With the rising prevalence of cardiac diseases and an increasing shortage of pathologists, the development of machine learning models utilizing endomyocardial biopsy specimens for the automatic diagnosis of cardiomyopathies is becoming a critical necessity. This study investigates the effectiveness of texture features and feature selection in identifying informative features for the differentiation of various cardiomyopathies. The results demonstrate that robust features, resistant to individual variability, were successfully extracted and selected, clearly distinguishing distinct myocardial conditions. These findings suggest the potential for future implementation of automated diagnostic systems. Furthermore, this approach is expected to contribute to the prevention of diagnostic delays, overlooked findings, and misdiagnoses stemming from insufficient expertise.