Efficient detection and robust tracking of spermatozoa in microscopic video

Abstract

Abstract Sperm concentration and motility are generally analysed only in the discrete state in microscopic videos. As for sperm nonspecific aggregation areas, it brings difficulties to accurate sperm detection. In this paper, an algorithm for nonspecific aggregates automatic segmentation, detection and tracking of sperm is proposed. A grid model commensurate with the size of a sperm head is created to segment nonspecific aggregation areas. Multi‐scale edge function and new energy functional are designed based on the level set method to realize sperm head segmentation. In the sperm tracking stage, we improve the weight condition and the standard of trust flow quantization based on graph theory method, and simplify the sperm tracking to the vertex matching between two frames to solve the matching failure problem of adjacent frames with small space distance. The proposed method achieves accurate segmentation of sperm non‐specific aggregation regions, which outperforms the level set methods of LBF and SBGFR. At the same time, our method can real‐time calculation of sperm concentration and motility during sperm tracking. It is compared with four state‐of‐the‐art algorithms, and it gives lower tracking error rates, which has potential applications in male fertility field.

Keywords

• Optical, image and video signal processing
• Image recognition
• Computer vision and image processing techniques
• Other topics in statistics
• Other topics in statistics
• Combinatorial mathematics