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Relevant bibliographies by topics / Medical Images Processing / Dissertations / Theses

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Dissertations / Theses on the topic 'Medical Images Processing'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Tummala, Sai Virali, and Veerendra Marni. "Comparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15360.

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2

Matalas, Ioannis. "Segmentation techniques suitable for medical images." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339149.

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3

Ford, Ralph M. (Ralph Michael) 1965. "Computer-aided analysis of medical infrared images." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/276986.

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Thermography is a useful tool for analyzing spinal nerve root irritation, but interpretation of digital infrared images is often qualitative and subjective. A new quantitative, computer-aided method for analyzing thermograms, utilizing the human dermatome map, is presented. Image processing and pattern recognition principles needed to accomplish this goal are discussed. Algorithms for segmentation, boundary detection and interpretation of thermograms are presented. An interactive, user-friendly program to perform this analysis has been developed. Due to the relatively large number of images in

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4

Young, N. G. "The digital processing of astronomical and medical coded aperture images." Thesis, University of Southampton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.482729.

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5

Chabane, Yahia. "Semantic and flexible query processing of medical images using ontologies." Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22784/document.

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L’interrogation efficace d’images en utilisant un système de recherche d’image est un problème qui a attiré l’attention de la communauté de recherche depuis une longue période. Dans le domaine médical, les images sont de plus en plus produites en grandes quantités en raison de leur intérêt croissant pour de nombreuses pratiques médicales comme le diagnostic, la rédaction de rapports et l’enseignement. Cette thèse propose un système d’annotation et recherche sémantique d’images gastroentérologiques basé sur une nouvelle ontologie des polypes qui peut être utilisée pour aider l

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6

O, Dobrina. "Segmentation as a part of the intelligent medical image processing." Thesis, Київ, Національний авіаційний університет, 2012. http://er.nau.edu.ua/handle/NAU/18854.

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Segmentation is one of the key tools in medical image analysis that allows an accurate recognizing and delineating individual objects (e.g. organs) on the whole image quickly and effectively. In general, segmentation technics can be divided into two main groups: methods of explicitly specifying the desired feature and algorithms where the specification is implicit. Automated segmentation of medical images is a difficult task, because the images are often noisy and contain more than a single anatomical structure with narrow distance between organ boundaries.

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Agrafiotis, Dimitris. "Three dimensional coding and visualisation of volumetric medical images." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271864.

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8

Zhao, Guang, and 趙光. "Automatic boundary extraction in medical images based on constrained edge merging." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31223904.

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9

Morton, A. S. "A knowledge-based approach to the interpretation of medical ultrasound images." Thesis, University of Brighton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254407.

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10

Cabrera, Gil Blanca. "Deep Learning Based Deformable Image Registration of Pelvic Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279155.

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Deformable image registration is usually performed manually by clinicians,which is time-consuming and costly, or using optimization-based algorithms, which are not always optimal for registering images of different modalities. In this work, a deep learning-based method for MR-CT deformable image registration is presented. In the first place, a neural network is optimized to register CT pelvic image pairs. Later, the model is trained on MR-CT image pairs to register CT images to match its MR counterpart. To solve the unavailability of ground truth data problem, two approaches were used. For the

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11

Madaris, Aaron T. "Characterization of Peripheral Lung Lesions by Statistical Image Processing of Endobronchial Ultrasound Images." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1485517151147533.

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12

Williams, Glenda Patricia. "Development and clinical application of techniques for the image processing and registration of serially acquired medical images." Thesis, University of South Wales, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326718.

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13

Björn, Martin. "Laterality Classification of X-Ray Images : Using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178409.

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When radiologists examine X-rays, it is crucial that they are aware of the laterality of the examined body part. The laterality refers to which side of the body that is considered, e.g. Left and Right. The consequences of a mistake based on information regarding the incorrect laterality could be disastrous. This thesis aims to address this problem by providing a deep neural network model that classifies X-rays based on their laterality. X-ray images contain markers that are used to indicate the laterality of the image. In this thesis, both a classification model and a detection model have been

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14

Zhao, Guang. "Automatic boundary extraction in medical images based on constrained edge merging." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22030207.

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15

Quartararo, John David. "Semi-Automated Segmentation of 3D Medical Ultrasound Images." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/155.

