Spermatogonium image recognition using Zernike moments.
The automatic identification and classification of spermatogonium images is a very important issue in biomedical engineering research. This paper proposes a scheme for spermatogonium recognition, in which Zernike moments are used to represent image features. First of all, the mathematical morphology method is employed to extract the intact individual cell in every image, and then we normalize these binary images. Then, Zernike moments are calculated from these normalized images, followed by recognizing the spermatogonia through computing similarity of vectors composed with Zernike moments using Euclidean distance. Experimental results demonstrate that the proposed method, based on Zernike moments, outperforms two well-known methods, namely those based on Hu moments and boundary moments. This method has stronger distinguishing ability, showing better performance in discriminating cell images whether belong to the same cell.
Liyun
Wang
W
College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Hefei
Ling
L
Fuhao
Zou
Z
Zhengding
Lu
L
Zhendi
Wang
W
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001331:Automation; D002469:Cell Separation; D019295:Computational Biology / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D008854:Microscopy, Electron / Q000379:methods; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods; D051381:Rats; D012815:Signal Processing, Computer-Assisted; D013093:Spermatogonia / Q000473:pathology* / Q000648:ultrastructure