Yes / A confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, medical practioners can extract clinical information on the state of health of the patient's cornea. In this work we are addressing problems associated with capturing and processing these images including blurring, non-uniform illumination and noise, as well as the displacement of images laterally and in the anterior posterior direction caused by subject movement. The latter may cause some of the captured images to be out of sequence in terms of depth. In this paper we introduce automated algorithms for classification, reordering, registration and segmentation to solve these problems. The successful implementation of these algorithms could open the door for another interesting development, which is the 3D modelling of these sequences.

Non-rigid image registration is an important tool for analysing morphometric differences in subjects with Alzheimer's disease from structural magnetic resonance images of the brain. This thesis describes a novel probabilistic approach to non-rigid registration of medical images, and explores the benefits of its use in this area of neuroimaging. Many image registration approaches have been developed for neuroimaging. The vast majority suffer from two limitations: Firstly, the trade-off between image fidelity and regularisation requires selection. Secondly, only a point-estimate of the mapping between images is inferred, overlooking the presence of uncertainty in the estimation. This thesis introduces a novel probabilistic non-rigid registration model and inference scheme. This framework allows the inference of the parameters that control the level of regularisation, and data fidelity in a data-driven fashion. To allow greater flexibility, this model is extended to allow the level of data fidelity to vary across space. A benefit of this approach, is that the registration can adapt to anatomical variability and other image acquisition differences. A further advantage of the proposed registration framework is that it provides an estimate of the distribution of probable transformations. Additional novel contributions of this thesis include two proposals for exploiting the estimated registration uncertainty. The first of these estimates a local image smoothing filter, which is based on the registration uncertainty. The second approach incorporates the distribution of transformations into an ensemble learning scheme for statistical prediction. These techniques are integrated into standard frameworks for morphometric analysis, and are demonstrated to improve the ability to distinguish subjects with Alzheimer's disease from healthy controls.

袁國華. / 論文(博士) -- 香港中文大學硏究院中國語言及文學學部, 1995. / 參考文献 : leaves 536-569. / Yuan Guohua. / 序 / Chapter 第一章 --- 緒論 --- p.1 / Chapter 第一節 --- 包山楚簡的發現 --- p.1 / Chapter 第二節 --- 包山楚簡的內容 --- p.4 / Chapter 一 --- 「文書」的內容 --- p.4 / Chapter (一) --- 「集箸」「集箸言」簡的內容 --- p.4 / Chapter (二) --- 「受□」簡的內容 --- p.6 / Chapter (三) --- 「疋獄」簡的內容 --- p.7 / Chapter (四) --- 欠標題簡的內容 --- p.9 / Chapter 二 --- 「卜筮祭禱紀錄」的內容 --- p.12 / Chapter 三 --- 「遣策」的內容 --- p.15 / Chapter 第三節 --- 包山楚簡的價值 --- p.19 / Chapter 一 --- 補充文獻的不足 --- p.19 / Chapter 二 --- 証明史書的可信 --- p.21 / Chapter 三 --- 印證其他楚簡的內容 --- p.22 / Chapter 四 --- 保存器物的名實 --- p.23 / Chapter 五 --- 有助古文字的考釋 --- p.25 / Chapter 第四節 --- 本文的研究範疇與目的 --- p.27 / Chapter 第二章 --- 包山楚簡所見名籍編制考 --- p.37 / Chapter 第一節 --- 「□典」意義的商榷 --- p.40 / Chapter 一 --- 「□」字的字形、讀音及意義 --- p.40 / Chapter 二 --- 「典」字的意義及其相關問題 --- p.55 / Chapter 三 --- 與「□典」有關簡文的釋讀 --- p.64 / Chapter 四 --- 釋「□典」 --- p.80 / Chapter 第二節 --- 名籍的種類 --- p.95 / Chapter 一 --- 玉□之典 --- p.99 / Chapter 二 --- □典 --- p.103 / Chapter 三 --- 陳豫之典 --- p.106 / Chapter 四 --- 某瘽之典 --- p.109 / Chapter 第三節 --- 名籍的編造與保管 --- p.112 / Chapter 一 --- 《周禮》所載名籍的編造與保管 --- p.112 / Chapter 二 --- 春秋戰國所載名籍的編造與保管 --- p.116 / Chapter 三 --- 包山楚簡所載名籍的編造與保管 --- p.120 / Chapter 第四節 --- 名籍的用途 --- p.123 / Chapter 一 --- 登除生死 --- p.123 / Chapter 二 --- 查證身分 --- p.127 / Chapter 三 --- 明別歸屬 --- p.130 / Chapter 第三章 --- 包山楚簡「受□」意義考 --- p.143 / Chapter 第一節 --- 「受□」意義的商榷 --- p.144 / Chapter 一 --- 「受□」即「受賄」說的商榷 --- p.144 / Chapter 二 --- 「受□」即「受稽」說的商榷 --- p.159 / Chapter 三 --- 「受□」即「接受年度考核」說的商榷 --- p.168 / Chapter 第二節 --- 釋「受□」 --- p.186 / Chapter 第三節 --- 「受□」簡的釋讀 --- p.191 / Chapter 第四節 --- 海外「受□」遺簡述要 --- p.202 / Chapter 一 --- 有一簡記二事者 --- p.202 / Chapter 二 --- 簡文內容有重覆出現者 --- p.206 / Chapter 三 --- 大事紀年所記地名有別者 --- p.208 / Chapter 第四章 --- 包山楚簡遣策所見「□」、「房□」、「鑐」等器物形制考 --- p.220 / Chapter 第一節 --- 「□」器形制考 --- p.220 / Chapter 一 --- 釋「□」 --- p.220 / Chapter 二 --- 釋「□」 --- p.232 / Chapter 三 --- 論「□」器的形制 --- p.248 / Chapter 第二節 --- 「房□」形制考 --- p.264 / Chapter 一 --- 釋「一房□」 --- p.264 / Chapter 二 --- 釋「房□」形制 --- p.274 / Chapter 第三節 --- 「鑐」鼎形制考 --- p.285 / Chapter 一 --- 釋「亥」 --- p.285 / Chapter 二 --- 釋「鑐」鼎的形制 --- p.293 / Chapter 第五章 --- 包山楚簡文字考釋 --- p.322 / Chapter 1 --- □ --- p.324 / Chapter 2 --- 頸 --- p.325 / Chapter 3 --- 戌 --- p.327 / Chapter 4 --- □ --- p.328 / Chapter 5 --- □、□、□ --- p.332 / Chapter 6 --- 主 --- p.336 / Chapter 7 --- □ --- p.340 / Chapter 8 --- 天 --- p.342 / Chapter 9 --- □ --- p.344 / Chapter 10 --- □ --- p.345 / Chapter 11 --- □ --- p.346 / Chapter 12 --- □ --- p.348 / Chapter 13 --- □ --- p.352 / Chapter 14 --- □ --- p.354 / Chapter 15 --- 受 --- p.355 / Chapter 16 --- □ --- p.359 / Chapter 17 --- □ --- p.360 / Chapter 18 --- □、□ --- p.361 / Chapter 19 --- □ --- p.362 / Chapter 20 --- 齊 --- p.362 / Chapter 21 --- 已 --- p.364 / Chapter 22 --- □ --- p.366 / Chapter 23 --- □ --- p.367 / Chapter 24 --- 常 --- p.367 / Chapter 25 --- 衣 --- p.371 / Chapter 26 --- □ --- p.372 / Chapter 27 --- □ --- p.373 / Chapter 28 --- □ --- p.375 / Chapter 29 --- 伬 --- p.376 / Chapter 30 --- □ --- p.379 / Chapter 附錄一： --- 重訂「包山楚簡」釋文 --- p.389 / Chapter 附錄二： --- 包山楚簡文字諸家考釋一覽表(一)(二) --- p.485 / 主要參考書目及其簡稱 --- p.536

Natural surfaces are usually associated with feature graphs, such as the cortical surface with anatomical atlas structure. Such a feature graph subdivides the whole surface into meaningful sub-regions. Existing brain mapping and registration methods did not integrate anatomical atlas structures. As a result, with existing brain mappings, it is difficult to visualize and compare the atlas structures. And also existing brain registration methods can not guarantee the best possible alignment of the cortical regions which can help computing more accurate shape similarity metrics for neurodegenerative disease analysis, e.g., Alzheimer’s disease (AD) classification. Also, not much attention has been paid to tackle surface parameterization and registration with graph constraints in a rigorous way which have many applications in graphics, e.g., surface and image morphing. This dissertation explores structural mappings for shape analysis of surfaces using the feature graphs as constraints. (1) First, we propose structural brain mapping which maps the brain cortical surface onto a planar convex domain using Tutte embedding of a novel atlas graph and harmonic map with atlas graph constraints to facilitate visualization and comparison between the atlas structures. (2) Next, we propose a novel brain registration technique based on an intrinsic atlas-constrained harmonic map which provides the best possible alignment of the cortical regions. (3) After that, the proposed brain registration technique has been applied to compute shape similarity metrics for AD classification. (4) Finally, we propose techniques to compute intrinsic graph-constrained parameterization and registration for general genus-0 surfaces which have been used in surface and image morphing applications.

