Applications of Multiresolution-Based Orthogonal Subspace Techniques to the Segmentation of Psoriasis Vulgaris Images

在本論文中，我們提出一種應用多重解析正交次空間投影技術的自動化方法於尋常性乾癬影像分割。由於尋常性乾癬是一種慢性疾病，因此，追蹤病人的病情並選擇適當的療法是很重要的。我們設計的影像分割系統可自動將乾癬影像分割成正常皮膚區域及乾癬區域，以估計某一病人在不同時間乾癬的面積，並提供一定量的治療進展量測。這些資訊可提供客觀的指標給醫生來選擇對某一病人最適當的治療方法，並可避免在評估過程中受到個人因素的影響。 此系統所需的主要的技術包含特徵抽取與影像分類（分割）的方法，我們首先採用模糊紋理光譜以及二維模糊色彩長條圖作為特徵向量來找出影像中的均勻區域，並用這些區域所獲得乾癬與正常皮膚的特徵向量來計算信號矩陣(signature matrix)，以使得正交次空間技術能獲得較精確的分類結果。在本論文中，我採用信號次空間分類器(signature subspace classifier, SSC)、匹配次空間偵測器(matched subspace detector, MSD)、核化信號次空間分類器(kernel signature subspace classifier, KSSC) 及核化匹配次空間偵測器(kernel matched subspace detector, KMSD)四種多光譜影像處理技術來分割尋常性乾癬影像。 信號次空間分類器(SSC)是由估測非抑制最小平方的過程所推導求得，它不是先驗型(a priori model.)分類器，其信號可直接由觀測影像獲得。信號次空間分類器(SSC)首先將一觀測像素分解至信號次空間與雜訊空間，以大幅降低雜訊的影響。再使用非期望信號 (undesired signature) 消除器伴隨匹配濾波器來粹取出期望信號。匹配次空間偵測器(MSD)為另一廣泛被應用至超光譜影像偵測的技術，它可被描述成二位元測試問題的訊號偵測模型。 由於核化分類器可探測被傳統分類器忽略的非線性信號相關性，因此，藉由核化理論，我們將信號次空間分類器(SSC)擴展至對應的核化信號次空間分類器(KSSC)。採用核化技術，可避免在高維度的特徵空間中計算特徵向量的內積，並由核函式(kernel function)來取代。所求得的核化信號次空間分類器(KSSC)等效於在原始空間中的非線性信號次空間分類器(SSC)，為進一步說明核化(kernel-based)分類器的優點，我們也在論文中討論核化匹配次空間偵測器(KMSD)，它是一非線性版本的匹配次空間偵測器(MSD)。 在分類過程中，我們發展了多重解析SSC、MSD、KSSC及KMSD (MSSC、MMSD、MKSSC及MKMSD )以降低計算量。在實驗中，我們採用一相似性函式(similarity function)來執行量化評估，且與著名的LS-SVM方法比較。結果說明本論文所提的核化(kernel-based)技術能有效分割尋常性乾癬影像。In this dissertation, an automatic method is proposed for the segmentation of psoriasis images using multiresolution-based orthogonal subspace techniques. Since psoriasis is a chronic disease, it is important to track the condition of the patient to select a proper treatment. In our design, the psoriasis images are segmented into normal regions and abnormal regions automatically. The areas of each kind of regions of a patient at different moments can then be estimated. The information can be used to give a quantitative measure of the treatment progress. The provided information can avoid the variation of the human factor in the evaluation procedure and offer an objective index for the doctor to select the most suitable treatment for the patient. The essential techniques consist of feature extraction and image segmentation (classification) methods. In this approach, the fuzzy texture spectrum and the two-dimensional fuzzy color histogram in the hue-saturation space are first adopted as the feature vector to locate homogeneous regions in the image. Then these regions are used to compute the signature matrices for the orthogonal subspace techniques to obtain a more accurate segmentation. In this dissertation, we investigate the applications of four multispectral image processing techniques to segment psoriasis images. They are the signature subspace classifier (SSC), matched subspace detector (MSD), kernel signature subspace classifier (KSSC), and kernel matched subspace detector (KMSD). The SSC is derived on the basis of an unconstrained least square estimation such that the signature abundances can be obtained from the observed data rather than assumed to be a priori model. It first decomposes an observed pixel into a signature space and a noise space so that noise effects can be greatly reduced, and then utilizes an undesired signature eliminator followed by a matched filter to extract the desired signature. The MSD is another approach which is widely used in hyperspectral image detection. It is formulated on the basis of a signal detection model that can be described by a binary hypothesis testing problem. Since the kernel-based classifiers can exploit the nonlinear correlations of signatures that are ignored by the conventional classifiers, the SSC is extended to its corresponding kernel version, the kernel SSC (KSSC), by applying the idea of kernel-based learning theory. With the kernel-based technique, the explicit computation in high dimensional feature space can be avoided by replacing it with a kernel function. The obtained KSSC is equivalent to a nonlinear SSC in the input space. To further illustrate the advantage of kernel-based classifiers, the kernel MSD (KMSD), a nonlinear version of the MSD, is also discussed. To reduce the computational requirement in segmentation, the multiresolution-based SSC, MSD, KSSC, and KMSD (MSSC, MMSD, MKSSC, and MKMSD) are developed. In the experiments, the proposed techniques is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed kernel-based techniques can effectively segment psoriasis images.Contents Acknowledgements i 摘 要 ii Abstract iv Contents vi List of Tables viii List of Figures ix Chapter 1 Introduction 1 1.1 Psoriasis 1 1.2 Review of Image Segmentation 2 1.3 Overview of The Proposed Technique 6 1.4 Dissertation Organization 8 Chapter 2 Related Background 9 2.1 Principal Component Models 9 2.1.1 Principal Component Analysis 9 2.1.2 Kernel Principal Component Analysis 10 2.2 Orthogonal Subspace Projection-Based Techniques 16 2.2.1 Orthogonal Subspace Projection 16 2.2.2 Signature Subspace Classifier 18 2.2.3 Matched Subspace Detector 19 Chapter 3 Automatic Selection of Training Regions 21 3.1 Feature Extraction 21 3.1.1 Fuzzy Texture Spectrum 21 3.1.2 Two-Dimensional Fuzzy Color Histogram 28 3.2 Detection of Homogeneous Regions 31 3.3 Classification of Homogeneous Regions 33 Chapter 4 Segmentation Methods 36 4.1 SSC and Kernel SSC Classifiers 36 4.1.1 Modified Signature Subspace Classifier (SSC) 36 4.1.2 Kernel SSC (KSSC) 38 4.2 MSD and Kernel MSD Classifiers 47 4.2.1 Matched Subspace Detector (MSD) 47 4.2.2 Kernel MSD (KMSD) 50 4.3 Multiresolution-Based Classifiers 53 4.4 Complexity Analysis 57 4.5 Sample Selection 58 Chapter 5 Experimental Results and Discussions 60 5.1 Experiments for Computer-Generated Data Sets 60 5.2 Experiments for Real Psoriasis Images 65 5.2.1 Method for Comparison 66 5.2.2 Experiment One on Psoriasis Images 67 5.2.3 Experiment Two on Psoriasis Images 79 Chapter 6 Conclusions 93 References 9

