無線膠囊內窺鏡（CE）是一種用於檢查整個胃腸道，尤其是小腸的無創技術。它極大地改善了許多小腸疾病的診斷和管理方式，如不明原因的消化道出血，克羅恩病，小腸腫瘤，息肉綜合征等。儘管膠囊內窺鏡有很好的臨床表現，但它仍然有一定的局限性。主要問題是每次檢查產生約50,000 幅低質量的圖像，對於醫生來說，評估如此大量的圖像是一項非常耗時、耗力的工作。 / 到目前為止，對於膠囊內窺鏡的分析和評估，學者們都把膠囊內窺鏡圖像視為單獨的，獨立的觀測對象。事實並非如此，因為圖像之間往往有顯著的重疊。特別是當膠囊內窺鏡在被小腸蠕動緩緩推動時，它可以捕捉同一病灶的多個視圖。我們的研究目的是使用所有可用的資訊，包括多幅圖像，研究對於膠囊內窺鏡的電腦輔助診斷（CAD）系統。 / 在這篇論文中，我們提出了一個嵌入分類器的多類隱馬爾可夫模型（HMM）的方案，它可以融合多幅相鄰圖像的時間資訊。由於膠囊內窺鏡圖像的品質比較低，我們首先進行預處理，以加強膠囊內窺鏡圖像，增加其對比度，消除噪聲。我們調查研究了多種圖像增強的方法，並調整了它們的參數使其適用於膠囊內窺鏡圖像。 / 對於基於單幅圖像的有監督的分類，AdaBoost 作為一個集成分類器來融合多個分類器，即本論文中的支持向量機（SVM），k-近鄰（k-NN），貝葉斯分類。在分類之前，我們提取和融合了顏色，邊緣和紋理特徵。 / 對於無線膠囊內窺鏡的視頻摘要，我們提出了有監督和無監督的兩類方法。對於有監督方法，我們提出了一個基於隱馬爾可夫模型的，靈活的，可擴展的框架，用於整合膠囊內窺鏡中連續圖像的時間資訊。它可以擴展到多類別，多特徵，多狀態。我們還提出了聯合隱馬爾可夫模型和並行隱馬爾可夫（PHMM）模型對系統進行改進，它們可以被看作是決策級的資訊融合。聯合隱馬爾可夫模型通過多層次的隱馬爾可夫模型，結合不同的資訊來源，對膠囊內窺鏡視頻進行分類和視頻摘要。 並行隱馬爾可夫模型採用貝葉斯推理，在決策時融合多個不同來源的資訊。對於無監督的方法，我們首先提出了一種基於顏色的特徵提取方法。在反色顏色空間中對亮度不變的色度不變矩用來表示膠囊內窺鏡圖像的顏色特徵。接著，我們又提出了一種基於輪廓元（Contourlet）變換的局部二元模式（LBP）作為紋理特徵。在特徵空間中，我們測量了相鄰圖像的距離，並把它視為一個位於二維平面上的開放輪廓上的點。 然後，我們採用一個無參數的關鍵點檢測方法檢測在視頻片段上的突變關鍵點。基於這些突變關鍵點，我們對膠囊內窺鏡視頻進行分割。最後，在每段被分割的視頻片段上，我們通過提取有代表性的關鍵幀來實現膠囊內窺鏡視頻摘要。我們分別用模擬和真實的病人數據進行實驗，對提出的方法進行驗證，結果表明了我們所提出的方案的有效性。它在實現自動評估膠囊內窺鏡圖像上具有很大的潛力。 / Wireless Capsule Endoscopy (CE) is a non-invasive technology to inspect the whole gastrointestinal (GI) tract, especially the small intestine. It has dramatically changed the way of diagnosis and management of many diseases of the small intestine, such as obscure gastrointestinal bleeding, Crohn’s disease, small bowel tumors, polyposis syndromes, etc. Despite its promising clinical findings, it still has some limitations. The main problem is that it requires manual assessment of approximately 50,000 low quality images per examination which is highly time-consuming and labor-intense. / CE analysis and assessment so far treated CE images as individual and independent observations. It is obviously not the case as there is often significant overlap among images. In particular, CE captures multiple views of the same anatomy as the capsule is slowly propelled by peristalsis. Our broader work aims to perform computer aided diagnosis (CAD) in endoscopy using all available information, including multiple images. / In this dissertation, a framework of multi-class Hidden Markov Models (HMM) embedded with statistical classifiers for combining information from multiple CE images is proposed. Due to the low quality of CE image, pre-processing is performed to enhance CE images by increasing the contrast and removing noises. Several image enhancement methods are investigated and customized for CE images. For frame-based supervised classification, AdaBoost is used as the ensemble classifier to combine multiple classifiers, i.e. support vector machine (SVM), k-nearest neighbor (k-NN), and Bayes classifier. Before classification, color, edge and texture features are extracted and fused. Finally, both supervised and unsupervised methods are proposed for CE study synopsis. For supervised method, a flexible and extensible framework based on HMM is developed to integrate temporal information in CE images. It can be extended to multi-class, multi-features, and multi-states. Improvements can be made by combined HMM and Parallel HMM (PHMM) which are introduced as decision-level fusion