detection, especially for distinguishing between viral and bacterial pneumonia. We proposed an interpretation model based on convolutional neural networks (CNNs) and extreme gradient boosting (XGboost) for pneumonia classification. The experimental study is processed through various scenarios, using Python as a programming language and a public database obtained from Guangzhou Women and Children's Medical Centre. The results demonstrate an acceptable accuracy of 87% within a mere 7 seconds, thereby endorsing its effectiveness compared to similar existing works. Our study provides a model based on CNN and XGboost to classify images of viral and bacterial pneumonia. The work is a challenging task due to the lack of appropriate data. The experimental process allows a better accuracy of 87%, a specificity of 89%, and a sensitivity of 85%.

Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.

A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding. In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images. The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846. Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work.

Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data

X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence (e.g., deep learning) have been proposed as solutions. However, typically, such solutions are overconfident when subjected to new data far from the training data, so-called out-of-distribution (OOD) data; we claim that safe automatic interpretation of industrial X-ray images, as part of quality control of critical products, requires a robust confidence estimation with respect to OOD data. We explored if such a confidence estimation, an OOD detector, can be achieved by explicit modeling of the training data distribution, and the accepted images. For this, we derived an autoencoder model trained unsupervised on a public dataset with X-ray images of metal fusion welds and synthetic data. We explicitly demonstrate the dangers with a conventional supervised learning-based approach and compare it to the OOD detector. We achieve true positive rates of around 90% at false positive rates of around 0.1% on samples similar to the training data and correctly detect some example OOD data.

Ultrasound in the Diagnosis of Pediatric Distal Radius Fractures: Does it Really Change the Treatment Policy? An Orthopedic View.

Distal radius fractures are the most common pediatric fractures, increasing in number in recent decades. Although simple bi-planar radiographs are sufficient for diagnosis, wrist ultrasonography has been popularized in recent years for fracture detection, mostly because of the concern about children's radiation exposure. Despite its availability and diagnostic accuracy, ultrasound has not gained widespread acceptance and popularity among orthopedic surgeons. We asked about the reasons for its lack of acceptance as a diagnostic tool by orthopedic surgeons, and its failure to be incorporated into diagnostic algorithms. We reviewed the latest articles concerning the use of ultrasound in the diagnosis of pediatric distal radius fracture. Data extraction was performed from each study with a focus on the following items: the specialty field of the authors, number of patients, number of fractures, mean age of the patients, and the gold standard method of diagnosis. Nine studies concerning the diagnostic accuracy of ultrasound in detecting distal radius fractures in children were included in the review. The most common field of practice of the authors was emergency medicine. Only two studies had an orthopedic surgeon among their authors. All studies employed X-ray imaging as the gold standard method. All studies were designed as prospective trials without randomization of patients. Generally, there was no independent blinded reviewer for the interpretation of ultrasound and X-ray images. Most studies were completed by emergency medicine physicians, without involving an orthopedic surgeon. Ultrasound evaluation was undertaken primarily by emergency medicine physicians with little experience. These studies were not randomized controlled trials, and knowledge of the history and clinical presentation of the subjects could have led to information bias. The relatively low number of included patients and lack of follow-up examinations were other limitations. As a result, we believe that ultrasound has not proven to be a suitable substitute for conventional X-ray imaging in the detection of pediatric distal radius fractures. We propose X-ray evaluation as the clinical gold standard method for pediatric wrist fractures.

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Background and objectivesThe traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease. MethodsThis study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT). ResultsOn chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image. ConclusionsCompared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.

On the stability of the difference analogue of the boundary value problem for a mixed type equation

This paper considers a difference problem for a mixed-type equation, to which a problem of integral geometry for a family of curves satisfying certain regularity conditions is reduced. These problems are related to numerous applications, including interpretation problem of seismic data, problem of interpretation of Xray images, problems of computed tomography and technical diagnostics. The study of difference analogues of integral geometry problems has specific difficulties associated with the fact that for finite-difference analogues of partial derivatives, basic relations are performed with a certain shift in the discrete variable. In this regard, many relations obtained in a continuous formulation, when transitioned to a discrete analogue, have a more complex and cumbersome form, which requires additional studies of the resulting terms with a shift. Another important feature of the integral geometry problem is the absence of a theorem for existence of a solution in general case. Consequently, the paper uses the concept of correctness according to A.N.Tikhonov, particularly, it is assumed that there is a solution to the problem of integral geometry and its differential-difference analogue. The stability estimate of the difference analogue of the boundary value problem for a mixed-type equation obtained in this work is vital for understanding the effectiveness of numerical methods for solving problems of geotomography, medical tomography, flaw detection, etc. It also has a great practical significance in solving multidimensional inverse problems of acoustics, seismic exploration.

Puzzle Based Learning in Undergraduate Studies

All undergraduate Radiography students require training in image interpretation and evaluation of x-ray images in their second year of studies as part of work integrated learning. The method of teaching pedagogy influences the student's learning process and recall ability during examinations. if the teaching process moves to a student-centred approach, students become responsible for their own learning allowing active engagement and construction of their knowledge systems. Aim/ Objectives The aim of the study is to implement and evaluate the use of puzzle-based learning in the teaching and learning process of undergraduate studies Objectives To determine the efficacy of crossword and jigsaw puzzles as a novel teaching tool for medical imaging education To increase student's interest and involvement with image interpretation topics To improve and assess recognition and recall of medical terminology To improve the understanding of innovative learning Methods The study is a cross sectional qualitative research design. Approval will be obtained from the Research and Ethics Committee of health Sciences. Online consent will be obtained from students involved, by means of Google Form submission, followed by an information session on Blackboard collaborate on the topic "Image evaluation and interpretation of radiographic imaging". Conclusion The research will prove the important collaboration of active teaching methodologies with simple, easy to use didactic material to improve student's understanding of basic concepts in their core module subject

COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.

