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.
翻译:皮肤损伤定义边界线的准确性对于随后的算法(如分类)和计算机辅助严重病情早期诊断至关重要。本文件建议采用基于本地二元模式(LBP)的新型分解方法,将LBP和K-Means群集结合起来,以在脱温图像中实现详细的分解。与通常的皮肤学家相似的分解(即现有的地面图象)相比,拟议方法能够找到更现实的皮肤损伤边界,即更加详细得多。结果还显示不同性能衡量尺度之间的变异性较小,而且它们在不同图像之间是一致的。拟议方法可以用于细胞型分解,例如适合腐蚀边界生长特性的分解。因此,该方法适合于跟踪与皮肤线性图象的利奥边界几何测量有关的生长动态。