Content Based Image Retrieval (CBIR) is an important research area in the field of multimedia information retrieval. The application of CBIR in the medical domain has been attempted before, however the use of CBIR in medical diagnostics is a daunting task. The goal of diagnostic medical image retrieval is to provide diagnostic support by displaying relevant past cases, along with proven pathologies as ground truths. Moreover, medical image retrieval can be extremely useful as a training tool for medical students and residents, follow-up studies, and for research purposes. Despite the presence of an impressive amount of research in the area of CBIR, its acceptance for mainstream and practical applications is quite limited. The research in CBIR has mostly been conducted as an academic pursuit, rather than for providing the solution to a need. For example, many researchers proposed CBIR systems where the image database consists of images belonging to a heterogeneous mixture of man-made objects and natural scenes while ignoring the practical uses of such systems. Furthermore, the intended use of CBIR systems is important in addressing the problem of "Semantic Gap". Indeed, the requirements for the semantics in an image retrieval system for pathological applications are quite different from those intended for training and education. Moreover, many researchers have underestimated the level of accuracy required for a useful and practical image retrieval system. The human eye is extremely dexterous and efficient in visual information processing; consequently, CBIR systems should be highly precise in image retrieval so as to be useful to human users. Unsurprisingly, due to these and other reasons, most of the proposed systems have not found useful real world applications. In this dissertation, an attempt is made to address the challenging problem of developing a retrieval system for medical diagnostics applications. More specifically, a system for semantic retrieval of Magnetic Resonance (MR) images in 3D brain volumes is proposed. The proposed retrieval system has a potential to be useful for clinical experts where the human eye may fail. Previously proposed systems used imprecise segmentation and feature extraction techniques, which are not suitable for precise matching requirements of the image retrieval in this application domain. This dissertation uses multiscale representation for image retrieval, which is robust against noise and MR inhomogeneity. In order to achieve a higher degree of accuracy in the presence of misalignments, an image registration based retrieval framework is developed. Additionally, to speed-up the retrieval system, a fast discrete wavelet based feature space is proposed. Further improvement in speed is achieved by semantically classifying of the human brain into various "Semantic Regions", using an SVM based machine learning approach. A novel and fast identification system is proposed for identifying a 3D volume given a 2D image slice. To this end, we used SVM output probabilities for ranking and identification of patient volumes. The proposed retrieval systems are tested not only for noise conditions but also for healthy and abnormal cases, resulting in promising retrieval performance with respect to multi-modality, accuracy, speed and robustness. This dissertation furnishes medical practitioners with a valuable set of tools for semantic retrieval of 2D images, where the human eye may fail. Specifically, the proposed retrieval algorithms provide medical practitioners with the ability to retrieve 2D MR brain images accurately and monitor the disease progression in various lobes of the human brain, with the capability to monitor the disease progression in multiple patients simultaneously. Additionally, the proposed semantic classification scheme can be extremely useful for semantic based categorization, clustering and annotation of images in MR brain databases. This research framework may evolve in a natural progression towards developing more powerful and robust retrieval systems. It also provides a foundation to researchers in semantic based retrieval systems on how to expand existing toolsets for solving retrieval problems.

