TY - JOUR
T1 - Automated sperm morphology analysis approach using a directional masking technique
AU - Ilhan, Hamza Osman
AU - Serbes, Gorkem
AU - Aydin, Nizamettin
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/7
Y1 - 2020/7
N2 - Sperm Morphology is the key step in the assessment of sperm quality. Due to the effect of misleading human factors in manual assessments, computer-based techniques should be employed in the analysis. In this study, a computation framework including multi-stage cascade connected preprocessing techniques, region based descriptor features, and non-linear kernel SVM based learning is proposed for the classification of any stained sperm images for the assessment of the morphology. The proposed framework was evaluated on two sperm morphology datasets: the Human Sperm Head Morphology dataset (HuSHeM) and Sperm Morphology Image Data Set (SMIDS). The results indicate that cascading the preprocessing techniques used in the proposed framework, such as wavelet based local adaptive de-noising, modified overlapping group shrinkage, image gradient, and automatic directional masking, increased the classification accuracy by 10% and 5% for the HuSHeM and SMIDS, respectively. The proposed framework results in better overall accuracy than most state-of-the-art methods, while having significant advantages, such as eliminating the exhaustive manual orientation and cropping operations of the competitors with reasonable rates of consumption of time and source.
AB - Sperm Morphology is the key step in the assessment of sperm quality. Due to the effect of misleading human factors in manual assessments, computer-based techniques should be employed in the analysis. In this study, a computation framework including multi-stage cascade connected preprocessing techniques, region based descriptor features, and non-linear kernel SVM based learning is proposed for the classification of any stained sperm images for the assessment of the morphology. The proposed framework was evaluated on two sperm morphology datasets: the Human Sperm Head Morphology dataset (HuSHeM) and Sperm Morphology Image Data Set (SMIDS). The results indicate that cascading the preprocessing techniques used in the proposed framework, such as wavelet based local adaptive de-noising, modified overlapping group shrinkage, image gradient, and automatic directional masking, increased the classification accuracy by 10% and 5% for the HuSHeM and SMIDS, respectively. The proposed framework results in better overall accuracy than most state-of-the-art methods, while having significant advantages, such as eliminating the exhaustive manual orientation and cropping operations of the competitors with reasonable rates of consumption of time and source.
KW - Descriptor based feature extraction
KW - Directional masking technique
KW - Maximally stable extremal regions
KW - Sperm morphology classification
KW - Support vector machines
KW - Wavelet based local adaptive de-noising
UR - http://www.scopus.com/inward/record.url?scp=85086141340&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2020.103845
DO - 10.1016/j.compbiomed.2020.103845
M3 - Article
C2 - 32658734
AN - SCOPUS:85086141340
SN - 0010-4825
VL - 122
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 103845
ER -