Background/Purpose: Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature of these early melanomas. If rapid and accurate automatic detection of pink color in these melanomas could be accomplished, there could be significant public health benefits.
Methods: Detection of three shades of pink (light pink, dark pink, and orange pink) was accomplished using color analysis techniques in five color planes (red, green, blue, hue, and saturation). Color shade analysis was performed using a logistic regression model trained with an image set of 60 dermoscopic images of melanoma that contained pink areas. Detected pink shade areas were further analyzed with regard to the location within the lesion, average color parameters over the detected areas, and histogram texture features.
Results: Logistic regression analysis of a separate set of 128 melanomas and 128 benign images resulted in up to 87.9% accuracy in discriminating melanoma from benign lesions measured using area under the receiver operating characteristic curve. The accuracy in this model decreased when parameters for individual shades, texture, or shade location within the lesion were omitted.
Conclusion: Texture, color, and lesion location analysis applied to multiple shades of pink can assist in melanoma detection. When any of these three details: color location, shade analysis, or texture analysis were omitted from the model, accuracy in separating melanoma from benign lesions was lowered. Separation of colors into shades and further details that enhance the characterization of these color shades are needed for optimal discrimination of melanoma from benign lesions.
R. Kaur et al., "Real-Time Supervised Detection of Pink Areas in Dermoscopic Images of Melanoma: Importance of Color Shades, Texture and Location," Skin Research and Technology, vol. 21, no. 4, pp. 466-473, John Wiley & Sons, Nov 2015.
The definitive version is available at https://doi.org/10.1111/srt.12216
Electrical and Computer Engineering
National Institutes of Health (U.S.). Small Business Innovation Research Program
Keywords and Phrases
Color; Computer Aided Analysis; Computer Aided Diagnosis; Computer Vision; Dermatology; Diagnosis; Location; Oncology; Public Health; Regression Analysis; Separation; Color Analysis; Color Shade; Dermoscopy; Melanoma; Pink Area; Image Analysis; Article; Camera; Cancer Diagnosis; Clinical Trial; Controlled Study; Dermatoscope; Entropy; Epiluminescence Microscopy; Histogram; Human; Receiver Operating Characteristic; Shade; Time; Algorithm; Automated Pattern Recognition; Colorimetry; Computer Assisted Diagnosis; Computer System; Evaluation Study; Pathology; Procedures; Reproducibility; Sensitivity And Specificity; Skin Pigmentation; Skin Tumor; Supervised Machine Learning; Computer Systems; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility Of Results; Skin Neoplasms; Supervised Machine Learning; Color Shade Detection; Machine Vision
International Standard Serial Number (ISSN)
Article - Journal
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