A New Method for Detecting Sperms in Microscopy Images: Combination of Zernike Moments and Spatial Processing

In recent years, modern microscopic imaging in parallel with digital image processing techniques, have facilitated computerized semen analysis. However, in these methods, distinguishing sperms from other semen particles can be hampered by low contrast of microscopic images and the possibility of neighboring sperms touching each other. This article introduced a new method based on combination of Zernike moments and spatial processing in sperm detection. In the first step, Zernike moments were estimated due to their rotation and noise-resistant nature to mark pixels with some chance of belonging to sperms. In the second step, pruning was executed considering the connectivity of candidates and using morphological processing, to extract sperms. The proposed algorithm was examined on microscopic images exhibiting several sperms with different morphologies. The obtained results showed the ability of the proposed method in sperm detection, such that it could detect 85% of the sperms without any false detection. In a more pragmatic situation, where false detection rate was 5%, the detection rate of the proposed algorithm increased to 94%. Comparing the proposed method with watershed segmentation algorithm (WSA) and morphological contour synthesis (MCS) revealed the superiority of the proposed method to its alternatives in such a way that it detected sperms at least 3% and 13% better than WSA and MCS, respectively, without any false detection. Furthermore, the rate of false detections in the proposed algorithm was at least 4% and 14% better than its alternatives.