A multiscale algorithm for image segmentation by variational method
SIAM Journal on Numerical Analysis
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Extracting Meaningful Curves from Images
Journal of Mathematical Imaging and Vision
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Blotch Detection in Pigmented Skin Lesions Using Fuzzy Co-clustering and Texture Segmentation
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Pattern classification of dermoscopy images: A perceptually uniform model
Pattern Recognition
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
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In this paper we propose a machine learning approach to classify melanocytic lesions as malignant or benign, using dermoscopic images. The lesion features used in the classification framework are inspired on border, texture, color and structures used in popular dermoscopy algorithms performed by clinicians by visual inspection. The main weakness of dermoscopy algorithms is the selection of a set of weights and thresholds, that appear not to be robust or independent of population. The use of machine learning techniques allows to overcome this issue. The proposed method is designed and tested on an image database composed of 655 images of melanocytic lesions: 544 benign lesions and 111 malignant melanoma. After an image pre-processing stage that includes hair removal filtering, each image is automatically segmented using well known image segmentation algorithms. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters. The detection of particular dermoscopic patterns associated with melanoma is also addressed, and its inclusion in the classification framework is discussed. The learning and classification stage is performed using AdaBoost with C4.5 decision trees. For the automatically segmented database, classification delivered a specificity of 77% for a sensitivity of 90%. The same classification procedure applied to images manually segmented by an experienced dermatologist yielded a specificity of 85% for a sensitivity of 90%.