Radiographic image segmentation presents unique challenges due to overlapping anatomical structures, projection ambiguity, and the scarcity of high-quality annotations. Recently, segmentation foundation models such as MedSAM have emerged as powerful tools for automated medical image analysis. Trained on large-scale and diverse image-mask pairs, MedSAM has achieved broad generalization across a wide range of medical image segmentation tasks. Despite this, its exposure to X-rays was primarily limited to chest radiographs annotated with lung masks, and the model relied on spatial prompts like bounding boxes, which are labor-intensive to draw precisely during inference and prone to ambiguity. To overcome these limitations, we propose a parameter-efficient adaptation of MedSAM designed for X-ray image segmentation. The approach integrates lightweight low-rank adaptation (LoRA) fine-tuning to enable efficient model updating while incorporating text-based conditioning to guide mask prediction. This design facilitates intuitive, non-expert human interaction without requiring precise geometric prompts. Evaluated on internal chest and lower-limb radiographic datasets, the model achieves a mean Dice (mDice) score of 92.42 and a mean intersection-over-union (mIoU) of 86.46 while unfreezing only a small fraction of parameters. These results demonstrate that parameter-efficient, language-conditioned adaptation offers an effective strategy for enhancing segmentation performance in projection-based medical imaging.