Colour Histogram Analysis for Melanoma Discrimination in Clinical Images
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.
Y. Faziloglu et al., "Colour Histogram Analysis for Melanoma Discrimination in Clinical Images," Skin Research and Technology, vol. 9, no. 2, pp. 147-155, Wiley-Blackwell, Apr 2003.
The definitive version is available at https://doi.org/10.1034/j.1600-0846.2003.00030.x
Electrical and Computer Engineering
Keywords and Phrases
Analytic Method; Benign Tumor; Color Discrimination; Data Analysis; Diagnostic Value; Experimentation; Histogram; Human; Image Analysis; Image Processing; Medical Research; Melanoma; Nevus Cell; Seborrheic Keratosis; Skin Color; Skin Defect; Three Dimensional Imaging; Tumor Differentiation; Algorithms; Colorimetry; Diagnosis; Differential; Image Interpretation; Computer-assisted; Keratosis; Seborrheic; Melanoma; Nevus; Photography; Reproducibility of Results; Sensitivity and Specificity; Skin Neoplasms
International Standard Serial Number (ISSN)
Article - Journal
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