title: 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.
authors.affiliation: Department of Internal Medicine, University of Missouri-Columbia 65212.
authors.forename: W V
authors.initials: WV
authors.lastname: Stoecker
authors.forename: C S
authors.initials: CS
authors.lastname: Chiang
authors.forename: R H
authors.initials: RH
authors.lastname: Moss
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010173:Palpation; D010781:Photography / Q000379:methods; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D012878:Skin Neoplasms / Q000145:classification*