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Pattern recognition systems design on parallel GPU architectures for breast lesions characterisation employing multimodality imagesSidiropoulos, Konstantinos January 2014 (has links)
The aim of this research was to address the computational complexity in designing multimodality Computer-Aided Diagnosis (CAD) systems for characterising breast lesions, by harnessing the general purpose computational potential of consumer-level Graphics Processing Units (GPUs) through parallel programming methods. The complexity in designing such systems lies on the increased dimensionality of the problem, due to the multiple imaging modalities involved, on the inherent complexity of optimal design methods for securing high precision, and on assessing the performance of the design prior to deployment in a clinical environment, employing unbiased system evaluation methods. For the purposes of this research, a Pattern Recognition (PR)-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA’s GPU-cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the Probabilistic Neural Network classifier and its performance was evaluated by a re-substitution method, for estimating the system’s highest accuracy, and by the external cross validation method, for assessing the PR-system’s unbiased accuracy to new, “unseen” by the system, data. Data comprised images of patients with histologically verified (benign or malignant) breast lesions, who underwent both ultrasound (US) and digital mammography (DM). Lesions were outlined on the images by an experienced radiologist, and textural features were calculated. Regarding breast lesion classification, the accuracies for discriminating malignant from benign lesions were, 85.5% using US-features alone, 82.3% employing DM-features alone, and 93.5% combining US and DM features. Mean accuracy to new “unseen” data for the combined US and DM features was 81%. Those classification accuracies were about 10% higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster. In addition, benign lesions were found smoother, more homogeneous, and containing larger structures. Additionally, the PR-system design was adapted for tackling other medical problems, as a proof of its generalisation. These included classification of rare brain tumours, (achieving 78.6% for overall accuracy (OA) and 73.8% for estimated generalisation accuracy (GA), and accelerating system design 267 times), discrimination of patients with micro-ischemic and multiple sclerosis lesions (90.2% OA and 80% GA with 32-fold design acceleration), classification of normal and pathological knee cartilages (93.2% OA and 89% GA with 257-fold design acceleration), and separation of low from high grade laryngeal cancer cases (93.2% OA and 89% GA, with 130-fold design acceleration). The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.
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Methods and models for 2D and 3D image analysis in microscopy, in particular for the study of muscle cells / Metoder och modeller för två- och tredimensionell bildanalys inom mikroskopi, speciellt med inrikting mot muskelcellerKarlsson Edlund, Patrick January 2008 (has links)
<p>Many research questions in biological research lead to numerous microscope images that need to be evaluated. Here digital image cytometry, i.e., quantitative, automated or semi-automated analysis of the images is an important rapidly growing discipline. This thesis presents contributions to that field. The work has been carried out in close cooperation with biomedical research partners, successfully solving real world problems.</p><p>The world is 3D and modern imaging methods such as confocal microscopy provide 3D images. Hence, a large part of the work has dealt with the development of new and improved methods for quantitative analysis of 3D images, in particular fluorescently labeled skeletal muscle cells.</p><p>A geometrical model for robust segmentation of skeletal muscle fibers was developed. Images of the multinucleated muscle cells were pre-processed using a novel spatially modulated transform, producing images with reduced complexity and facilitating easy nuclei segmentation. Fibers from several mammalian species were modeled and features were computed based on cell nuclei positions. Features such as myonuclear domain size and nearest neighbor distance, were shown to correlate with body mass, and femur length. Human muscle fibers from young and old males, and females, were related to fiber type and extracted features, where myonuclear domain size variations were shown to increase with age irrespectively of fiber type and gender.</p><p>A segmentation method for severely clustered point-like signals was developed and applied to images of fluorescent probes, quantifying the amount and location of mitochondrial DNA within cells. A synthetic cell model was developed, to provide a controllable golden standard for performance evaluation of both expert manual and fully automated segmentations. The proposed method matches the correctness achieved by manual quantification. </p><p>An interactive segmentation procedure was successfully applied to treated testicle sections of boar, showing how a common industrial plastic softener significantly affects testosterone concentrations.</p>
