Automatic detection and segmentation of sperm head, acrosome and nucleus in microscopic images of human semen smears. Issue 132 (August 2016)

• Record Type: Journal Article
• Authors: Shaker, Fariba; Monadjemi, S. Amirhassan; Naghsh-Nilchi, Ahmad Reza
• Is Part Of: Computer methods and programs in biomedicine. Issue 132(2016)
• Page Start: 11
• Page End: 20
• Publication Date: 2016-08
• Subjects: Sperm morphology -- Sperm detection -- Sperm head segmentation -- Active contours -- Hue histogram -- Tail detection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.04.026
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We use an edge based active contour for sperm head segmentation. We propose new algorithms to locate the sperm tail and remove the midpiece. Our segmentation method achieves more than 92% overlap with hand segmented ground truth for sperm heads. Our segmentation method achieves a higher Dice coefficient and lower dispersion compared to the current state-of-the-art. Our tail detection algorithm correctly locates the tail with the rate of 96%. Abstract: Background and 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.