by Ng Chung Ying Davy. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 91-103). / ACKNOWLEDGMENT --- p.1 / ABSTRACT --- p.2 / Chapter CHAPTER 1 : --- INTRODUCTION --- p.3 / Chapter CHAPTER 2 : --- LITERATURE REVIEW --- p.5 / Chapter 2.1. --- The History of Laser --- p.5 / Chapter 2.1.1 --- Pre-Maser History (1917 - 1952) --- p.5 / Chapter 2.1.2. --- The Development of the Maser (1953 - 1959) --- p.6 / Chapter 2.1.3. --- The Development of the Laser (1960 - 1961) --- p.7 / Chapter 2.1.4. --- Expanding the Laser Concept (1962 - 1966) --- p.8 / Chapter 2.1.5. --- New Laser Development (1967 - at present) --- p.11 / Chapter 2.2. --- Basic Laser System --- p.11 / Chapter 2.2.1. --- The Active Laser Media --- p.12 / Chapter 2.2.2. --- The Source of Excitation Energy --- p.12 / Chapter 2.2.3. --- A Febry-Perot Interferometer --- p.13 / Chapter 2.3. --- Basic operational principles of laser --- p.13 / Chapter 2.4. --- Characteristics of Laser Radiation --- p.14 / Chapter 2.5. --- Laser Reactions in Living Tissue --- p.15 / Chapter 2.5.1. --- Laser Interactions with the Eye --- p.15 / Chapter 2.5.2. --- Laser Interaction with the Skin and its Appendages --- p.16 / Chapter 2.5.3. --- Laser Interaction with other tissues --- p.16 / Chapter 2.6. --- Thermal Effect of Laser Irradiation of Living Tissues --- p.20 / Chapter 2.7. --- Medical lasers and their applications --- p.21 / Chapter 2.7.1. --- Neodymium-doped yttrium-aluminium- garnet (Nd:YAG) laser --- p.21 / Chapter 2.7.2. --- Argon laser --- p.21 / Chapter 2.7.3. --- Carbon dioxide laser --- p.22 / Chapter 2.7.4. --- Tunable dye laser --- p.22 / Chapter 2.7.5. --- Gold Vapour Laser --- p.23 / Chapter 2.7.6. --- Copper Vapour Laser --- p.23 / Chapter 2.8. --- Port-wine stains --- p.24 / Chapter 2.8.1. --- Pathology of port-wine stains --- p.24 / Chapter 2.8.2. --- Treatment of port-wine stains by lasers --- p.24 / Chapter 2.9. --- Laser Therapy in the Gastrointestinal Tract --- p.28 / Chapter 2.10. --- Esophageal varices --- p.30 / Chapter 2.10.1. --- Etiology of oesophageal varices --- p.30 / Chapter 2.10.2. --- Pathology of oesophageal varices --- p.31 / Chapter 2.10.3. --- Diagnosis of esophageal varices --- p.31 / Chapter 2.10.4. --- Treatment of esophageal varices --- p.33 / Chapter CHAPTER 3 : --- ANIMAL EXPERIMENTS --- p.42 / Chapter 3.1. --- General Materials --- p.42 / Chapter 3.1.1. --- Equipment --- p.43 / Chapter 3.1.2. --- Drugs and chemical --- p.47 / Chapter 3.1.3. --- Laboratory Animals --- p.48 / Chapter 3.1.4. --- Surgical Instruments --- p.49 / Chapter 3.1.7. --- Disposable --- p.49 / Chapter 3.1.8. --- Sutures --- p.49 / Chapter 3.1.9. --- Histopathological Preparation --- p.50 / Chapter 3.2. --- Experiment 1 : Iatrogenic perforation of the rat's stomach by the continues application of the Copper Vapour Laser at different power level --- p.51 / Chapter 3.2.1. --- Introduction --- p.51 / Chapter 3.2.2. --- Methods --- p.52 / Chapter 3.2.3. --- Results --- p.55 / Chapter 3.2.4. --- Discussion --- p.60 / Chapter 3.3. --- Experiment 2 : Obliteration of the rats' mesenteric vein with the Copper Vapour Laser --- p.61 / Chapter 3.3.1. --- Introduction --- p.61 / Chapter 3.3.2. --- Method --- p.62 / Chapter 3.3.3. --- Results --- p.63 / Chapter 3.3.4. --- Discussion --- p.65 / Chapter CHAPTER 4 : --- CONCLUSION --- p.67 / REFERENCES --- p.91

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

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.