Cells cultured on micropatterns exhibit a chiral orientation, which may underlie the development of left-right asymmetry in tissue microarchitectures. To investigate this phenomenon, fluorescence staining of nuclei has been used to reveal such orientation. However, for images with high cell density, analysis is difficult because of the overlapping nuclei. Here, we report an image processing method that can acquire cell orientations within dense cell populations. After initial separation based on Boolean addition of binarized images using global and adaptive thresholds, the overlapping nucleus contours in the binarized images were segmented by iteratively etching the outlines of nuclei, which allowed the orientations of each cell to be extracted from densely packed cell clusters. In applying this technique to cultured C2C12 myoblasts in micropatterned stripes on different substrates, we found an enhanced chiral orientation on glass substrate. More important, this enhanced chirality was consistently observed with increased intercellular alignment and independent of cell-cell distance or cell density, suggesting that intercellular alignment plays a role in determining the chiral orientation. By segmenting single cells with intact orientation, this technique offers an automated method for quantitative analysis with improved accuracy, providing an essential tool for studying left-right asymmetry and other morphogenic dynamics in tissue formation.