Multi-focal nematode image classification using the 3D X-Ray Transform
In this paper, we present a 3D X-Ray Transform based feature extraction and classification method for Digital Multi-focal Images (DMI). In such images, morphological information for a transparent specimen can be captured in the form of a stack of high-quality images, representing individual focal planes through the specimen's body. We present a method that can effectively exploit the entire information in the stack using the 3D X-Ray Transform at different angle views. By combining the texture and shape information from different angles, we can get better recognition rates than just relying on the original or key frames of DMI stacks. The experimental results on the nematode DMI data show that the 3D X-Ray Transform based classification method can effectively improve the recognition rate from 60% (PCA) to 96.8%.