Because images of neurons show interweaved processes from multiple cells, it is hard to determine which pixels belong to each cell, and consequently to analyze the images automatically. To manage these difficulties, we introduce probabilistic segmentation, in which each pixel is assigned a probability of belonging to each cell instead of being categorically assigned to one cell. We propose a randomized algorithm for probabilistic segmentation. The algorithm is based on repeated, intensity-weighted random walks on the image, and leads to improved segmentation quality. Analysis and mining techniques can utilize the more nuanced and complete information that the probabilistic segmentation yields about an image. Such techniques can then compute probabilistic values, which indicate the level of confidence that can be placed in them.