Localization-based super-resolution techniques are revolutionizing biological research by breaking the diffraction limit of fluorescence microscopy. Each super-resolution image is reconstructed from a time series of images of randomly activated fluorophores. Here, a fundamental question is to determine the minimal imaging length so that the reconstructed image faithfully reflects the biological structures under observation. So far, proposed methods focus entirely on image resolution, which reflects localization uncertainty and fluorophore density, without taking into account the fact that images of biological structures are structured rather than random patterns. Here, we propose a different approach to determine imaging length based on direct quantification of image structural information using Gabor filters. Experimental results show that this approach is superior over approaches that only account for image-intensity distribution, confirming the importance of using structural information. In contrast to resolution-based methods, our method does not require an artificial selection of image resolution and provides a statistically rigorous strategy for determining imaging length based on image structural information.