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A level set-based segmentation procedure has been implemented to identify target object boundaries from 3D medical ultrasound images. Several test images (simulated, scanned phantoms, clinical) were subjected to various preprocessing methods and segmented. Two metrics of segmentation accuracy were used to compare the segmentation results to ground truth models and determine which preprocessing methods resulted in the best segmentations. It was found that by using an anisotropic diffusion filtering method to reduce speckle type noise with a 3D active contour segmentation routine using the leve

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Koller, Daniela. "Processing of Optical Coherence Tomography Images : Filtering and Segmentation of Pathological Thyroid Tissue." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161988.

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In the human body, the main function of the healthy thyroid gland is the regulation of the metabolism and hormone production. Included in the thyroid are organized structured and uniformly shaped follicles ranging from 50-500 μm in diameter. Pathologies lead to morphological changes of these follicles, affecting the density and size, but can also lead to an absence. In this study optical coherence tomography (OCT) was used to examine pathological thyroid tissue by extracting structural information of the follicles from image segmentation. However, OCT images usually include a high amount of sp

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17

Martínez, Escobar Marisol. "An interactive color pre-processing method to improve tumor segmentation in digital medical images." [Ames, Iowa : Iowa State University], 2008.

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18

Gao, Zhiyun. "Novel multi-scale topo-morphologic approaches to pulmonary medical image processing." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/805.

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The overall aim of my PhD research work is to design, develop, and evaluate a new practical environment to generate separated representations of arterial and venous trees in non-contrast pulmonary CT imaging of human subjects and to extract quantitative measures at different tree-levels. Artery/vein (A/V) separation is of substantial importance contributing to our understanding of pulmonary structure and function, and immediate clinical applications exist, e.g., for assessment of pulmonary emboli. Separated A/V trees may also significantly boost performance of airway segmentation methods for h

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Manousakas, Ioannis. "A comparative study of segmentation algorithms applied to 2- and 3- dimensional medical images." Thesis, University of Aberdeen, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360340.

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A method that enables discriminating between CSF-grey matter edges and grey-white matter edges separately has been suggested. It was obvious that edges from this method are more complete that those resolved by the original method and have fewer artifacts. Some edges that were undetected before, are now detected because they do not have any influence from stronger nearby edges. Texture noise is also suppressed and this allows us to work at higher space scales. These 3D edge detection methods proved to be superior to the equivalent 2D methods because they can calculate the gradient more accurate

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Moore, C. J. "Mathematical analysis and picture encoding methods applied to large stores of archived digital images." Thesis, University of Manchester, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234220.

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21

Usta, Fatma. "Image Processing Methods for Myocardial Scar Analysis from 3D Late-Gadolinium Enhanced Cardiac Magnetic Resonance Images." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37920.

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Myocardial scar, a non-viable tissue which occurs on the myocardium due to the insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Analysis of myocardial scar is important for predicting the risk of arrhythmia and locations of re-entrant circuits in patients’ hearts. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, reconstruction of the intact geometry of scar is required. Currently, 2D multi-slice late gadolinium-e

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Gonzalez, Ana Guadalupe Salazar. "Structure analysis and lesion detection from retinal fundus images." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6456.

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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 specif

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ZHAO, HANG. "Segmentation and synthesis of pelvic region CT images via neural networks trained on XCAT phantom data." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178209.

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Deep learning methods for medical image segmentation are hindered by the lack of training data. This thesis aims to develop a method that overcomes this problem. Basic U-net trained on XCAT phantom data was tested first. The segmentation results were unsatisfactory even when artificial quantum noise was added. As a workaround, CycleGAN was used to add tissue textures to the XCAT phantom images by analyzing patient CT images. The generated images were used totrain the network. The textures introduced by CycleGAN improved the segmentation, but some errors remained. Basic U-net was replaced with

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Abutalib, Feras Wasef. "A methodology for applying three dimensional constrained Delaunay tetrahedralization algorithms on MRI medical images /." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=112551.

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This thesis addresses the problem of producing three-dimensional constrained Delaunay triangulated meshes from the sequential two dimensional MRI medical image slices. The approach is to generate the volumetric meshes of the scanned organs as a result of a several low-level tasks: image segmentation, connected component extraction, isosurfacing, image smoothing, mesh decimation and constrained Delaunay tetrahedralization. The proposed methodology produces a portable application that can be easily adapted and extended by researchers to tackle this problem. The application requires very minimal

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Lu, Yi Cheng. "Classifying Liver Fibrosis Stage Using Gadoxetic Acid-Enhanced MR Images." Thesis, Linköpings universitet, Institutionen för medicin och hälsa, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162989.