schemes. Combined HMM considers different sources via a multi-layer HMM model to perform classification and video synopsis. PHMM employs Bayesian inference to combine the recognition results at decision level. For unsupervised method, illumination-independent opponent color moment invariants and local binary pattern (LBP) based on Contourlet transform are explored as color and texture features, respectively. Pair-wise image dissimilarity is measured in the feature space and treated as points on an open contour in a 2-D plane. CE video is segmented based on sudden change points which are detected using a non-parametric key-point detection method. From each segment, representative frames are extracted to summarize the CE video. Validation results on simulated and real patient data show promising performance of the proposed framework. It has great potential to achieve automatic assessment for CE images. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhao, Qian. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 142-175). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.ii / Acknowledgments --- p.vii / List of Tables --- p.xiii / List of Figures --- p.xv / Chapter 1 --- The Relevance of Synopsis --- p.1 / Chapter 1.1 --- Problem Statement --- p.1 / Chapter 1.2 --- Application - Capsule Endoscopy Assessment --- p.4 / Chapter 1.3 --- Literature Review --- p.9 / Chapter 1.3.1 --- Methods Based on Frame Classification --- p.11 / Chapter 1.3.2 --- Methods Integrating Temporal Information --- p.14 / Chapter 1.4 --- Contributions --- p.19 / Chapter 1.5 --- Organization --- p.23 / Chapter 2 --- Preliminary --- p.25 / Chapter 2.1 --- Hidden Markov Model (HMM) --- p.25 / Chapter 2.2 --- Factorial HMM --- p.35 / Chapter 3 --- Temporal Integration in Capsule Endoscopy Image Analysis --- p.37 / Chapter 3.1 --- Pre-processing --- p.38 / Chapter 3.2 --- Feature Extraction --- p.43 / Chapter 3.3 --- Frame-based Supervised Classification --- p.47 / Chapter 3.3.1 --- Supervised Classification using Individual Frames --- p.47 / Chapter 3.3.2 --- Ensemble Learning Based on AdaBoost --- p.50 / Chapter 3.4 --- Sequence-based Supervised Classification --- p.52 / Chapter 3.5 --- Experiments --- p.58 / Chapter 3.5.1 --- Capsule Endoscopy Image Enhancement --- p.60 / Chapter 3.5.2 --- Frame-based Supervised Classification --- p.67 / Chapter 3.5.3 --- Image Sequence Classification --- p.68 / Chapter 3.6 --- Discussion --- p.80 / Chapter 3.7 --- Summary --- p.82 / Chapter 4 --- Capsule Endoscopy Study Synopsis --- p.98 / Chapter 4.1 --- Supervised Synopsis Using Statistical Models --- p.98 / Chapter 4.2 --- Unsupervised Synopsis via Representative Frame Extraction --- p.100 / Chapter 4.2.1 --- Feature Extraction --- p.100 / Chapter 4.2.2 --- Non-parametric Key-point Detection --- p.111 / Chapter 4.2.3 --- Representative Frame Extraction --- p.112 / Chapter 4.3 --- Experiments --- p.119 / Chapter 4.3.1 --- Supervised Synopsis Based on HMM --- p.119 / Chapter 4.3.2 --- Unsupervised Synopsis --- p.125 / Chapter 4.4 --- Discussion --- p.132 / Chapter 4.5 --- Summary --- p.133 / Chapter 5 --- Conclusions and Future Work --- p.138 / Chapter 5.1 --- Conclusions --- p.138 / Chapter 5.2 --- Future Work --- p.141 / Bibliography --- p.142

