BACKGROUND: Previous studies have successfully classified 86% of malignant melanomas using a relative-color segmentation method, by feature extraction from photographic images in the automatic identification of skin tumors. These studies were extended by applying the relative-color method to dermoscopic images of melanoma grouped with melanoma in situ and clark nevus lesions in dermoscopic images allow more control over lighting variations, which contribute to lesion misclassification. Dermoscopic images then enable a more detailed examination of the structure of skin lesions, provide much more structural detail within lesions, and contain visual information that cannot be seen in photographic images. This present work extends the previous studies by applying relative-color feature extraction to dermoscopic images to differentiate among melanoma, seborrheic keratoses and Reed/Spitz nevi. OBJECTIVE: To develop a method for automatically differentiating among malignant melanoma, seborrheic keratoses and Reed/Spitz nevi, using digitized, color, dermoscopic images. METHODS: Images underwent preprocessing, tumor segmentation, feature extraction and tumor classification. The relative-color method was used in the segmentation stage. Classification was accomplished by taking the inner products of model tumor feature vectors with test-image tumor vectors followed by the nearest-neighbor classification method. RESULTS: The classification rates of melanoma, seborrheic keratoses and Reed/Spitz nevi images mixed together, were 60%, 58.3% and 80%, respectively. Classification of melanoma and Reed/Spitz nevi mixed, were 70% and 90%, respectively. Classification rates were the best when melanoma was being differentiated from seborrheic keratoses. These rates were 100% and 88.9%, respectively. CONCLUSION: Dermoscopic rather than photographic images were preprocessed, using a hair-removal technique. They were then converted to relative-color images, which were segmented using the principal components transform and median split, followed by morphological filtering. After processing, the multi-dimensional tumor feature space described herein was used to differentiate the tumors. The high success rates for differentiating seborrheic keratoses from melanoma show that the use of dermoscopic images has a strong promise in enabling prescreening, as well as automated assistance and significant improvement in tumor diagnosis in clinics.
BACKGROUND: Previous studies have successfully classified 86% of malignant melanomas using a relative-color segmentation method, by feature extraction from photographic images in the automatic identification of skin tumors. These studies were extended by applying the relative-color method to dermoscopic images of melanoma grouped with melanoma in situ and clark nevus lesions in dermoscopic images allow more control over lighting variations, which contribute to lesion misclassification. Dermoscopic images then enable a more detailed examination of the structure of skin lesions, provide much more structural detail within lesions, and contain visual information that cannot be seen in photographic images. This present work extends the previous studies by applying relative-color feature extraction to dermoscopic images to differentiate among melanoma, seborrheic keratoses and Reed/Spitz nevi. OBJECTIVE: To develop a method for automatically differentiating among malignant melanoma, seborrheic keratoses and Reed/Spitz nevi, using digitized, color, dermoscopic images. METHODS: Images underwent preprocessing, tumor segmentation, feature extraction and tumor classification. The relative-color method was used in the segmentation stage. Classification was accomplished by taking the inner products of model tumor feature vectors with test-image tumor vectors followed by the nearest-neighbor classification method. RESULTS: The classification rates of melanoma, seborrheic keratoses and Reed/Spitz nevi images mixed together, were 60%, 58.3% and 80%, respectively. Classification of melanoma and Reed/Spitz nevi mixed, were 70% and 90%, respectively. Classification rates were the best when melanoma was being differentiated from seborrheic keratoses. These rates were 100% and 88.9%, respectively. CONCLUSION: Dermoscopic rather than photographic images were preprocessed, using a hair-removal technique. They were then converted to relative-color images, which were segmented using the principal components transform and median split, followed by morphological filtering. After processing, the multi-dimensional tumor feature space described herein was used to differentiate the tumors. The high success rates for differentiating seborrheic keratoses from melanoma show that the use of dermoscopic images has a strong promise in enabling prescreening, as well as automated assistance and significant improvement in tumor diagnosis in clinics.
Authors: Lavinia Ferrante di Ruffano; Yemisi Takwoingi; Jacqueline Dinnes; Naomi Chuchu; Susan E Bayliss; Clare Davenport; Rubeta N Matin; Kathie Godfrey; Colette O'Sullivan; Abha Gulati; Sue Ann Chan; Alana Durack; Susan O'Connell; Matthew D Gardiner; Jeffrey Bamber; Jonathan J Deeks; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04