In this paper, we study segmentation of tight junctions and analyze the formation and integrity of tight junctions in large-scale confocal image stacks, a challenging biological problem because of the low spatial resolution images and the presence of breaks in tight junction structure. We present an automated, three-step processing approach for tight junction analysis. In our approach, we first localize each individual nucleus in the image by using thresholding, morphological filters and active contours. By using each nucleus position as a seed point, we automatically segment the cell body based on the active contour. We then use an intensity-based skeletonization algorithm to generate the boundary regions for each cell, and features are extracted from tight junctions associated with each cell to assess tight junction continuity. Based on qualitative results and quantitative comparisons, we show that we are able to automatically segment tight junctions and compute relevant features that provide a quantitative measure of tight junction formation to which the permeability of the cell monolayer can ultimately be correlated.