A multi-direction image fusion based approach for classification of multi-focal nematode image stacks

In this paper, we present to use a multi-direction image fusion based feature extraction approach to classify multi-focal image stacks. The discrete wavelet transform sparse representation (DWTSR) image fusion technique is used to combine relevant information from a given image stack into a single image, which is more informative and complete than any single individual image within the given stack. Besides, multi-focal images within a multi-focal stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by using canonical correlation analysis (CCA). The experimental results on the nematode multi-focal images show that our proposed multi-direction image fusion based feature extraction method can improve the recognition rate from 83.8% in the previous work to 96% by using texture feature only.