Texture in Skin Images: Comparison of Three Methods to Determine Smoothness
Abstract
Smooth texture, a critical feature in skin tumor diagnosis, is analyzed using three texture measurement methods. A dermatologist classified 1290 small blocks within 42 tumor images as smooth, partially smooth, or nonsmooth. Texture discriminatory power of three methods were compared: the neighboring gray-level dependence matrix (NGLDM) method of Sun and Wee, the circular symmetric autoregressive random field model of Kashyap and Khotanzad, and a new peak-variance method. The texture analysis method that allows best prediction of smoothness for our tumor domain is the NGLDM method, affording 98% correct prediction of a smooth block with 21% false positives. We discuss applicability of texture analysis to dermatology.
Recommended Citation
W. V. Stoecker et al., "Texture in Skin Images: Comparison of Three Methods to Determine Smoothness," Computerized Medical Imaging and Graphics, vol. 16, no. 3, pp. 179 - 190, Elsevier, May 1992.
The definitive version is available at https://doi.org/10.1016/0895-6111(92)90072-H
Department(s)
Chemistry
Second Department
Electrical and Computer Engineering
Sponsor(s)
National Science Foundation (U.S.). Small Business Research Innovation Program
Keywords and Phrases
Biological Materials - Textures; Biomedical Engineering - Diagnosis; Biomedical Engineering - Oncology; Image Processing - Image Analysis; Surfaces - Roughness Measurement; Circular Symmetric Autoregressive Random Field Model; Neighboring Gray Level Dependence Matrix (NGLDM); New Peak Variance Method; Skin Tumor Diagnosis; Texture Analysis; Biological Materials; Article; Cancer Classification; Cancer Diagnosis; Computer Graphics; Dermatology; Diagnostic Imaging; Digital Computer; Image Analysis; Medical Photography; Medical Technology; Melanoma; Priority Journal; Skin Cancer; Algorithms; Comparative Study; Diagnosis, Computer-Assisted; Human; Image Processing, Computer-Assisted; Models, Biological; Palpation; Photography; Predictive Value Of Tests; Signal Processing, Computer-Assisted; Skin Neoplasms; Support, U.S. Gov't, Non-P.H.S.; Computer Diagnosis; Skin Imaging; Smooth
International Standard Serial Number (ISSN)
0895-6111; 1879-0771
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 1992 Elsevier, All rights reserved.
Publication Date
01 May 1992
PubMed ID
1623493
Comments
This research was supported by the National Science Foundation small business innovation research grant ISI 8521284.