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Development of advanced 3D medical analysis tools for clinical training, diagnosis and treatmentSkounakis, Emmanouil D. January 2013 (has links)
The objective of this PhD research was the development of novel 3D interactive medical platforms for medical image analysis, simulation and visualisation, with a focus on oncology images to support clinicians in managing the increasing amount of data provided by several medical image modalities. DoctorEye and Automatic Tumour Detector platforms were developed through constant interaction and feedback from expert clinicians, integrating a number of innovations in algorithms and methods, concerning image handling, segmentation, annotation, visualisation and plug-in technologies. DoctorEye is already being used in a related tumour modelling EC project (ContraCancrum) and offers several robust algorithms and tools for fast annotation, 3D visualisation and measurements to assist the clinician in better understanding the pathology of the brain area and define the treatment. It is free to use upon request and offers a user friendly environment for clinicians as it simplifies the implementation of complex algorithms and methods. It integrates a sophisticated, simple-to-use plug-in technology allowing researchers to add algorithms and methods (e.g. tumour growth and simulation algorithms for improving therapy planning) and interactively check the results. Apart from diagnostic and research purposes, it supports clinical training as it allows an expert clinician to evaluate a clinical delineation by different clinical users. The Automatic Tumour Detector focuses on abdominal images, which are more complex than those of the brain. It supports full automatic 3D detection of kidney pathology in real-time as well as 3D advanced visualisation and measurements. This is achieved through an innovative method implementing Templates. They contain rules and parameters for the Automatic Recognition Framework defined interactively by engineers based on clinicians’ 3D Golden Standard models. The Templates enable the automatic detection of kidneys and their possible abnormalities (tumours, stones and cysts). The system also supports the transmission of these Templates to another expert for a second opinion. Future versions of the proposed platforms could integrate even more sophisticated algorithms and tools and offer fully computer-aided identification of a variety of other organs and their dysfunctions.
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Automatic measurements of femoral characteristics using 3D ultrasound images in uteroYaqub, Mohammad January 2011 (has links)
Vitamin D is very important for endochondral ossification and it is commonly insufficient during pregnancy (Javaid et al., 2006). Insufficiency of vitamin D during pregnancy predicts bone mass and hence predicts adult osteoporosis (Javaid et al., 2006). The relationship between maternal vitamin D and manually measured fetal biometry has been studied (Mahon et al., 2009). However, manual fetal biometry especially volumetric measurements are subjective, time-consuming and possibly irreproducible. Computerised measurements can overcome or at least reduce such problems. This thesis concerns the development and evaluation of novel methods to do this. This thesis makes three contributions. Firstly, we have developed a novel technique based on the Random Forests (RF) classifier to segment and measure several fetal femoral characteristics from 3D ultrasound volumes automatically. We propose a feature selection step in the training stage to eliminate irrelevant features and utilise the "good" ones. We also develop a weighted voting mechanism to weight tree probabilistic decisions in the RF classifier. We show that the new RF classifier is more accurate than the classic method (Yaqub et al., 2010b, Yaqub et al., 2011b). We achieved 83% segmentation precision using the proposed technique compared to manually segmented volumes. The proposed segmentation technique was also validated on segmenting adult brain structures in MR images and it showed excellent accuracy. The second contribution is a wavelet-based image fusion technique to enhance the quality of the fetal femur and to compensate for missing information in one volume due to signal attenuation and acoustic shadowing. We show that using image fusion to increase the image quality of ultrasound images of bony structures leads to a more accurate and reproducible assessment and measurement qualitatively and quantitatively (Yaqub et al., 2010a, Yaqub et al., 2011a). The third contribution concerns the analysis of data from a cohort study of 450 fetal femoral ultrasound volumes (18-21 week gestation). The femur length, cross-sectional areas, volume, splaying indices and angles were automatically measured using the RF method. The relationship between these measurements and the fetal gestational age and maternal vitamin D was investigated. Segmentation of a fetal femur is fast (2.3s/volume), thanks to the parallel implementation. The femur volume, length, splaying index were found to significantly correlate with fetal gestational age. Furthermore, significant correlations between the automatic measurements and 10 nmol increment in maternal 25OHD during second trimester were found.
