title: Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques.
abstract: 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.
authors.affiliation: Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan, ROC.
authors.forename: J S
authors.initials: JS
authors.lastname: Taur
authors.forename: G H
authors.initials: GH
authors.lastname: Lee
authors.forename: C W
authors.initials: CW
authors.lastname: Tao
authors.forename: C C
authors.initials: CC
authors.lastname: Chen
authors.forename: C W
authors.initials: CW
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods*; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011565:Psoriasis / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity