We derive and implement a method to compute isosurface area that demonstrates better numeric properties than previously described similar algorithms. The described stochastic area computation algorithm has been tested on geometric objects as well as on protein models. One of the advantages of the method is that for each line in a sample the number of surface intersection points can be counted in a parallel manner, independently of other lines in the sample, which maps well to the multi-core and multi-node architectures.