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Segmentation and sizing of breast cancer masses with ultrasound elasticity imagingvon Lavante, Etienne January 2009 (has links)
Uncertainty in the sizing of breast cancer masses is a major issue in breast screening programs, as there is a tendency to severely underestimate the sizing of malignant masses, especially with ultrasound imaging as part of the standard triple assessment. Due to this issue about 20% of all surgically treated women have to undergo a second resection, therefore the aim of this thesis is to address this issue by developing novel image analysis methods. Ultrasound elasticity imaging has been proven to have a better ability to differentiate soft tissues compared to standard B-mode. Thus a novel segmentation algorithm is presented, employing elasticity imaging to improve the sizing of malignant breast masses in ultrasound. The main contributions of this work are the introduction of a novel filtering technique to significantly improve the quality of the B-mode image, the development of a segmentation algorithm and their application to an ongoing clinical trial. Due to the limitations of the employed ultrasound device, the development of a method to improve the contrast and signal to noise ratio of B-mode images was required. Thus, an autoregressive model based filter on the radio-frequency signal is presented which is able to reduce the misclassification error on a phantom by up to 90% compared to the employed device, achieving similar results to a state-of-the art ultrasound system. By combining the output of this filter with elasticity data into a region based segmentation framework, a computationally highly efficient segmentation algorithm using Graph-cuts is presented. This method is shown to successfully and reliably segment objects on which previous highly cited methods have failed. Employing this method on 18 cases from a clinical trial, it is shown that the mean absolute error is reduced by 2 mm, and the bias of the B-Mode sizing to underestimate the size was overcome. Furthermore, the ability to detect widespread DCIS is demonstrated.
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Respiratory motion correction in positron emission tomographyBai, Wenjia January 2010 (has links)
In this thesis, we develop a motion correction method to overcome the degradation of image quality introduced by respiratory motion in positron emission tomography (PET), so that diagnostic performance for lung cancer can be improved. Lung cancer is currently the most common cause of cancer death both in the UK and in the world. PET/CT, which is a combination of PET and CT, providing clinicians with both functional and anatomical information, is routinely used as a non-invasive imaging technique to diagnose and stage lung cancer. However, since a PET scan normally takes 15-30 minutes, respiration is inevitable in data acquisition. As a result, thoracic PET images are substantially degraded by respiratory motion, not only by being blurred, but also by being inaccurately attenuation corrected due to the mismatch between PET and CT. If these challenges are not addressed, the diagnosis of lung cancer may be misled. The main contribution of this thesis is to propose a novel process for respiratory motion correction, in which non-attenuation corrected PET images (PET-NAC) are registered to a reference position for motion correction and then multiplied by a voxel-wise attenuation correction factor (ACF) image for attenuation correction. The ACF image is derived from a CT image which matches the reference position, so that no attenuation correction artefacts would occur. In experiments, the motion corrected PET images show significant improvements over the uncorrected images, which represent the acquisitions typical of current clinical practice. The enhanced image quality means that our method has the potential to improve diagnostic performance for lung cancer. We also develop an automatic lesion detection method based on motion corrected images. A small lung lesion is only 2 or 3 voxels in diameter and of marginal contrast. It could easily be missed by human observers. Our method aims to provide radiologists with a map of potential lesions for decision so that diagnostic efficiency can be improved. It utilises both PET and CT images. The CT image provides a lung mask, to which lesion detection is confined, whereas the PET image provides distribution of glucose metabolism, according to which lung lesions are detected. Experimental results show that respiratory motion correction significantly increases the success of lesion detection, especially for small lesions, and most of the lung lesions can be detected by our method. The method can serve as a useful computer-aided image analysing tool to help radiologists read images and find malignant lung lesions. Finally, we explore the possibility of incorporating temporal information into respiratory motion correction. Conventionally, respiratory gated PET images are individually registered to the reference position. Temporal continuity across the respiratory period is not considered. We propose a spatio-temporal registration algorithm, which models temporally smooth deformation in order to improve the registration performance. However, we discover that the improvement introduced by temporal information is relatively small at the cost of a much longer computation time. Spatial registration with regularisation yields similar results but is superior in speed. Therefore, it is preferable for respiratory motion correction.
