Identification of Gad-enhancing lesions is of great interest in Multiple Sclerosis (MS) disease since they are associated with disease activity. Current techniques for detecting Gad-enhancing lesions use a contrast agent (Gadolinium) which is administered intravenously to highlight Gad-enhancing lesions. However, the contrast agent not only highlights these lesions, but also causes other tissues (e.g., blood vessels) or noise in the Magnetic Resonance Image (MRI) to appear hyperintense. Discrimination of enhanced lesions from other enhanced structures is particularly challenging as these lesions are typically small and can be found in close proximity to vessels. We present a new approach to address the segmentation of Gad-enhancing MS lesions using Conditional Random Fields (CRF). CRF performs the classification by simultaneously incorporating the spatial dependencies of data and labels. The performance of the CRF classifier on 20 clinical data sets shows promising results in successfully capturing all Gad-enhancing lesions. Furthermore, the quantitative results of the CRF classifier indicate a reduction in the False Positive (FP) rate by an average factor of 5.8 when comparing to Linear Discriminant Analysis (LDA) and 1.6 comparing to a Markov Random Field (MRF) classifier.