Intra-class classification of tumor in brain MR images

摘要

In robust tumor recognition engine implementation, it is important to superimpose different types of MR images to verify the adequacy of treatment. Usually, the tumors in time-staggered MR images may vary in shape, format, orientation, angle, translation, scale and by a variety of other distortions. It is already known that various image registration techniques such as Affine transform suffers from lack of speed. Thus, the ability to extract distortion-invariant image features is highly desirable for improved speed and efficiency. We propose to explore distortion-invariant metadata extraction and subsequent classification of tumor in brain MR images.