Purification of Colon Carcinoma Cells from Primary Colon Tumor Using a Filtration Method via Porous Polymeric Filters

Cancer stem cells (CSCs) or cancer-initiating cells (CICs) are key factors for tumor generation and metastasis. We investigated a filtration method to enhance CSCs (CICs) from colon carcinoma HT-29 cells and primary colon carcinoma cells derived from patient colon tumors using poly(lactide-co-glycolic acid)/silk screen (PLGA/SK) filters. The colon carcinoma cell solutions were permeated via porous filters to obtain a permeation solution. Then, the cell cultivation media were permeated via the filters to obtain the recovered solution, where the colon carcinoma cells that adhered to the filters were washed off into the recovered solution. Subsequently, the filters were incubated in the culture media to obtain the migrated cells via the filters. Colon carcinoma HT-29 cells with high tumorigenicity, which might be CSCs (CICs), were enhanced in the cells in the recovered solution and in the migrated cells based on the CSC (CIC) marker expression, colony-forming unit assay, and carcinoembryonic antigen (CEA) production. Although primary colon carcinoma cells isolated from colon tumor tissues contained fibroblast-like cells, the primary colon carcinoma cells were purified from fibroblast-like cells by filtration through PLGA/SK filters, indicating that the filtration method is effective in purifying primary colon carcinoma cells

Low-Threshold Plasmonic Lasers on a Single-Crystalline Epitaxial Silver Platform at Telecom Wavelength

We report on the first demonstration of metal–insulator–semiconductor-type plasmonic lasers at the telecom wavelength (∼1.3 μm) using top-down fabricated semiconductor waveguides on single-crystalline metallic platforms formed using epitaxially grown Ag films. The critical role of the Ag film thickness in sustaining plasmonic lasing at the telecom wavelength is investigated systematically. Low-threshold (0.2 MW/cm²) and continuous-wave operation of plasmonic lasing at cryogenic temperatures can be achieved on a 150 nm Ag platform with minimum radiation leakage into the substrate. Plasmonic lasing occurs preferentially through higher-order surface-plasmon-polariton modes, which exhibit a higher mode confinement factor, lower propagation loss, and better field–gain coupling. We observed plasmonic lasing up to ∼200 K under pulsed excitations. The plasmonic lasers on large-area epitaxial Ag films open up a scalable platform for on-chip integrations of plasmonics and optoelectronics at the telecom wavelength