黑色素瘤轮廓的不规则性是区别于良性皮肤痣的重要临床特征之一，研究皮肤肿瘤轮廓的不规则性描述对黑色素瘤计算机辅助早期诊断和治疗具有重要意义。传统分形维（FD）利用自相似度量表示轮廓的不规则性，但作为全局特征，分类能力较弱。为了探索新的多尺度下轮廓不规则程度的描述方法。提出了高斯滤波和局部分形维相结合的度量模型：两种新的轮廓结构不规则性度量和多尺度下轮廓不规则性特征描述。后者优点在于提供了图像空间不同尺度下轮廓不规则性度量的特征簇。实验分析表明，该轮廓不规则性描述子不仅增强了轮廓复杂性的细节表述，而且不同尺度提取的轮廓不规则度统计特征可有效甄别黑色素瘤和良性皮肤肿瘤。

Research on CAD based boundary-feature descriptions of skin lesions for early diagnosis and medical treatments are crucial as boundary irregularity of melanomas is one of the most important clinical indicators to discriminate lesiens from other benign moles. Conventional fractal dimension（FD） is utilized to describe boundary complexity based on self-similarity measures. The criminative power of FDs is low due to its global property and lack of detail information. The objective of the paper is to explore new irregularity descriptors of object boundaries at different scales. In the paper, there are two novel measures: the irregularities of boundary structures and multi-scale features related to boundary roughness are described using an integrated quantitative model combining Gaussian filtering and local FDs. The advantage of the latter approach is that feature clusters of boundary irregularity are formed at different scales in the image space, so that the extracted measurements from multi-scale descriptions and different viewpoints（statistics and boundary-like roughness） can provide effective descriptors compared to the conventional FD and other boundary based methods and will be helpful for further classification tasks. Experiments show that the proposed irregular descriptors not only enhance fine expressions of boundary complexity but also can effectively discriminate melanomas among moles using statistical features of the boundary roughness at different scales.