DocumentCode
3365682
Title
Application of artificial neural networks for automatic measurement of micro-bubbles in microscopic images
Author
Pan, Baoning ; Abdelhamied, Kadry
Author_Institution
Dept. of Biomed. Eng., Louisiana Tech. Univ., Ruston, LA, USA
fYear
1992
fDate
14-17 Jun 1992
Firstpage
105
Lastpage
114
Abstract
A novel approach for quantitative segmentation and measurement of oxygen microbubbles in microscopic images is presented. In this approach, ellipse-based models were first built using moment parameters as rough approximations of oxygen microbubbles. Artificial neural networks were then developed and trained for segmentation refinement. The results show that the proposed approach achieved high accuracy of microbubbles measurement with less than 8% measurement error
Keywords
image segmentation; medical image processing; neural nets; ellipse-based models; microscopic images; neural networks; quantitative segmentation; segmentation refinement; Artificial neural networks; Biomedical imaging; Biomedical measurements; Image analysis; Image edge detection; Image segmentation; Intelligent networks; Microscopy; Ultrasonic imaging; Ultrasonic variables measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on
Conference_Location
Durham, NC
Print_ISBN
0-8186-2742-5
Type
conf
DOI
10.1109/CBMS.1992.244957
Filename
244957
Link To Document