Diffuse interstitial lung disease comprises a group of disorders characterized by inflammation and cicatrization of the lung tissue, which affects the functionality of the lungs and presents similar radiological manifestations. These patterns can be seen in computed tomography (CT). Identifying these patterns is crucial for proper diagnosis and treatment. However, this task can be challenging for non-specialized clinicians, which can lead to misdiagnoses and serious complications. In this study, our attention has been directed to differentiating patterns of ground glass opacity, consolidation, micronodules, and healthy tissue. Statistical and texture feature extraction techniques have been used to analyze the quantitative characteristics of radiological images. In addition, statistical methods, such as the Mann-Whitney U test, have been applied to evaluate the difference in radiological patterns. Results showed significant mean-median differences in each radiological pattern. Especially the distinction of consolidation vs micronodules with a p-value of 1.14 × 10−73 in the mean and 7.00 × 10−72 in the median. The significant differences detected by the Whitney U test suggest that the extracted features could be effective biomarkers for radiological pattern identification, enhancing current detection methods. Integrating these features into artificial intelligence models may optimize radiological pattern identification in DILD, offering a more accurate and efficient clinical tool.