Аннотация
This paper presents a reproducible system innovation for processing Digital Imaging and Communications in Medicine (DICOM) mammography images based on pixel-monotonic dynamic range management and engineering-verifiable intensity transformations. Standard DICOM conversion schemes to 8-bit representation often result in irreversible luminance-range compression, locality-dependent contrast distortions, and reduced robustness of deep learning models. The proposed framework preserves the physical consistency of the Modality LUT and photometric polarity, performs breast-aware robust Winsor normalization, and applies strictly monotonic global tone mapping while preserving the 16-bit depth of the training data. System validation was performed using architecture-independent metrics. Compared to standard processing, the median value of normalized mutual information increased from 0.878 to 0.892, the effective number of bits increased from 7.88 to 10.11 (+2.25), the representation entropy increased by 1.42 bits, and the clipping rate was reduced to almost zero. Experiments with the Faster R-CNN detector showed stable or improved calcification localization at Intersection over Union (IoU) ≥ 0.5 under controlled augmentation conditions. The results confirm that pixel-monotonic dynamic range control provides a reproducible, engineering-verifiable basis for AI-based mammography analysis within the evaluated dataset and experimental setting.