Content Based Image Retrieval (CBIR) is an important research area in the field of multimedia information retrieval. The application of CBIR in the medical domain has been attempted before, however the use of CBIR in medical diagnostics is a daunting task. The goal of diagnostic medical image retrieval is to provide diagnostic support by displaying relevant past cases, along with proven pathologies as ground truths. Moreover, medical image retrieval can be extremely useful as a training tool for medical students and residents, follow-up studies, and for research purposes. Despite the presence of an impressive amount of research in the area of CBIR, its acceptance for mainstream and practical applications is quite limited. The research in CBIR has mostly been conducted as an academic pursuit, rather than for providing the solution to a need. For example, many researchers proposed CBIR systems where the image database consists of images belonging to a heterogeneous mixture of man-made objects and natural scenes while ignoring the practical uses of such systems. Furthermore, the intended use of CBIR systems is important in addressing the problem of "Semantic Gap". Indeed, the requirements for the semantics in an image retrieval system for pathological applications are quite different from those intended for training and education. Moreover, many researchers have underestimated the level of accuracy required for a useful and practical image retrieval system. The human eye is extremely dexterous and efficient in visual information processing; consequently, CBIR systems should be highly precise in image retrieval so as to be useful to human users. Unsurprisingly, due to these and other reasons, most of the proposed systems have not found useful real world applications. In this dissertation, an attempt is made to address the challenging problem of developing a retrieval system for medical diagnostics applications. More specifically, a system for semantic retrieval of Magnetic Resonance (MR) images in 3D brain volumes is proposed. The proposed retrieval system has a potential to be useful for clinical experts where the human eye may fail. Previously proposed systems used imprecise segmentation and feature extraction techniques, which are not suitable for precise matching requirements of the image retrieval in this application domain. This dissertation uses multiscale representation for image retrieval, which is robust against noise and MR inhomogeneity. In order to achieve a higher degree of accuracy in the presence of misalignments, an image registration based retrieval framework is developed. Additionally, to speed-up the retrieval system, a fast discrete wavelet based feature space is proposed. Further improvement in speed is achieved by semantically classifying of the human brain into various "Semantic Regions", using an SVM based machine learning approach. A novel and fast identification system is proposed for identifying a 3D volume given a 2D image slice. To this end, we used SVM output probabilities for ranking and identification of patient volumes. The proposed retrieval systems are tested not only for noise conditions but also for healthy and abnormal cases, resulting in promising retrieval performance with respect to multi-modality, accuracy, speed and robustness. This dissertation furnishes medical practitioners with a valuable set of tools for semantic retrieval of 2D images, where the human eye may fail. Specifically, the proposed retrieval algorithms provide medical practitioners with the ability to retrieve 2D MR brain images accurately and monitor the disease progression in various lobes of the human brain, with the capability to monitor the disease progression in multiple patients simultaneously. Additionally, the proposed semantic classification scheme can be extremely useful for semantic based categorization, clustering and annotation of images in MR brain databases. This research framework may evolve in a natural progression towards developing more powerful and robust retrieval systems. It also provides a foundation to researchers in semantic based retrieval systems on how to expand existing toolsets for solving retrieval problems.