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The purpose is trying to classify the Liver Fibrosis stage using Gadoxetic Acid-EnhancedMR Images. In the very beginning, a method proposed by one Korean group is being examined and trying to reproduce their result. However, the performance is not as impressive as theirs. Then, some gray-scale image feature extraction methods are used. Last but not least, the hottest method in recent years - ConvolutionNeural Network(CNN) was utilized. Finally, the performance has been evaluated in both methods. The result shows that with manual feature extraction, the Adaboost model works pretty well that AU

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D'Souza, Aswin Cletus. "Automated counting of cell bodies using Nissl stained cross-sectional images." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2035.

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Kéchichian, Razmig. "Structural priors for multiobject semi-automatic segmentation of three-dimensional medical images via clustering and graph cut algorithms." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00967381.

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We develop a generic Graph Cut-based semiautomatic multiobject image segmentation method principally for use in routine medical applications ranging from tasks involving few objects in 2D images to fairly complex near whole-body 3D image segmentation. The flexible formulation of the method allows its straightforward adaption to a given application.\linebreak In particular, the graph-based vicinity prior model we propose, defined as shortest-path pairwise constraints on the object adjacency graph, can be easily reformulated to account for the spatial relationships between objects in a given pro

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De, Luca Massimo. "New techniques for the processing and analysis of retinal images in diagnostic ophtalmology." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3425117.

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This thesis deals with the automatic analysis of color fundus images and with its application to diagnostic ophthalmology. Diabetes is a growing epidemia in the world, due to population growth, aging, urbanization and increasing prevalence of obesity and physical inactivity, so diabetic retinopathy has an ever increasing importance as a cause of blindness. Also hypertension affects the microcirculation and hypertensive retinopathy is one of the consequences of such damage. In this thesis new algorithms to help ophthalmologist's diagnosis and to be used in automated systems for retinopathy

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Guarnieri, Gabriele. "High dynamic range images: processing, display and perceptual quality assessment." Doctoral thesis, Università degli studi di Trieste, 2009. http://hdl.handle.net/10077/3121.

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2007/2008<br>The intensity of natural light can span over 10 orders of magnitude from starlight to direct sunlight. Even in a single scene, the luminance of the bright areas can be thousands or millions of times greater than the luminance in the dark areas; the ratio between the maximum and the minimum luminance values is commonly known as dynamic range or contrast. The human visual system is able to operate in an extremely wide range of luminance conditions without saturation and at the same time it can perceive fine details which involve small luminance differences. Our eyes achieve this abi

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Elbita, Abdulhakim M. "Efficient Processing of Corneal Confocal Microscopy Images. Development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6463.

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Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there

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Oscanoa1, Julio, Marcelo Mena, and Guillermo Kemper. "A Detection Method of Ectocervical Cell Nuclei for Pap test Images, Based on Adaptive Thresholds and Local Derivatives." Science and Engineering Research Support Society, 2015. http://hdl.handle.net/10757/624843.

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Cervical cancer is one of the main causes of death by disease worldwide. In Peru, it holds the first place in frequency and represents 8% of deaths caused by sickness. To detect the disease in the early stages, one of the most used screening tests is the cervix Papanicolaou test. Currently, digital images are increasingly being used to improve Pap test efficiency. This work develops an algorithm based on adaptive thresholds, which will be used in Pap smear assisted quality control software. The first stage of the method is a pre-processing step, in which noise and background removal is done. N

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Elbita, Abdulhakim Mehemed. "Efficient processing of corneal confocal microscopy images : development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6463.

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Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there

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Sidiropoulos, Konstantinos. "Pattern recognition systems design on parallel GPU architectures for breast lesions characterisation employing multimodality images." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9190.

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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 de

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Pehrson, Skidén Ottar. "Automatic Exposure Correction And Local Contrast Setting For Diagnostic Viewing of Medical X-ray Images." Thesis, Linköping University, Department of Biomedical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56630.

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<p>To properly display digital X-ray images for visual diagnosis, a proper display range needs to be identified. This can be difficult when the image contains collimators or large background areas which can dominate the histograms. Also, when there are both underexposed and overexposed areas in the image it is difficult to display these properly at the same time. The purpose of this thesis is to find a way to solve these problems. A few different approaches are evaluated to find their strengths and weaknesses. Based on Local Histogram Equalization, a new method is developed to put various cons

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Karlsson, Simon, and Per Welander. "Generative Adversarial Networks for Image-to-Image Translation on Street View and MR Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148475.