Image segmentation is a process to identify regions of interest from digital images. Image segmentation plays an important role in medical image processing which enables a variety of clinical applications. It is also a tool to facilitate the detection of abnormalities such as cancerous lesions in the brain. Although numerous efforts in recent years have advanced this technique, no single approach solves the problem of segmentation for the large variety of image modalities existing today. Consequently, brain MRI segmentation remains a challenging task. The purpose of this thesis is to demonstrate brain MRI segmentation for delineation of tumors, ventricles and other anatomical structures using Insight Segmentation and Registration Toolkit (ITK) routines as the foundation. ITK is an open-source software system to support the Visible Human Project. Visible Human Project is the creation of complete, anatomically detailed, three-dimensional representations of the normal male and female human bodies. Currently under active development, ITK employs leading-edge segmentation and registration algorithms in two, three, and more dimensions. A goal of this thesis is to implement those algorithms to facilitate brain segmentation for a brain cancer research scientist.

A feature extraction approach based on color is firstly proposed. Exploiting color histogram of an image, we can obtain distribution of different colors in images. Then we employ minimum distance classifier based on a new distance criterion to judge status of regions. In this section, we also validate benefits of WCE image enhancement to the proposed CAD system. / Finally, we propose a new approach of chrominance moment as another kind of feature to discriminate normal regions from abnormal regions, which makes full use of Tchebichef polynomials and HSI color space. This new feature extraction scheme preserves illumination invariance without numerical approximation. / In conclusion, this thesis investigates several major and challenging problems such as WCE images enhancement and feature extractions in CAD for WCE images, and proposes several novel schemes to solve those problems. Extensive experiments are reported to demonstrate effectiveness of the proposed algorithms. / Next, we investigate automatic diseases detection for WCE images to partially solve the second problem. In this part we explore different features that are suitable for detection of diseases from three viewpoints, i.e., color, texture and chromaticity, because clinicians mainly use these clues to diagnose. At the same time, we introduce their corresponding classifiers. / We further advance a new texture feature extraction method, curvelet based local binary pattern, to detect abnormal regions in WCE images. This method takes advantage of curvelet transform and local binary pattern to describe textural features of WCE images. / Wireless capsule endoscopy (WCE) is a state-of-the-art technology to diagnose gastrointestinal (GI) tract diseases without invasiveness. However, there exist two major problems concerning WCE images. One problem is that many images for diagnosis have rather low contrast and are noisy, which causes difficulties to diagnosis and also to computer-aided detection, so it is necessary to enhance these images. The other one is that the viewing process of video data per examination is very time consuming because of the great amount of video data. If we can use computerized methods to help the physicians detect some abnormal regions in WCE images, it will certainly reduce the burden of physicians. Focusing on these two goals, this thesis mainly studies some main challenging problems in computer-aided diagnosis (CAD) system for WCE images. To solve the first problem, we put forward an adaptive curvature strength diffusion method to enhance WCE images. Based on local characteristics analysis of WCE images, we propose a new concept of curvature strength. Then, we employ curvature strength diffusion to enhance WCE images with an adaptive choice of conductance parameter. Finally, we extend the curvature strength diffusion to color space since WCE images are color images. / Li, Baopu. / Adviser: Max Q. H. Meng. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3640. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 126-150). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Wang, Zexi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 63-64). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 0 --- Introduction --- p.1 / Chapter 1 --- The Design of the Experiments --- p.4 / Chapter 1.1 --- Flies and the Devices --- p.5 / Chapter 1.2 --- Parameter Settings and Interested Information --- p.8 / Chapter 2 --- Video Processing --- p.11 / Chapter 2.1 --- "Videos, Computer Vision and Image Processing" --- p.11 / Chapter 2.2 --- Details in Video Processing --- p.14 / Chapter 3 --- Data Analysis --- p.20 / Chapter 3.1 --- Background --- p.20 / Chapter 3.2 --- Outline of Data Analysis in Our Project --- p.22 / Chapter 4 --- Effect of the Medicine --- p.25 / Chapter 4.1 --- Hypothesis Testing --- p.26 / Chapter 4.2 --- Two-sample t Test --- p.28 / Chapter 5 --- Significance of the Two Factors --- p.32 / Chapter 5.1 --- Background of ANOVA --- p.33 / Chapter 5.2 --- The Model of ANOVA --- p.35 / Chapter 5.3 --- Two-way ANOVA in Our Data Analysis --- p.42 / Chapter 6 --- Regression Model --- p.45 / Chapter 6.1 --- Background of Regression Analysis --- p.47 / Chapter 6.2 --- Polynomial Regression Models --- p.52 / Chapter 6.2.1 --- Background --- p.52 / Chapter 6.2.2 --- R2 and adjusted R2 --- p.53 / Chapter 6.3 --- Model Verification --- p.58 / Chapter 6.4 --- A Simpler Model As the Other Choice --- p.59 / Chapter 6.5 --- Conclusions --- p.60 / Chapter 7 --- Further Studies --- p.61 / Bibliography --- p.62

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En la actualidad, los avances en los sistemas de captación y procesado de imágenes médicas, permiten dotar a los clínicos de herramientas muy potentes que les ayudan en el desarrollo de su actividad clínica e investigadora. Pero no todas las herramientas comerciales permiten obtener la información necesaria para llevar a cabo ciertas investigaciones. Por esta razón surge la necesidad de realizar un procesado avanzado de ciertas imágenes médicas. Este proyecto trata de desarrollar una serie de herramientas para el análisis computacional basado en imagen cardiaca orientado fundamentalmente al escenario clínico de la insuficiencia cardiaca y la Terapia de Resincronización Cardiaca (TRC). La hipótesis global del proyecto es que el análisis más preciso de la electromecánica a partir del postprocesado de técnicas de imagen cardiaca puede mejorar los resultados clínicos de la TRC.

Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.

Medical image analysis aims at developing techniques to extract information from medical images. Among its many sub-fields, image registration and segmentation are two important topics. In this report, we present four pieces of work, addressing different problems as well as coupling them into a unified framework of shape based image segmentation. Specifically: 1. We link the image registration with the point set registration, and propose a globally optimal diffeomorphic registration technique for point set registration. 2. We propose an image segmentation technique which incorporates the robust statistics of the image and the multiple contour evolution. Therefore, the method is able to simultaneously extract multiple targets from the image. 3. By combining the image registration, statistical learning, and image segmentation, we perform a shape based method which not only utilizes the image information but also the shape knowledge. 4. A multi-scale shape representation based on the wavelet transformation is proposed. In particular, the shape is represented by wavelet coefficients in a hierarchical way in order to decompose the shape variance in multiple scales. Furthermore, the statistical shape learning and shape based segmentation is performed under such multi-scale shape representation framework.

Imaging speed is very important in magnetic resonance imaging (MRI), especially in dynamic cardiac applications, which involve respiratory motion and heart motion. With the introduction of reduced-data MR imaging methods, increasing acquisition speed has become possible without requiring a higher gradient system. But these reduced-data imaging methods carry a price for higher imaging speed. This may be a signal-to-noise ratio (SNR) penalty, reduced resolution, or a combination of both. Many methods sacrifice edge information in favor of SNR gain, which is not preferable for applications which require accurate detection of myocardial boundaries. The central goal of this thesis is to develop novel reduced-data imaging methods to improve reconstructed image performance. This thesis presents a novel reduced-data imaging method, PINOT (Parallel Imaging and NOquist in Tandem), to accelerate MR imaging. As illustrated by a variety of computer simulated and real cardiac MRI data experiments, PINOT preserves the edge details, with flexibility of improving SNR by regularization. Another contribution is to exploit the data redundancy from parallel imaging, rFOV and partial Fourier methods. A Gerchberg Reduced Iterative System (GRIS), implemented with the Gerchberg-Papoulis (GP) iterative algorithm is introduced. Under the GRIS, which utilizes a temporal band-limitation constraint in the image reconstruction, a variant of Noquist called iterative implementation iNoquist (iterative Noquist) is proposed. Utilizing a different source of prior information, first combining iNoquist and Partial Fourier technique (phase-constrained iNoquist) and further integrating with parallel imaging methods (PINOT-GRIS) are presented to achieve additional acceleration gains.

This research centers on the fabrication, characterization, and engineering of inverse opal scaffolds, a novel class of three-dimensional (3D) porous scaffolds made of biocompatible and biodegradable polymers, for applications in tissue engineering and regenerative medicine. The unique features of an inverse opal scaffold include a highly ordered array of pores, uniform and finely tunable pore sizes, high interconnectivity, and great reproducibility. The first part of this work focuses on the fabrication and functionalization of inverse opal scaffolds based on poly(D,L-lactic-co-glycolic acid) (PLGA), a biodegradable material approved by the U.S. Food and Drug Administration (FDA). The advantages of the PLGA inverse opal scaffolds are also demonstrated by comparing with their counterparts with spherical but non-uniform pores and poor interconnectivity. The second part of this work shows two examples where the PLGA inverse opal scaffolds were successfully used as a well-defined system to investigate the effect of pore size of a 3D porous scaffold on the behavior of cell and tissue growth. Specifically, I have demonstrated that i) the differentiation of progenitor cells in vitro was dependent on the pore size of PLGA-based scaffolds and the behavior of the cells was determined by the size of individual pores where the cells resided in, and ii) the neovascularization process in vivo could be directly manipulated by controlling a combination of pore and window sizes when they were applied to a mouse model. The last part of this work deals with the novel application of photoacoustic microscopy (PAM), a volumetric imaging modality recently developed, to tissue engineering and regenerative medicine, in the context of non-invasive imaging and quantification of cells and tissues grown in PLGA inverse opal scaffolds, both in vitro and in vivo. Furthermore, the capability of PAM to monitor and quantitatively analyze the degradation of the scaffolds themselves was also demonstrated.