光照鲁棒的模糊C均值聚类显微图像分割

传统的模糊C均值FCM聚类图像分割算法在显微图像分割中由于没有考虑光照不均匀的影响而降低了分割的效果，为此，提出了一种光照鲁棒的FCM显微图像分割算法。该算法用正交基函数的线性组合模拟不均匀光照，并引入到FCM算法的目标函数中，进行图像的模糊分割。算法不仅降低了非均匀光照对分割效果的影响，还可以同步估计不均匀光照场。实验结果表明，该方法非常有效。

Fuzzy C-means clustering based microscopic image segmentation with illumination robustness

It influences the microscopic image segmentation results that traditional fuzzy C means (FCM) ignores the spatial intensity variations caused by uneven illumination, so a novel FCM based microscopic image segmentation algorithm with illumination robustness is proposed, which models the uneven illumination as a linear combination of a set of orthogonal basis functions. Meanwhile, the energy function in FCM is modified by means of the proposed model to support image fuzzy segmentation. The algorithm can alleviate the impact of uneven illumination on segmentation effect and estimate the uneven illumination fields simultaneously. The experimental results show that the algorithm is high efficient.