Esgiar A N, Naguib R N, Bennett M K, Murray A
Department of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, U.K.
Anal Quant Cytol Histol. 1998 Aug;20(4):297-301.
To assess an automated algorithm, developed for the classification of normal and cancerous colonic mucosa, using geometric analysis of features and texture analysis.
Twenty-one images were analyzed, 10 from normal and 11 from cancerous mucosa. The classification was based on a regularity index dependent on shape, object orientation for establishing parallelism and five texture features derived using the co-occurrence image analysis method.
Geometric analysis yielded an overall classification accuracy of 80%. The corresponding sensitivity and specificity were 94% and 64%, respectively. Using texture analysis, the overall classification accuracy was 90%, with a sensitivity and specificity of 82% and 100%, respectively.
This initial study demonstrated that geometric and texture analysis techniques show promise for automated analysis of colon cancer.
使用特征的几何分析和纹理分析来评估一种为正常和癌性结肠黏膜分类而开发的自动化算法。
分析了21幅图像，其中10幅来自正常黏膜，11幅来自癌性黏膜。分类基于一个依赖于形状的规则性指数、用于建立平行性的物体方向以及使用共生图像分析方法得出的五个纹理特征。
几何分析得出的总体分类准确率为80%。相应的敏感性和特异性分别为94%和64%。使用纹理分析时，总体分类准确率为90%，敏感性和特异性分别为82%和100%。
这项初步研究表明，几何和纹理分析技术在结肠癌自动化分析方面显示出前景。
