C4.5: programs for machine learning
C4.5: programs for machine learning
Computer Vision
Line Detection and Texture Characterization of Network Patterns
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
MEMEA '06 Proceedings of the IEEE International Workshop on Medical Measurement and Applications, 2006. MeMea 2006.
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
Automatic Diagnosis of Melanoma: A Software System Based on the 7-Point Check-List
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Modeling the dermoscopic structure pigment network using a clinically inspired feature set
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
Generalizing Common Tasks in Automated Skin Lesion Diagnosis
IEEE Transactions on Information Technology in Biomedicine
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
Oriented pattern analysis for streak detection in dermoscopy images
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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By means of this study, a detection algorithm for the ''pigment network'' in dermoscopic images is presented, one of the most relevant indicators in the diagnosis of melanoma. The design of the algorithm consists of two blocks. In the first one, a machine learning process is carried out, allowing the generation of a set of rules which, when applied over the image, permit the construction of a mask with the pixels candidates to be part of the pigment network. In the second block, an analysis of the structures over this mask is carried out, searching for those corresponding to the pigment network and making the diagnosis, whether it has pigment network or not, and also generating the mask corresponding to this pattern, if any. The method was tested against a database of 220 images, obtaining 86% sensitivity and 81.67% specificity, which proves the reliability of the algorithm.