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Automated Measurement of Midline Shift in Brain CT Images and its Application in Computer-Aided Medical Decision MakingWenan, Chen 03 March 2010 (has links)
The severity of traumatic brain injury (TBI) is known to be characterized by the shift of the middle line in brain as the ventricular system often changes in size and position, depending on the location of the original injury. In this thesis, the focus is given to processing of the CT (Computer Tomography) brain images to automatically calculate midline shift in pathological cases and use it to predict Intracranial Pressure (ICP). The midline shift measurement can be divided into three steps. First the ideal midline of the brain, i.e., the midline before injury, is found via a hierarchical search based on skull symmetry and tissue features. Second, the ventricular system is segmented from the brain CT slices. Third, the actual midline is estimated from the deformed ventricles by shape matching method. The horizontal shift in the ventricles is then calculated based on the ideal midline and the actual midline in TBI CT images. The proposed method presents accurate detection of the ideal midline using anatomical features in the skull, accurate segmentation of ventricles for actual midline estimation using the information of anatomical features with a spatial template derived from a magnetic resonance imaging (MRI) scan, and an accurate estimation of the actual midline based on the robust proposed multiple regions shape matching algorithm. After the midline shift is successively measured, features including midline shift, texture information of CT images, as well as other demographic information are used to predict ICP. Machine learning algorithms are used to model the relation between the ICP and the extracted features. By using systematic feature selection and parameter selection of the learning model, promising results on ICP prediction are achieved. The prediction results also indicate the reliability of the proposed midline shift estimation.
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A reliable method of tractography analysis : of DTI-data from anatomically and clinically difficult groupsBlomstedt, Johanna January 2019 (has links)
MRI is used to produce images of tissue in the body. DTI, specifically, makes it possible to track the effects of nerves where they are in the brain. This project includes a shell script and a guide for using the FMRIB Software Library, followed by StarTrack and then Trackvis in order to track difficult areas in the brain. The focus is on the trigeminal nerve (CN V). The method can be used to compare nerves in the same patient, or as a comparison to a healthy brain.
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Identification du système d'acquisition d'images médicales à partir d'analyse du bruit / Identification of the Acquisition System in Medical Images by Noise AnalysisKharboutly, Anas, Mustapha 13 September 2016 (has links)
Le traitement d’images médicales a pour but d’aider les médecins dans leur diagnostic et d’améliorer l’interprétation des résultats. Les scanners tomo-densitométriques (scanners X) sont des outils d’imagerie médicale utilisés pour reconstruire des images 3D du corps humain.De nos jours, il est très important de sécuriser les images médicales lors de leur transmission, leur stockage, leur visualisation ou de leur partage entre spécialistes. Par exemple, dans la criminalistique des images, la capacité d’identifier le système d’acquisition d’une image à partir de cette dernière seulement, est un enjeu actuel.Dans cette thèse, nous présentons une première analyse du problème d’identification des scanners X. Pour proposer une solution à ce type de problèmes, nous nous sommes basés sur les méthodes d’identification d’appareils photo. Elles reposent sur l’extraction de l’empreinte des capteurs. L’objectif est alors de détecter sa présence dans les images testées. Pour extraire le bruit, nous utilisons un filtre de Wiener basé sur une transformation en ondelettes. Ensuite, nous nous appuyons sur les propriétés relatives aux images médicales pour proposer des solutions avancées pour l’identification des scanners X. Ces solutions sont basées sur une nouvelle conception de leur empreinte, cette dernière étant définie en trois dimensions et sur les trois couches : os, tissu et air.Pour évaluer notre travail, nous avons généré des résultats sur un ensemble de données réelles acquises avec différents scanners X. Finalement, nos méthodes