An efficient method for automatic morphological abnormality detection from human sperm images. Issue 3 (December 2015)

• Record Type: Journal Article
• Authors: Ghasemian, Fatemeh; Mirroshandel, Seyed Abolghasem; Monji-Azad, Sara; Azarnia, Mahnaz; Zahiri, Ziba
• Is Part Of: Computer methods and programs in biomedicine. Volume 122:Issue 3(2015)
• Page Start: 409
• Page End: 420
• Publication Date: 2015-12
• Subjects: Human sperm -- Sperm morphometry -- Automatic analysis -- Sperm defects -- Infertility -- Image processing; 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.2015.08.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Automatically selection of normal sperms. Working on unstained sperms. Ability to work on low resolution images. High accuracy and real-time processing time. Preparing a freely available dataset. Abstract: Background and objective: Sperm morphology analysis (SMA) is an important factor in the diagnosis of human male infertility. This study presents an automatic algorithm for sperm morphology analysis (to detect malformation) using images of human sperm cells. Methods: The SMA method was used to detect and analyze different parts of the human sperm. First of all, SMA removes the image noises and enhances the contrast of the image to a great extent. Then it recognizes the different parts of sperm (e.g., head, tail) and analyzes the size and shape of each part. Finally, the algorithm classifies each sperm as normal or abnormal. Malformations in the head, midpiece, and tail of a sperm, can be detected by the SMA method. In contrast to other similar methods, the SMA method can work with low resolution and non-stained images. Furthermore, an image collection created for the SMA, has also been described in this study. This benchmark consists of 1457 sperm images from 235 patients, and is known as human sperm morphology analysis dataset (HSMA-DS). Results: The proposed algorithm was tested on HSMA-DS. The experimental results show the high ability of SMA to detect morphological deformities from sperm images. In this study, the SMA algorithm produced above 90% accuracy in sperm abnormality detection task. Another advantage of the proposed method is its low computation time (that is, less than 9 s), as such, the expert can quickly decide to choose the analyzed sperm or select another one. Conclusions: Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time.