  With lung cancer being the most fatal cancer worldwide, it is important to
detect the disease early. A potentially effective way of detecting early cancer
lesions developing along the airway walls (epithelium) is bronchoscopy. To this
end, developments in bronchoscopy offer three promising noninvasive modalities
for imaging bronchial lesions: white-light bronchoscopy (WLB), autofluorescence
bronchoscopy (AFB), and narrow-band imaging (NBI). While these modalities give
complementary views of the airway epithelium, the physician must manually
inspect each video stream produced by a given modality to locate the suspect
cancer lesions. Unfortunately, no effort has been made to rectify this
situation by providing efficient quantitative and visual tools for analyzing
these video streams. This makes the lesion search process extremely
time-consuming and error-prone, thereby making it impractical to utilize these
rich data sources effectively. We propose a framework for synchronizing
multiple bronchoscopic videos to enable an interactive multimodal analysis of
bronchial lesions. Our methods first register the video streams to a reference
3D chest computed-tomography (CT) scan to produce multimodal linkages to the
airway tree. Our methods then temporally correlate the videos to one another to
enable synchronous visualization of the resulting multimodal data set.
Pictorial and quantitative results illustrate the potential of the methods.
