Low-contrast profile images are frequently encountered in medical practice, and the correct interpretation of these images is of vital importance. This study introduces a contrast enhancement technique based on singular value decomposition (SVD) to enhance low-contrast fracture x-ray images. We propose a development of the traditional singular value solution by applying a feature selection process on the extracted singular values. The proposal calls for the establishment of a feature space in which the interpretability or perception of information in images for human viewers is enhanced, while noise and blurring are reduced. In this approach, the area of interest is manually cropped, and histogram equalization (HE) and singular value selection procedures are then conducted for comparative study. This approach exploits the spectral property of SVD, and the singular value selection algorithm is developed based on the corresponding Fourier domain technique for high frequency enhancement. The proposed method can generate more enhanced views of the target images than HE processing. Ten physicians confirm the performance of the proposed model using the visual analog scale (VAS). The average VAS score improves from 2.5 with HE to 8.3 using the proposed method. Experimental results indicate that the proposed method is helpful in fracture x-ray image processing.