sont robustes et donnent une précision d’authentification élevée. Nous sommes en mesure d’identifier quelle machine a servi pour l’acquisition d’une image 3D et l’axe selon lequel elle a été effectuée. / Medical image processing aims to help the doctors to improve the diagnosis process. Computed Tomography (CT) Scanner is an imaging medical device used to create cross-sectional 3D images of any part of the human body. Today, it is very important to secure medical images during their transmission, storage, visualization and sharing between several doctors. For example, in image forensics, a current problem consists of being able to identify an acquisition system from only digital images. In this thesis, we present one of the first analysis of CT-Scanner identification problem. We based on the camera identification methods to propose a solution for such kind of problem. It is based on extracting a sensor noise fingerprint of the CT-Scanner device. The objective then is to detect its presence in any new tested image. To extract the noise, we used a wavelet-based Wiener denoising filter. Then, we depend on the properties of medical images to propose advanced solutions for CT-Scanner identification. These solutions are based on new conceptions in the medical device fingerprint that are the three dimension fingerprint and the three layers one. To validate our work, we applied our experiments on multiple real data images of multiple CT-Scanner devices. Finally, our methods that are robust, give high identification accuracy. We were able to identify the acquisition CT-Scanner device and the acquisition axis.
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Método para avaliação dos algoritmos utilizados no processamento de imagens médicas / Method for evaluation of the algorithms used in the processing of medical imagesRodrigues, Silvia Cristina Martini 24 September 1999 (has links)
Este trabalho apresenta como parte de resultados, uma ampla pesquisa que permitiu identificar os grupos de pesquisas mais importantes do mundo, os quais possuem em comum o processamento de imagens médicas, mais especificamente o processamento de imagens que busca a identificação de microcalcificações mamárias. O vasto levantamento, a seleção e organização culminou na reunião de mais de cem artigos, publicados nos mais importantes periódicos da área, que mostram claramente as formas utilizadas pelos grupos de pesquisa para apresentação dos resultados encontrados pelos seus algoritmos. Esses resultados devem auxiliar o médico no diagnóstico do câncer de mama. Demonstramos neste trabalho porque as técnicas utilizadas para apresentação dos resultados são insatisfatórias e propusemos um novo método de avaliação desses resultados. O método proposto no trabalho baseia-se no teste do X² (Qui-Quadrado), nas curvas ROC (Receiver Operating Characteristic) e no teste de concordância, que juntos permitem apresentar de forma clara e objetiva as relações entre verdadeiros positivos e falsos positivos, verdadeiros negativos e falsos negativos, sensibilidade e especificidade do algoritmo analisado. O novo método é preciso e tem bases estatísticas conhecidas pelos médicos e pelos pesquisadores, facilitando sua aceitação. / This work presents as part of results, a wide investigation that it allowed to identify the principal research groups of the world, which possess in common the processing of medical images, more specifically the processing of images that search for the identification of mammary microcalcifications. The vast collection, selection and organization culminated in the meeting of more than a hundred articles, published in the most important newspapers of the area, that show the forms used by the research groups to present the results found clearly by its algorithms. Those results should assist the doctor in the diagnosis of the breast cancer. We demonstrated in this work that the techniques used for presentation of the results are unsatisfactory and we proposed a new method of evaluation of those results. The proposed method bases on the test of the X² (Qui-square), in ROC curve (Receiver Operating Characteristic) and in the agreement test, that take together allow to present in a clear and objective way the relationships among true positive and false positive, true negative and false negative, sensibility and specificity of the analyzed algorithm. The new method is precise and has statistical bases known by the clinicians and researchers, facilitating its acceptance.
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