Modified Background Subtraction Statistic Models for Improvement Detection and Counting of Active Spermatozoa Motility

I Gede Susrama Masdiyasa (Institut Teknologi Sepuluh November Surabaya)
I D. G. Hari Wisana (Departement of Informatics, Universitas of Pembangunan Nasional Veteran East Java)
I K. Eddy Purnama (Department of Electromedic Engineering, Politeknik Kesehatan Surabaya)
M. Hery Purnomo (Department of Computer Engineering, Institut Teknologi Sepuluh Nopember Surabaya)

Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Vol. 9, No. 1 April 2018

An important early stage in the research of sperm analysis is the phase of sperm detection or separating sperm objects from images/video obtained from observations on semen. The success rate in separating sperm objects from semen fluids has an important role for further analysis of sperm objects. Algorithm or Background subtraction method is a process that can be used to separate moving objects (foreground) and background on sperm video data that tend to uni-modal. In this research, some of the subproject model statistics of substrata model are Gaussian single, Gaussian Mixture Model (GMM), Kernel Density Estimation and compared with some basic subtraction model background algorithm in detecting and counting the number of active spermatozoa. From the results of the tests, the Grimson GMM method has an f-measure value of 0.8265 and succeeded in extracting the sperm form near its original form compared to other methods