A novel support vector machine (SVM)-based boundary recovery procedure for segmented medical objects in low-resolution images is proposed. The proposed procedure consists of two steps: segmentation and boundary interpolation steps. First, we initially estimate a coarse object region using an active contour-based segmentation method. Boundary recoveries from the first step exhibit considerably blocky artifacts and are easily misled by noise. Then, a reliable boundary recovery is achieved in the next step by the proposed support vector machines based interpolation scheme. In simulation, the proposed algorithm shows more reliable and better performance in the presence of noise and adequately preserves shapes and smooth boundaries that are essential characteristics of medical objects. We illustrate it using real-life data sets in regard to nonconvex tube detection in wall shear stress, lumen detection in carotid stenosis, micro-calcifications detection in digital mammography, and nonmedical fields as well.