  Segmentation is a key stage in dermoscopic image processing, where the
accuracy of the border line that defines skin lesions is of utmost importance
for subsequent algorithms (e.g., classification) and computer-aided early
diagnosis of serious medical conditions. This paper proposes a novel
segmentation method based on Local Binary Patterns (LBP), where LBP and K-Means
clustering are combined to achieve a detailed delineation in dermoscopic
images. In comparison with usual dermatologist-like segmentation (i.e., the
available ground-truth), the proposed method is capable of finding more
realistic borders of skin lesions, i.e., with much more detail. The results
also exhibit reduced variability amongst different performance measures and
they are consistent across different images. The proposed method can be applied
for cell-based like segmentation adapted to the lesion border growing
specificities. Hence, the method is suitable to follow the growth dynamics
associated with the lesion border geometry in skin melanocytic images.