Tato práce se zabývá multimodální registrací snímků sítnice z různých skenovacích zařízení. Multimodální registrace umožňuje zvýraznit prvky na snímcích sítnice, které jsou důležité pro detekci různých typů onemocnění oka (jako je glaukom, degradace nervových vláken, degradace cév, atd.). Teoretická část tvoří zhruba první půlku práce a je následována praktickou částí, která popisuje postupy při různých typech registrací snímků z fundus kamery, SLO a OCT. Registrace fundus a SLO snímků je provedena pomocí prostorové transformace. Tato práce popisuje tři různé metody registrace SLO snímků se snímky z fundus kamery. První a zároveň nejjednodušší je manuální registrace. Druhou je automatická registrace založená na metodě korelace. Výsledky, včetně porovnání obou metod, jsou uvedeny v závěru. Třetím typem je poloautomatická registrace, která využívá výhod obou předchozích metod a tím pádem je kompromisem mezi rychlostí a přesností registrace. Registrace fundus snímků a B-scanů z OCT je realizována dvěma různými metodami. První je opět založená na korelaci a druhá na prostorové transformaci. Všechny tyto registrační metody jsou realizovány také prakticky v programovém prostředí Matlab.

Image registration is widely used in clinical practice. However image registration and its~evaluation is still challenging especially with regards to new possibilities of various modalities. One of these areas is contrast-enhanced ultrasound imaging. The time-dependent image contrast, low signal-to-noise ratio and specific speckle pattern make preprocessing and image registration difficult. In this thesis a method for registration of images in ultrasound contrast-enhanced sequences is proposed. The method is based on automatic fragmentation into image subsequences in which the images with similar characteristics are registered. The new evaluation method based on comparison of perfusion model is proposed. Registration and evaluation method was tested on a flow phantom and real patient data and compared with a standard methods proposed i literature. The second part of this thesis contains examples of application of image registration in~ophthalmology and proposition for its improvement. In this area the image registration methods are widely used, especially landmark based image registration method. In this thesis methods for landmark detection and its correspondence estimation are proposed.

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Currently the entire South African industry of Environmental Control Officers (ECOs) is unregulated, yet ECOs have the important task of checking and verifying compliance to environmental regulatory and performance requirements. According to international best practice principles for Environmental Impact Assessment (EIA), the process should be made credible through independent checks and verification (IAIA, 1999:3). According to the Environmental Impact Assessment Management Strategy Subtheme 5, quality assurance can only be attained when practitioners fulfil competence and ethical practice criteria. In this document the South African Department of Environmental Affairs (SADEA) also noted that there is currently no home for ECOs, yet this is where effective monitoring and enforcement could significantly improve environmental outcomes. The central objective of this dissertation was to investigate the need for regulation of the South African ECO industry. Data obtained from the questionnaires indicated that 100% of respondents were in agreement that there is a need for regulation of the South African ECO industry. One of the key motivational factors identified by respondents was quality assurance, which is important, as the environmental legislative regime changes constantly. Various other factors were identified other than quality assurance and were labelled “drivers”. These include establishment of minimum standards in respect of qualifications and/or experience (core competencies), establishment of a professional code of conduct and ethics that enhances accountability and professionalism, skills improvement through continual professional development (CPD), enhancement of credibility, independence of practitioners, enhancement of skills for capacity building, protection of clients against substandard work and overall lack of professionalism, and finally creation of a source of information support and interaction. It was important to determine what drives the regulation of an industry, in order to establish whether the South African ECO industry has similar drivers justifying regulation. The establishment of qualification and competency requirements was an important objective of this research, as a set of these requirements is an essential toolkit for operating ECOs and key stakeholders of the industry. Various registration and competence requirements for ECOs were identified from the literature review and responses to the questionnaires and interviews. With this research the author also intended to establish which current accreditation bodies could be considered for registration of ECOs and regulation of the South African ECO industry. The dissertation concludes by emphasising the importance of regulating the South African ECO industry, as this will ensure that compliance monitoring takes place effectively. / M Environmental Management, North-West University, Potchefstroom Campus, 2014