Astronomers are interested in delineating boundaries of extended sources in noisy images. An example is finding outlines of a jet in a distant quasar. This is particularly difficult for jets in high redshift, X-ray images where there are a limited number of pixel counts. Using Low-counts Image Reconstruction and Analysis (LIRA), Stein et al. 2015 and McKeough et al. 2016 propose and apply a method where jets are detected using previously defined regions of interest (ROI). LIRA, a Bayesian multi-scale image reconstruction, has been tremendously successful in analyzing low count images and extracting noisy structure. However, we do not always have supplementary information to predetermine ROI and the size and shape can greatly affect flux/luminosity. LIRA is also unaware of correlations that may exist between adjacent pixels in the real image. In order to group similar pixels, we impose a successor or post-model on the output of LIRA. We adopt the Ising model as a prior on assigning the pixels to either the background or the ROI. From the posterior of this model, we are able to delineate probabilistic boundaries. This method has been applied to the jet data as well as simulations and appears to be capable of picking out meaningful ROIs.