Ji Q, Engel J, Craine E
Department of Computer Science, University of Nevada, Reno 89557, USA.
IEEE Trans Med Imaging. 2000 Nov;19(11):1144-9. doi: 10.1109/42.896790.
This paper presents a generalized statistical texture analysis technique for characterizing and recognizing typical, diagnostically most important, vascular patterns relating to cervical lesions from colposcopic images. The contributions of the research include: 1) the introduction of a generalized texture analysis technique based on the combination of the conventional statistical and structural textural analysis approaches by using a statistical description of geometric primitives; 2) the introduction of a set of textural measures that capture the specific characteristics of cervical textures as perceived by human. Experimental study with real images demonstrated the feasibility and promising of the proposed approach in discriminating between cervical texture patterns indicative of different stages of cervical lesions.
本文提出了一种广义统计纹理分析技术,用于从阴道镜图像中表征和识别与宫颈病变相关的典型、诊断上最重要的血管模式。该研究的贡献包括:1)通过使用几何基元的统计描述,引入了一种基于传统统计和结构纹理分析方法相结合的广义纹理分析技术;2)引入了一组纹理度量,这些度量能够捕捉人类所感知的宫颈纹理的特定特征。对真实图像的实验研究证明了所提出方法在区分指示宫颈病变不同阶段的宫颈纹理模式方面的可行性和前景。