Lung cancer remains the most common fatal malignancy in both men and women in the United States and elsewhere around the world among people who have been exposed passively or actively to tobacco smoke. Technological advances have produced imaging modalities that are proving to be useful for the early detection of lung cancer. In the usual modes, computed tomography (CT) and magnetic resonance imaging (MRI) can identify suspicious lesions, but further work is needed to detect cancer in its early stage. Tumor angiogenesis assessed by perfusion-sensitive MRI is a promising method for early and accurate identification of lung cancer that avoids patient stress and the potential progression to a less treatable stage inherent in serial imaging. However, compensating for the respiratory and anatomical structure motion is a challenge. In order to use perfusion MRI in a signal imaging session to define metabolic and vascular characteristics of tumors, we present a novel affine registration method for perfusion MRI that can register points of interest by tracking image intensity changes around the target point. The registration results are used to generate accurate time intensity curves (TIC) of different regions of interest (ROI). The patterns of different TICs are mapped on the cancer and other structures. The method is computationally efficient and performs well in our perfusion MRI sequence analysis.