Open AccessDetection of atypical texture features in early malignant melanoma
Author(s) -
Shrestha Bijaya,
Bishop Joseph,
Kam Keong,
Chen Xiaohe,
Moss Randy H.,
Stoecker William V.,
Umbaugh Scott,
Stanley R. Joe,
Celebi M. Emre,
Marghoob Ashfaq A.,
Argenziano Giuseppe,
Soyer H. Peter
Publication year - 2010
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2009.00402.x
Subject(s) - texel , texture (cosmology) , melanoma , pixel , artificial intelligence , correlation , medicine , computer science , pattern recognition (psychology) , computer vision , dermatology , mathematics , image (mathematics) , cancer research , geometry
Background: The presence of an atypical (irregular) pigment network (APN) can indicate a diagnosis of melanoma. This study sought to analyze the APN with texture measures. Methods: For 106 dermoscopy images including 28 melanomas and 78 benign dysplastic nevi, the areas of APN were selected manually. Ten texture measures in the CVIPtools image analysis system were applied. Results: Of the 10 texture measures used, correlation average provided the highest discrimination accuracy, an average of 95.4%. Discrimination of melanomas was optimal at a pixel distance of 20 for the 768 × 512 images, consistent with a melanocytic lesion texel size estimate of 4–5 texels per mm. Conclusion: Texture analysis, in particular correlation average at an optimized pixel spacing, may afford automatic detection of an irregular pigment network in early malignant melanoma.