Muscle myopathies or defects, such as white striping (WS), downgrade the quality of broiler meat, leading to the loss of millions of dollars annually for the United States poultry industry. Visual inspection is currently practiced for assessing myopathies in broiler meat, which is however prone to human evaluation error, labor-intensive, and costly. Imaging technology that employs uniform or diffuse illumination has been investigated for assessing broiler meat quality, but their performance is not always satisfactory, especially for detecting subtle defects with few visual symptoms. This study presents the first proof-of-concept evaluation of the utility of an innovative technique for improved imaging of structures of biological materials, i.e., structured-illumination reflectance imaging (SIRI), for detecting WS conditions in broiler breast meat. Broiler breast fillets with varying degrees of WS severity were imaged by an in-house assembled SIRI system under sinusoidal illumination at different spatial frequencies (0.05–0.40 cycles/mm). The acquired pattern images were demodulated into direct components (DC) and amplitude components (AC) at each spatial frequency. Texture features were extracted from DC and AC images for building classification models using regularized linear discriminant analysis, which were to classify the poultry meat samples into 2, 3, and 4 classes, respectively, according to WS conditions. AC images were found by visual inspection more effective than DC in resolving WS characteristics, depending on the spatial frequency of illumination patterns. Compared to DC, AC yielded the maximum accuracy improvements of 8.33%, 7.93%, and 12.3% in 2-, 3- and 4-class classifications, respectively. Classification models using ranked features yielded similar improvements of up to 9.53% by AC over DC. The SIRI technique is promising for enhanced detection of WS and potentially other defects of broiler breast meat.