Automatic detection and segmentation of sperm head, acrosome and nucleus in microscopic images of human semen smears.
OBJECTIVE
Manual assessment of sperm morphology is subjective and error prone so developing automatic methods is vital for a more accurate assessment. The first step in automatic evaluation of sperm morphology is sperm head detection and segmentation. In this paper a complete framework for automatic sperm head detection and segmentation is presented.


METHODS
After an initial thresholding step, the histogram of the Hue channel of HSV color space is used, in addition to size criterion, to discriminate sperm heads in microscopic images. To achieve an improved segmentation of sperm heads, an edge-based active contour method is used. Also a novel tail point detection method is proposed to refine the segmentation by locating and removing the midpiece from the segmented head. An algorithm is also proposed to separate the acrosome and nucleus using morphological operations. Dice coefficient is used to evaluate the segmentation performance. The proposed methods are evaluated using a publicly available dataset.


RESULTS
The proposed method has achieved segmentation accuracy of 0.92 for sperm heads, 0.84 for acrosomes and 0.87 for nuclei, with the standard deviation of 0.05, which significantly outperforms the current state-of-the-art. Also our tail detection method achieved true detection rate of 96%.


CONCLUSIONS
In this paper we presented a complete framework for sperm detection and segmentation which is totally automatic. It is shown that using active contours can improve the segmentation results of sperm heads. Our proposed algorithms for tail detection and midpiece removal further improved the segmentation results. The results indicate that our method achieved higher Dice coefficients with less dispersion compared to the existing solutions.
Department of Artificial Intelligence, Faculty of Computer Engineering, University of Isfahan, Isfahan, 81746, Iran.
Fariba
F
Shaker
Department of Artificial Intelligence, Faculty of Computer Engineering, University of Isfahan, Isfahan, 81746, Iran. Electronic address: monadjemi@eng.ui.ac.ir.
S Amirhassan
SA
Monadjemi
Department of Artificial Intelligence, Faculty of Computer Engineering, University of Isfahan, Isfahan, 81746, Iran.
Ahmad Reza
AR
Naghsh-Nilchi
D000177:Acrosome / Q000378:metabolism*; D000465:Algorithms; D001331:Automation; D002467:Cell Nucleus / Q000378:metabolism*; D006801:Humans; D008297:Male; D012661:Semen / Q000378:metabolism*; D013077:Sperm Head / Q000378:metabolism*
Active contours; Hue histogram; Sperm detection; Sperm head segmentation; Sperm morphology; Tail detection