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The interpretation of medical images is one of the most difficult tasks in computer vision, largely because of the high degree of variability associated with normal and abnormal appearances. This thesis introduces a systematic method for the detection of microcalcifications as one of the most important signs of early breast cancer. It involves a four step procedure. The first step is blob detection to detect regions of microcalcification size range. The second step involves a specially designed directional region growing method to find the best fitting boundaries for each blob region. A newly developed combination of classifiers is then applied to label each region as a microcalcification or background. The final processing step involves a search for the existence of clusters of microcalcifications using a hierarchical nearest mean clustering method. The contributions of the work to the field of image processing are; a new blob detection system; a novel region growing method and a theoretical framework for combining classifiers which use a combination of shared and distinct representations. Here specifically, we present a blob detection method with the capability of detecting any suspected blob of specific size range. Then a new region growing method is developed based on a unique directional growing process providing predictable behaviour for the method. The application of two discontinuity measures is considered for the extraction of two fitting boundaries representing information about the region and its local background. The information conveyed by the boundaries and their associated regions is used to compute reliable representations for labelling each blob region. The robustness of the region growing method to the choice of a starting point and to Gaussian noise is examined on real images. We demonstrate that commonly used classifiers provide reliable results in labelling the suspected regions. In spite of achieving an acceptable performance using different individual classifiers, a decision fusion rule involving a weighted combination of classifiers is developed and its performance on the problem is investigated. The combination rule is applicable when mixed mode representations (some shared and some individual features) are used. A comparative study of the individtial classifiers and also of conventional classifier combination techniques with the weighted combiner is performed on independent test sets. The results achieved with the presented algorithm are very promising and approaching a level where a clinical pilot evaluation for screening purposes would be warranted.

Medical images are extremely complicated to comprehend for a person without expertise. The limited number of practitioners across the globe often face the issue of fatigue due to the high number of cases. This fatigue, physical and mental, can induce human-errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision-maker. Thus, it becomes crucial to have a reliable Visual Question Answering (VQA) system which can provide a "second opinion" on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this thesis, we develop a deep learning-based model for VQA on medical images taking the associated challenges into account. Our MedFuseNet system aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and weaving everything together to predict the answer. We tackle two types of answer prediction - categorization and generation. We conduct an extensive set of both quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our results conclude that MedFuseNet outperforms other state-of-the-art methods available in the literature for these tasks. / Master of Science / Medical images are extremely complicated to comprehend for a person without expertise. The limited number of practitioners across the globe often face the issue of fatigue due to the high number of cases. This fatigue, physical and mental, can induce human-errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision-maker. Thus, it becomes crucial to have a reliable Visual Question Answering (VQA) system which can provide a "second opinion" on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. In this thesis, we propose an end-to-end deep learning-based system, MedFuseNet, for predicting the answer for the input query associated with the image. We cater to close-ended as well as open-ended type question-answer pairs. We conduct an extensive analysis to evaluate the performance of MedFuseNet. Our results conclude that MedFuseNet outperforms other state-of-the-art methods available in the literature for these tasks.

This dissertation thesis is focused on the optimization of bandwidth parameters for the transport of medical image data between medical devices and remote data storage. As real and fully functional structure, which will be analyzed in this work. It was selected system MeDiMed (Metropolitan Digital Imaging System in Medicine). The thesis examines the operation of the small health organizations and their modalities, which use this system for remote data archiving. Traffic analysis is then statistically processed. The thesis also deals with the analysis of increasing the security during accessing health system, and assesses its impact on transmitted data. The effect of setting the transmission parameters and the most widely used types of ciphers on the transfer speed is also compared.

We propose a novel method for generating synthetic X-rays from atypical inputs. This method creates approximate X-rays for use in non-diagnostic visualization problems where only generic cameras and sensors are available. Traditional methods are restricted to 3-D inputs such as meshes or Computed Tomography (CT) scans. We create custom synthetic X-ray datasets using a custom generator capable of creating RGB images, point cloud images, and 2-D pose images. We create a dataset using natural hand poses and train general-purpose Conditional Generative Adversarial Networks (CGANs) as well as our own novel network pix2xray. Our results show the successful plausibility of generating X-rays from point cloud and RGB images. We also demonstrate the superiority of our pix2xray approach, especially in the troublesome cases of occlusion due to overlapping or rotated anatomy. Overall, our work establishes a baseline that synthetic X-rays can be simulated using inputs such as RGB images and point cloud.

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No / Purpose Expertise in viewing medical images is thought to be due to the ability to process holistic image information. Eye care clinicians can inspect photographs of the retina to search for signs of disease. However, they commonly also view the eye in vivo using the restricted view of a slit lamp, which removes the potential benefits of holistic processing. We investigated how expert and novice clinicians inspect the fundus using these two methods. Methods Twenty clinicians (10 experienced, 10 novices) examined 64 photographs of human retinae. Each participant viewed half of the images as fundus photographs while having their eye position recorded. The other half were viewed via a simple slit lamp simulation, whereby a computer mouse was used to control the position of a viewing window that revealed the underlying fundus photograph. Results Experienced clinicians made decisions significantly faster than novices, with faster decision-making when viewing the fundus photograph compared to via the slit lamp simulation. The distribution of inspection was similar, although novices spent longer examining the optic nerve head than other regions. Experienced clinicians showed significantly earlier inspection of the optic nerve head when it was judged to be unhealthy. Conclusions Our results support the idea that experienced eyecare clinicians use holistic image information, if available, when inspecting the fundus. This was particularly prominent for the optic nerve head region, which was the region that novices spent most of their time examining. Holistic processing benefits were only present in experts’ free-viewing fundus photographs; the limited field of view from the slit lamp disrupts such global image benefits.

Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.

Nesse trabalho foi desenvolvido o método VBSeg, um método de segmentação semiautomático de corpos vertebrais, que utiliza superpixels para aumentar a eficiência de técnicas de segmentação de imagens já estabelecidas na literatura, sem perder qualidade do resultado final. Experimentos mostraram que o uso de superpixels melhorou o resultado da segmentação dos corpos vertebrais em até 18%, além de aumentar a eficiência desses métodos, deixando a execução dos algoritmos de segmentação pelo menos 38% mais rápida. Além disso, o método desenvolvido possui baixa dependência do nível de especialidade do usuário e apresentou resultados comparáveis ao método Watershed, um método bem estabelecido na área de segmentação de imagens. Contudo, o método VBSeg segmentou 100% dos corpos vertebrais das imagens analisadas, enquanto que o método Watershed deixou de segmentar 44% dos corpos. / This work presents the development of a semiautomatic vertebral body segmentation method VBSeg, which uses superpixels to increase effi- ciency of well established image segmentation methods without losing quality. Experiments have shown motivating results with superpixels im- proving vertebral bodies segmentation in 18% and making segmentation algorithms at least 38% faster. Furthermore, our VBSeg method has low dependency on the level of expertise and got similar results to Watershed method, a well-established image segmentation method. However, VB- Seg method was able to segment 100% of the analyzed vertebral bodies while Watershed method missed 44% of those.