Computational texture features of dermoscopic images and their link to the descriptive terminology: A survey.

Computational texture features of dermoscopic images and their link to the descriptive terminology: A survey.

Publication date: Dec 01, 2019

Computer-extracted texture features are relevant to diagnose cutaneous lesions such as melanomas. Our goal is to set a relationship between a well-established descriptive terminology, which describes the attributes of dermoscopic structures based on their aspect rather than their underlying causes, and the computational methods to extract texture-based features. By tackling this problem, we can ascertain what indicators used by dermatologists are reflected in the extracted texture features. We first review the state-of-the-art models for texture extraction in dermoscopic images. By comparing the methods’ performance and goals, we conclude that (I) a single color space does not seem to give performances as good as using several ones, thus the latter is reasonable (II) the optimal number of extracted features seems to vary depending on the method’s goal, and extracting a large number of features can lead to a loss of models robustness (III) methods such as GLCM, Sobel or Law energy filters are mainly used to capture local properties to detect specific dermoscopic structures (IV) methods that extract local and global features, like Gabor wavelets or SPT, tend to be used to analyze the presence of certain patterns of dermoscopic structures, e.g. globular, reticular, etc.

Talavera-Mart’inez, L., Bibiloni, P., and Gonz’alez-Hidalgo, M. Computational texture features of dermoscopic images and their link to the descriptive terminology: A survey. 24968. 2019 Comput Methods Programs Biomed (182):

Concepts Keywords
Color Space Energy filters
Energy Computer graphics
Melanomas Dermatoscopy
Robustness Microscopy
Survey Melanoma
Wavelets Texture mapping
Medicine
Image processing

Semantics

Type Source Name
disease MESH melanomas
pathway KEGG Melanoma

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