Paper Info
Title
Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns.
Abstract
In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The segmentation algorithm achieved a mean (±standard deviation) sensitivity and specificity of 0.8693 ± 0.1371 and 0.9136 ± 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 ± 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 ± 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.
Year
DOI
Venue
2013
10.1007/s11517-012-1009-2
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Texture analysis, Ultrasonography, Corpora lutea, Local binary patterns, Genetic programming
Computer vision,Ultrasonography,Pattern recognition,Segmentation,Local binary patterns,Genetic programming,Root mean square,Invariant (mathematics),Artificial intelligence,Hausdorff distance,Mathematics
Journal
Volume
Issue
ISSN
51
4
1741-0444
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Meng Dong100.34
Mark G Eramian2267.96
Simone A Ludwig31309179.41
Roger A Pierson441.47