Lung cancer is the leading cause of cancer fatalities in the world. A recent trend has begun to focus on the idea of using bronchoscopy for early detection of suspect cancerous lesions developing along the airway walls. Because standard white-light bronchoscopy has insufficient sensitivity in locating suspect lesions, researchers are turning to the promising modality referred to as narrow band imaging (NBI). NBI bronchoscopy has the advantage of highlighting the blood vessels contained in the lung mucosa. Since cancer lesions tend to exhibit abnormal vessel growth, NBI bronchoscopy is able to highlight such lesions. Unfortunately, the task of locating lesions and their vessel patterns in an NBI bronchoscopy video stream proves to be tedious for the physician. We present automatic methods for enhancing and segmenting the major blood vessels depicted in NBI bronchoscopic video. Results with ground-Truth data indicate that our methods can achieve superior results to a popular existing vessel-segmentation method. We also consider a preliminary application of deep learning to this task; while this approach gives low sensitivity compared to the other approaches, it achieves higher specificity and accuracy.