Automated human sperm tracking using mean shift - collision detection and modified covariance matrix method

Tan, Weng Chun; Isa, Nor Ashidi Mat; Mohamed, Mahaneem

doi:10.1007/s11042-020-09396-2

Cited by 3 publications

(5 citation statements)

References 21 publications

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“…MS–CDMCM [12]: Mean shift–collision detection and modified covariance matrix is employed to detects collision and generates a new covariance matrix based on the collision condition. The new covariance matrix can form a new tracked region to continue the tracking sperm.…”

Section: Discussionmentioning

confidence: 99%

“…Tail to Head movement algorithm is employed for the motility analysis and tracking. AWAS [5]: Nafisi et al. propose a combined method for sperm cells detection, which consists of a non‐linear pre‐processing stage, a histogram‐based thresholding algorithm, and a tracking method based on an adaptive distance scheme. MS–CDMCM [12]: Mean shift–collision detection and modified covariance matrix is employed to detects collision and generates a new covariance matrix based on the collision condition. The new covariance matrix can form a new tracked region to continue the tracking sperm. OM–Kalman [25]: Otsu thresholding and morphological operations is employed for sperm cells detection.…”

Section: Discussionmentioning

confidence: 99%

“…Literature [5] propose a combined method for sperm cells detection, which consists of a nonlinear pre-processing stage, a histogram-based thresholding algorithm, and a tracking method based on an adaptive distance scheme. Tan et al [12] use mean shift-collision detection and modified covariance matrix (MS-CDMCM) method. MS-CDMCM can detect collision and generates a new covariance matrix based on the collision condition.…”

Section: Related Workmentioning

confidence: 99%

“…Today, a number of sperm detection and tracking algorithms have been proposed in semen micro video [5, 12]. Unlike other staining imaging techniques, semen quality testing tracks the movement of sperm cells in the sequence of images without killing the cells.…”

Section: Introductionmentioning

confidence: 99%

“…Tan et al. [12] use mean shift–collision detection and modified covariance matrix (MS–CDMCM) method. MS–CDMCM can detect collision and generates a new covariance matrix based on the collision condition.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient detection and robust tracking of spermatozoa in microscopic video

Zhu

Cui

Hou³

et al. 2021

IET Image Processing

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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.

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Section: Discussionmentioning

confidence: 99%