Automatic classification of fish germ cells through optimum-path forest

Author

Papa, João P. ; Gutierrez, Mario E M ; Nakamura, Rodrigo Y M ; Papa, Luciene P. ; Vicentini, Irene B F ; Vicentini, Carlos A.

Author_Institution

Dept. of Comput., Univ. Estadual Paulista, Bauru, Brazil

fYear

2011

fDate

Aug. 30 2011-Sept. 3 2011

Firstpage

5084

Lastpage

5087

Abstract

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques.

Keywords

biology computing; cellular biophysics; image classification; learning (artificial intelligence); pattern recognition; zoology; automatic classification; fish germ cells; machine learning techniques; optimum-path forest; pattern recognition technique; recognition accuracy; spermatogenesis; Feature extraction; Image segmentation; Machine learning; Prototypes; Support vector machines; Training; Vegetation; Animals; Artificial Intelligence; Fishes; Germ Cells; Image Interpretation, Computer-Assisted; Microscopy; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

Conference_Location

Boston, MA

ISSN

1557-170X

Print_ISBN

978-1-4244-4121-1

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2011.6091259

Filename

6091259