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Generative Adversarial Networks (GANs) is a deep learning method that has been developed for synthesizing data. One application for which it can be used for is image-to-image translations. This could prove to be valuable when training deep neural networks for image classification tasks. Two areas where deep learning methods are used are automotive vision systems and medical imaging. Automotive vision systems are expected to handle a broad range of scenarios which demand training data with a high diversity. The scenarios in the medical field are fewer but the problem is instead that it is diffi

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Ren, Jing. "From RF signals to B-mode Images Using Deep Learning." Thesis, KTH, Medicinteknik och hälsosystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235061.

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Ultrasound imaging is a safe and popular imaging technique that relies on received radio frequency (RF) echos to show the internal organs and tissue. B-mode (Brightness mode) is the typical mode of ultrasound images generated from RF signals. In practice, the real processing algorithms from RF signals to B-mode images in ultrasound machines are kept confidential by the manufacturers. The thesis aims to estimate the process and reproduce the same results as the Ultrasonix One ultrasound machine does using deep learning. 11 scalar parameters including global gain, time-gain-compensation (TGC1-8)

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Hrabovszki, Dávid. "Classification of brain tumors in weakly annotated histopathology images with deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177271.

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Brain and nervous system tumors were responsible for around 250,000 deaths in 2020 worldwide. Correctly identifying different tumors is very important, because treatment options largely depend on the diagnosis. This is an expert task, but recently machine learning, and especially deep learning models have shown huge potential in tumor classification problems, and can provide fast and reliable support for pathologists in the decision making process. This thesis investigates classification of two brain tumors, glioblastoma multiforme and lower grade glioma in high-resolution H&E-stained hist

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Zaman, Shaikh Faisal. "Automated Liver Segmentation from MR-Images Using Neural Networks." Thesis, Linköpings universitet, Avdelningen för radiologiska vetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162599.

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Liver segmentation is a cumbersome task when done manually, often consuming quality time of radiologists. Use of automation in such clinical task is fundamental and the subject of most modern research. Various computer aided methods have been incorporated for this task, but it has not given optimal results due to the various challenges faced as low-contrast in the images, abnormalities in the tissues, etc. As of present, there has been significant progress in machine learning and artificial intelligence (AI) in the field of medical image processing. Though challenges exist, like image sensitiv

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Ognard, Julien. "Place et apport des outils pour l'automatisation du traitement des images médicales en pratique clinique." Thesis, Brest, 2018. http://www.theses.fr/2018BRES0096.

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L'application du traitement de l'image et son automatisation dans le domaine de l'imagerie médicale montre l'évolution des tendances avec la disponibilité des technologies émergentes. Les procédés et outils de traitement de l’image médicale sont résumés, les différentes manières de travailler sur une image sont représentés pour expliquer une recherche expansive dans différents domaines, tandis que les applications disponibles sont discutées. Ces applications sont aussi illustrées par le biais d’outils du traitement de l’image développés pour des besoins spécifiques. La catégorisation de chaque

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Marcuzzo, Mônica. "Quantificação de impressões diagnósticas em imagens de cintilografia renal." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/10344.

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A cintilografia renal é um exame amplamente utilizado para a avaliação visual do funcionamento do córtex renal. Ele permite visualizar a concentração do radiofármaco, o tamanho, a forma, a simetria e a posição dos rins. No entanto, a avaliação visual das impressões diagnósticas dessas imagens tende a ser um processo subjetivo. Isso faz com que ocorra uma significativa variabilidade entre as interpretações feitas por diferentes especialistas. Assim, este trabalho tem como objetivo propor medidas quantitativas que refletem impressões diagnósticas comumente observadas por especialistas nas imagen

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Thakkar, Chintan. "Ventricle slice detection in MRI images using Hough Transform and Object Matching techniques." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001815.

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Wenan, Chen. "Automated Measurement of Midline Shift in Brain CT Images and its Application in Computer-Aided Medical Decision Making." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/121.

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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 sea

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Clark, Matthew C. "Knowledge guided processing of magnetic resonance images of the brain [electronic resource] / by Matthew C. Clark." University of South Florida, 2001. http://purl.fcla.edu/fcla/etd/SFE0000001.

