Detection of Pigment Network in Dermatoscopy Images Using Texture Analysis
Abstract
Dermatoscopy, also known as dermoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such prominent feature is the pigment network. Two texture-based algorithms are developed for the detection of pigment network. These methods are applicable to various texture patterns in dermatoscopy images, including patterns that lack fine lines such as cobblestone, follicular, or thickened network patterns. Two texture algorithms, Laws energy masks and the neighborhood gray-level dependence matrix (NGLDM) large number emphasis, were optimized on a set of 155 dermatoscopy images and compared. Results suggest superiority of Laws energy masks for pigment network detection in dermatoscopy images. For both methods, a texel width of 10 pixels or approximately 0.22 mm is found for dermatoscopy images.
Recommended Citation
M. Anantha et al., "Detection of Pigment Network in Dermatoscopy Images Using Texture Analysis," Computerized Medical Imaging and Graphics, Elsevier, Jan 2004.
The definitive version is available at https://doi.org/10.1016/j.compmedimag.2004.04.002
Department(s)
Chemistry
Second Department
Electrical and Computer Engineering
Sponsor(s)
National Institutes of Health (U.S.)
International Standard Serial Number (ISSN)
0895-6111
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2004 Elsevier, All rights reserved.
Publication Date
01 Jan 2004