Efficient ensemble for image-based identification of Pneumonia utilizing deep CNN and SGD with warm restarts

Childhood pneumonia, the leading cause of children mortality globally, is most commonly diagnosed based on the radiographic data, which requires radiologic interpretation of X-ray images. With recent advancements in the field of deep learning, the convolutional neural networks (CNN) have proven to be able to achieve great performance in medical image segmentation, analysis and classification tasks. However, developing and training methods utilizing CNN is still a complex and time-consuming process with several open issues — generalization, demand for large datasets, and high time complexity. The optimization objective in the training of CNN models has multiple minima, which do not necessarily generalize well and may result in poor performance. Ensemble methods are commonly employed to address the generalization issue, but they require a group of diverse models and are generally even more time-consuming. To address the issues of generalization, dataset size and time complexity, we developed an ensemble method based on stochastic gradient descent with warm restarts (SGDRE) that exploits the generalization capabilities of ensemble methods and SGD with warm restarts mechanism, which is adopted to obtain a diverse group of classifiers, necessary for ensemble, in one single training process, spending the same or less training time than a single CNN classification model. The SGDRE method has been trained on publicly available pediatric chest X-ray images dataset and evaluated using 10-fold cross-validation approach. The experimental results show a significant improvement of SGDRE over the two compared baseline methods. With an achieved test accuracy of 96.26% and AUC of 95.15%, the proposed method proved to be a very competitive classification method.

ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images

In medical imaging, segmentation plays a vital role towards the interpretation of X-ray images where salient features are extracted with the help of image segmentation. Without undergoing surgery, clinicians employ various modalities ranging from X-rays and CT-Scans to ultrasonography, and other imaging techniques to visualise and examine interior human body organ and structures. To ensure appropriate convergence, training a deep convolutional neural network (CNN) from scratch is tough since it requires more computational time, a big amount of labelled training data and a considerable degree of experience. Fine-tuning a CNN that has been pre-trained using, for instance, a huge set of labelled medical datasets, is a viable alternative. In this paper, a comparative study was done using pre-trained models such as VGG-19 and ResNet-50 as against training from scratch. To reduce overfitting, data augmentation and dropout regularization was used. With a recall of 92.03%, our analysis showed that the pre-trained models with proper finetuning was comparable with Iyke-Net, a CNN trained from scratch.

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images.

Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1-5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

Radiographers’ perspectives’ on Visual Grading Analysis as a scientific method to evaluate image quality

IntroductionRadiographers routinely undertake many initiatives to balance image quality with radiation dose (optimisation). For optimisation studies to be successful image quality needs to be carefully evaluated. Purpose was to 1) discuss the strengths and limitations of a Visual Grading Analysis (VGA) method for image quality evaluation and 2) to outline the method from a radiographer's perspective. MethodsA possible method for investigating and discussing the relationship between radiographic image quality parameters and the interpretation and perception of X-ray images is the VGA method. VGA has a number of advantages such as being low cost and a detailed image quality assessment, although it is limited to ensure the images convey the relevant clinical information and relate the task based radiography. ResultsComparing the experience of using VGA and Receiver Operating Characteristic (ROC) it is obviously that less papers are published on VGA (Pubmed n=1.384) compared to ROC (Pubmed n=122.686). Hereby the scientific experience of the VGA method is limited compared to the use of ROC. VGA is, however, a much newer method and it is slowly gaining more and more attention. ConclusionThe success of VGA requires a number of steps to be completed, such as defining the VGA criteria, choosing the VGA method (absolute or relative), including observers, finding the best image display platforms, training observers and selecting the best statistical method for the study purpose should be thoroughly considered. Implication for practiceDetailed evaluation of image quality for optimisation studies related to technical definition of image quality.

Interpretation of X-Ray Images to Investigate the Viability of Incorporating Poly(Ethylene-co-Vinyl Acetate) (EVA) Waste in Portland Cement

EVA (poly (ethylene-co-vinyl acetate)) is a copolymer widely used in the Brazilian footwear industry, where approximately 18% of the EVA remains as waste, principally after the cutting out of insoles. EVA wastes cannot be recycled or reused for the original purpose, but it may be possible to add them to Portland cement to produce concrete for non-structural applications in constructions. This work presents an analytical technique based on the interpretation of X-ray images to assess the spatial distributions of these wastes within concrete test specimens. Evaluation was made of the feasibility of using this technique to study the incorporation of the wastes. Test specimens of concrete were produced according to Brazilian technical standard ABNT-NBR 5738, using a sand/gravel/cement ratio of 3:2:1, where EVA replaced part of the gravel content (10, 20, 50, and 70% of the total gravel volume). Analysis of front projection X-ray images of the specimens showed that the waste was homogeneously incorporated throughout the entire material, as required for concrete. The results of compressive strength tests showed that for samples containing up to 20% of EVA waste, the compression resistance remained almost unchanged, while incorporation of 50 or 70% of waste led to decreases of up to 64% in the resistance.

Automated Analysis of Orthopaedic X-ray Images based on Digital-Geometric Techniques

This thesis reports several methods for automated analysis and interpretation of bone X-ray images. Automatic segmentation of the bone part in a digital X-ray image is a challenging problem because of its low contrast against the surrounding flesh. In this thesis, we propose a fully automated X-ray image segmentation technique, which is based on a variant of entropy measure of the image. We have also analyzed the geometric information embedded in the long-bone contour image to identify the presence of abnormalities in the bone and perform fracture detection, fracture classification, and bone cancer diagnosis.