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中文摘要：
提出基于点特异度和自适应分类策略的血管分割方法（SSVD,specificity and self-adaptive vessel detection）,首先给出点特异度的定义,通过设置高点特异度阈值,实现主血管的提取,然后由多主体进行自适应像素分类,将每个未确定像素作为一个Agent,在多尺度点特异度阈值范围内,根据邻域Agent状态修订自身状态,逐步完成对像素的分类,最后通过多窗口去噪对噪音进行滤除完成对图像血管结构的分割。将SSVD方法应用到DRIVE数据库眼底图像的血管分割中,实验结果表明该方法要比现有其他方法具有更高的准确度和效率。
英文摘要：
A new vessel segmentation method called specificity and self-adaptive vessel detection（SSVD） was proposed based on pixel specificity and self-adaptive classification strategy, in the beginning pixel specificity was defined, by set- ting a higher pixel specificity threshold, the main vessel skeleton was extracted; then self-adaptive classification process was implemented, and each of the remaining undetermined pixels acted as an Agent, within a multi-scale threshold range, Agent revised its own status according to the status of its neighbor, so as to complete the classification of the pixels; fi- nally the noise was removed by multi-window noise filtering method. By testing SSVD on DRIVE database, the experi- ment shows that it is more accurate and efficient than state-of-the-art methods.
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