Tracking kidney tumor dimensional measurements via image morphing

Monitoring the evolution of renal cancer is essential in clinical oncology trials. However, manual tumor measurements are subjective and inconsistent. We have developed and implemented an efficient image-morphing-based method to track dimensional measurements in serial medical images. Our method extracts feature blocks centered at edges detected in source and target images, and then applies a fast, approximate nearest-neighbor algorithm to match these features. This matching gives rise to a global feature flow, and this feature flow can be used to track dimensional measurements.