Edge Detection Algorithm for Biological Slice Images Based on Empirical Wavelet Transform and Morphology

Edge detection is important in extracting image features, and microscopic slice images consist of closed-loop structures and complex internal textures, and extracting the corresponding features has an important role in biology, epidemiology, pathology and other fields. In this study, an edge detection algorithm for slice images based on empirical wavelet transform (EWT) and morphology is proposed. The empirical wavelet divides the Fourier spectrum of the signal into successive intervals, and then constructs a wavelet filter bank for filtering in the corresponding interval segments, and finally obtains the amplitude modulation frequency components by signal reconstruction. The empirical wavelet transform overcomes the modal aliasing problem caused by the scale discontinuity in the time domain, which reflects the characteristics of the empirical wavelet transform. The image components extracted by the empirical wavelet are then enhanced using a morphological algorithm, which can effectively extract the closed-loop edges of the sliced image as well as the significant textures inside. In this paper, the proposed method is tested on locust slice images as an example. The proposed algorithm can also be effectively applied to other biological cross-sectional images.