Quantification of hemorrhages in head computed tomography (CT) images from patients with traumatic brain injury (TBI) has potential applications in monitoring disease progression and better understanding of the patho-physiology of TBI. Although manual segmentations can provide accurate measures of hemorrhages, the processing time and inter-rater variability make it infeasible for large studies. In this paper, we propose a fully automatic novel pipeline for segmenting intraparenchymal hemorrhages (IPH) from clinical head CT images. Unlike previous methods of model based segmentation or active contour techniques, we rely on relevant and matching examples from already segmented images by trained raters. The CT images are first skull-stripped. Then example patches from an "atlas" CT and its manual segmentation are used to learn a two-class sparse dictionary for hemorrhage and normal tissue. Next, for a given "subject" CT, a subject patch is modeled as a sparse convex combination of a few atlas patches from the dictionary. The same convex combination is applied to the atlas segmentation patches to generate a membership for the hemorrhages at each voxel. Hemorrhages are segmented from 25 subjects with various degrees of TBI. Results are compared with segmentations obtained from an expert rater. A median Dice coefficient of 0.85 between automated and manual segmentations is achieved. A linear fit between automated and manual volumes show a slope of 1.0047, indicating a negligible bias in volume estimation.