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Includes vita.<br>Title from PDF of title page.<br>Document formatted into pages; contains 222 pages.<br>Includes bibliographical references.<br>Text (Electronic thesis) in PDF format.<br>ABSTRACT: This dissertation presents a knowledge-guided expert system that is capable of applying routinesfor multispectral analysis, (un)supervised clustering, and basic image processing to automatically detect and segment brain tissue abnormalities, and then label glioblastoma-multiforme brain tumors in magnetic resonance volumes of the human brain. The magnetic resonance images used here consist of three f

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Ahmady, Phoulady Hady. "Adaptive Region-Based Approaches for Cellular Segmentation of Bright-Field Microscopy Images." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6794.

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Microscopy image processing is an emerging and quickly growing field in medical imaging research area. Recent advancements in technology including higher computation power, larger and cheaper storage modules, and more efficient and faster data acquisition devices such as whole-slide imaging scanners contributed to the recent microscopy image processing research advancement. Most of the methods in this research area either focus on automatically process images and make it easier for pathologists to direct their focus on the important regions in the image, or they aim to

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Dhinagar, Nikhil J. "Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1366384987.

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Hammadi, Shumoos T. H. "Novel medical imaging technologies for processing epithelium and endothelium layers in corneal confocal images. Developing automated segmentation and quantification algorithms for processing sub-basal epithelium nerves and endothelial cells for early diagnosis of diabetic neuropathy in corneal confocal microscope images." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16924.

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Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the corneal epithelium nerve structures and the corneal endothelial cell can assist early diagnosis of this disease and other corneal diseases, which can lead to visual impairment and then to blindness. In this thesis, fully-automated segmentation and quantification algorithms for processing and analysing sub-basal epithelium nerves and endothelial cells are proposed for early diagnosis of diabetic neuropathy in Corneal Confocal Microscopy (CCM) images. Firstly,

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Moya, Nikolas 1991. "Interactive segmentation of multiple 3D objects in medical images by optimum graph cuts = Segmentação interativa de múltiplos objetos 3D em imagens médicas por cortes ótimos em grafo." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275554.

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Orientador: Alexandre Xavier Falcão<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-27T14:45:13Z (GMT). No. of bitstreams: 1 Moya_Nikolas_M.pdf: 5706960 bytes, checksum: 9304544bfe8a78039de8b62562531865 (MD5) Previous issue date: 2015<br>Resumo: Segmentação de imagens médicas é crucial para extrair medidas de objetos 3D (estruturas anatômicas) que são úteis no diagnóstico e tratamento de doenças. Nestas aplicações, segmentação interativa é necessária quando métodos automáticos falham ou não são factíveis. Métodos p

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Jönsson, Marthina. "Automated methods in the diagnosing of retinal images." Thesis, KTH, Systemsäkerhet och organisation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122721.

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This report contains a summation of a variety of articles that have been read and analysed. Each article describes different methods that can be used to detect lesions, optic disks, drusen and exudates in retinal images. I.e. diagnose e.g. Diabetic Retinopathy and Age-Related Macular Degeneration. A general approach is presented, which all methods more or less is based on. Methods to locate the optic disk The PCA kNN Regression Hough Transform Fuzzy Convergence Vessel Direction Matched Filter Etc. The best method based on result, reliability, number of images and publisher is kNN regres

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Daba, Dieudonne Diba. "Quality Assurance of Intra-oral X-ray Images." Thesis, Umeå universitet, Radiofysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171001.

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Dental radiography is one of the most frequent types of diagnostic radiological investigations performed. The equipment and techniques used are constantly evolving. However, dental healthcare has long been an area neglected by radiation safety legislation and the medical physicist community, and thus, the quality assurance (QA) regime needs an update. This project aimed to implement and evaluate objective tests of key image quality parameters for intra-oral (IO) X-ray images. The image quality parameters assessed were sensitivity, noise, uniformity, low-contrast resolution, and spatial resolut

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Rodrigues, Erbe Pandini. "Avaliação de métricas para o corregistro não rígido de imagens médicas." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-15062010-094159/.

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A medida de similaridade é parte fundamental no corregistro de imagens, guiando todo seu processo. Neste estudo foi feita a comparação entre diferentes métricas de similaridade no contexto do corregistro não rígido (ou elástico) de imagens médicas. Como as imagens cardíacas representam as mais desaadoras situações em corregistro de imagens médicas, foram utilizadas para teste imagens de ressonância magnética nuclear e imagens de ultrasom cardíaco com contraste. 10 métricas de similaridades diferentes foram comparadas extensivamente, quanto ao seu desempenho para o corregistro não rígido: a som

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