Please use this identifier to cite or link to this item:
|標題:||Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques||作者:||Taur, J.S.
|關鍵字:||fuzzy texture spectrum;image segmentation;signature subspace;classifier;vector machine classifiers;projection approach;texture classification;neural-networks;region;information;extraction;histogram||Project:||Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics||期刊/報告no：:||Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, Volume 36, Issue 2, Page(s) 390-402.||摘要:||
In this paper, a method is proposed for the segmentation of color images using a multiresolution-based signature subspace classifier (MSSC) with application to psoriasis images. 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 classifier to obtain a more accurate segmentation. To reduce the computational requirement, the MSSC has been developed. In the experiments, the method is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed algorithm can effectively segment psoriasis images. The proposed approach can also be applied to general color texture segmentation applications.
|Appears in Collections:||電機工程學系所|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.