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Applications of Multiresolution-Based Orthogonal Subspace Techniques to the Segmentation of Psoriasis Vulgaris Images
|關鍵字:||Fuzzy texture spectrum;模糊紋理光譜;image segmentation;signature subspace classifier;影像分割;信號次空間分類器||出版社:||電機工程學系所||引用:|| T. P. Habif, Clinical Dermatology, The C.V. Mosby Company, 2nd ed., 1990.  J. S. Taur, C. W. Tao, C. C. Chen, and C. W. Yang, “Segmentation of psoriasis vulgaris images using orthogonal subspace techniques,” In Proceedings, The 7th Conference on Artificial Intelligence and Applications, pp. 667-671, Nov. 2002.  J. S. Taur,“Neural-fuzzy approach to the segmentation of psoriasis images,” Journal of VLSI Signal Processing, vol. 35, no.1, pp. 19-27, Feb. 2003.  J. S. Taur, G. H. Lee, C. W. Ta, C. C. Chen, and C. W. Yang, “Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques,” IEEE Trans. On System, Man and Cybern. Part-B, vol. 36, no. 2, pp. 390-402, Apr. 2006.  R.M. Haralick and L.G. Shapiro, “Image segmentation techniques,” Comput. Vis. Graph. Im. Proc., vol. 29, pp.100-132, 1985.  N.R. Pal and S.K. 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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。
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.
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