Segmentation and tracking of human sperm cells using spatio- temporal representation and clustering

This work proposes an algorithm for segmentation and tracking of human sperm. The algorithm analyzes video sequences containing multiple moving sperms and produces video segmentation maps and moving objects trajectories. Sperm trajectories analysis is widely used in computer-aided sperm analysis (CASA) systems. Several researches show that CASA systems face a problem when dealing with the "actual" or "perceived" collisions of sperms. The proposed algorithm reduces the probability of wrong trajectory construction related to collisions. We represent the video data using a 4-dimensional model containing spatial and temporal coordinates and the direction of optical flow vectors. The video sequence is divided into a succession of overlapping subsequences. The video data of each subsequence is grouped in the feature domain using the mean shift procedure. We identify clusters corresponding to moving objects in each subsequence. The complete trajectories are reconstructed by matching clusters that are most likely to represent that same object in adjacent subsequences. The clusters are matched using heuristics which are based on cluster overlaps, and by solving a specially formulated linear assignment problem. Tracking results are evaluated for different video sequences containing different types of motions and collisions.

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