Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Med Biol Eng Comput. 2013 Apr;51(4):405-16. doi: 10.1007/s11517-012-1009-2. Epub 2012 Dec 11.
In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The segmentation algorithm achieved a mean (±standard deviation) sensitivity and specificity of 0.8693 ± 0.1371 and 0.9136 ± 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 ± 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 ± 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.
在这项研究中，我们提出了一种使用遗传编程和旋转不变局部二值模式自动检测和分割黄体（CL）的算法。在包含 CL 的 30 张图像和没有 CL 的 30 张图像上进行了检测和分割实验，并对其进行了评估。检测算法正确地确定了 93.33%的图像中是否存在 CL。分割算法在 30 个 CL 图像上的平均（±标准偏差）灵敏度和特异性分别为 0.8693±0.1371 和 0.9136±0.0503。分割边界与真实边界的平均均方根距离为 1.12±0.463mm，平均最大偏差（Hausdorff 距离）为 3.39±2.00mm。这些算法的成功表明，为分析体内人类卵巢而设计的类似算法可能是可行的。
