The Evaluation of Detectors and Descriptors on Determination of Semen Cell

Abstract

Detectors and descriptors refer to the key points of the images where informative features can be detected or extracted to use in machine learning for classification or clustering problems. Four algorithms as two descriptor (SURF, MRES) and two detectors (Harris, Shi Tomasi) are utilized for semen cell detection problem in this paper. Results emphasize the best algorithm for future studies to use in tracking or morphological analysis of semen. The evaluation of algorithms is carried out on manually labeled images over a pre-defined verification area. Not only accuracy is measured owing to data imbalance problem, but also f-measure scores are registered to indicate the methods success rates. As a summary of paper, SURF surpasses over other methods with 87.67% accuracy rate and 0.92 F-measure score owing to the scale invariant method.