Open AccessColour histogram analysis for melanoma discrimination in clinical images
Author(s) -
Faziloglu Yunus,
Stanley R. Joe,
Moss Randy H.,
Van Stoecker William,
McLean Rob P.
Publication year - 2003
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 - 10.1034/j.1600-0846.2003.00030.x
Subject(s) - melanoma , lesion , medicine , seborrheic keratosis , dermatology , histogram , feature (linguistics) , pathology , artificial intelligence , computer science , image (mathematics) , linguistics , philosophy , cancer research
Background: Malignant melanoma, the most deadly form of skin cancer, has a good prognosis if treated in the curable early stages. Colour provides critical discriminating information for the diagnosis of malignant melanoma. Methods: This research introduces a three‐dimensional relative colour histogram analysis technique to identify colours characteristic of melanomas and then applies these ‘melanoma colours’ to differentiate benign skin lesions from melanomas. The relative colour of a skin lesion is determined based on subtracting a representative colour of the surrounding skin from each lesion pixel. A colour mapping for ‘melanoma colours’ is determined using a training set of images. A percent melanoma colour feature, defined as the percentage of the lesion pixels that are melanoma colours, is used for discriminating melanomas from benign lesions. The technique is evaluated using a clinical image data set of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi. Results: Using the percent melanoma colour feature for discrimination, experimental results yield correct melanoma and benign lesion discrimination rates of 84.3 and 83.0%, respectively. Conclusions: The results presented in this work suggest that lesion colour in clinical images is strongly related to the presence of melanoma in that lesion. However, colour information should be combined with other information in order to further reduce the false negative and false positive rates.