title: Structure similarity-guided image binarization for automatic segmentation of epidermis surface microstructure images.
abstract: Partitioning epidermis surface microstructure (ESM) images into skin ridge and skin furrow regions is an important preprocessing step before quantitative analyses on ESM images. Binarization segmentation is a potential technique for partitioning ESM images because of its computational simplicity and ease of implementation. However, even for some state-of-the-art binarization methods, it remains a challenge to automatically segment ESM images, because the grey-level histograms of ESM images have no obvious external features to guide automatic assessment of appropriate thresholds. Inspired by human visual perceptual functions of structural feature extraction and comparison, we propose a structure similarity-guided image binarization method. The proposed method seeks for the binary image that best approximates the input ESM image in terms of structural features. The proposed method is validated by comparing it with two recently developed automatic binarization techniques as well as a manual binarization method on 20 synthetic noisy images and 30 ESM images. The experimental results show: (1) the proposed method possesses self-adaption ability to cope with different images with same grey-level histogram; (2) compared to two automatic binarization techniques, the proposed method significantly improves average accuracy in segmenting ESM images with an acceptable decrease in computational efficiency; (3) and the proposed method is applicable for segmenting practical EMS images. (Matlab code of the proposed method can be obtained by contacting with the corresponding author.).
authors.affiliation: Institute of Intelligent Vision and Image Information, China Three Gorges University, Hubei, China.|Group for Biomedical Imaging and Bioinformatics, China Three Gorges University, Hubei, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Zou
authors.affiliation: Centre for Microscopy Analysis, China Three Gorges University, Hubei, China.|Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China.
authors.forename: B
authors.initials: B
authors.lastname: Lei
authors.affiliation: Institute of Intelligent Vision and Image Information, China Three Gorges University, Hubei, China.
authors.forename: F
authors.initials: F
authors.lastname: Dong
authors.affiliation: Institute of Intelligent Vision and Image Information, China Three Gorges University, Hubei, China.|Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China.
authors.forename: G
authors.initials: G
authors.lastname: Xu
authors.affiliation: Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China.
authors.forename: S
authors.initials: S
authors.lastname: Sun
authors.affiliation: Centre for Microscopy Analysis, China Three Gorges University, Hubei, China.|Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Hubei, China.
authors.forename: P
authors.initials: P
authors.lastname: Xia
mesh_terms: D057205:Automation, Laboratory / Q000379:methods*; D004817:Epidermis / Q000648:ultrastructure*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D061848:Optical Imaging / Q000379:methods*; D013499:Surface Properties
keywords: Epidermis surface microstructure image; human visual perception; image binarization/thresholding; image segmentation; multiscale gradient multiplication; structure similarity-guided