Accuracy of nuclear classification in cervical smear images. Quantitative impact of computational deconvolution and 3-D feature computation.
OBJECTIVE
To investigate the accuracy with which the nuclei of cells in overlapped and thick clusters in cervical/ vaginal smears can be classified independent of the segmentation algorithm used and to determine the influence of three-dimensional (3-D) processing as compared to two-dimensional (2-D) methods on classification of the nuclei.


METHODS
Cell clusters were imaged from 31 ThinPrep smears composed of 808 nuclei, of which 420 were determined to be abnormal by a cytotechnologist. Sets of 2-D and 3-D volumetric features of the detected nuclei were formulated, and classifiers were constructed. The effect of computational deconvolution on classification was assessed using nearest-neighbor and Wiener filter in 2-D and 3-D before calculating features. A "best focus plane" was calculated for each nucleus from the 3-D data set, and the 2-D features in this plane were also analyzed.
Mackin
R W
RW
Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, New York, USA.
Newton
L M
LM
Turner
J N
JN
Holmes
T J
TJ
Roysam
B
B
D000465:Algorithms; D002467:Cell Nucleus; D002584:Cervix Uteri / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015203:Reproducibility of Results; D014626:Vaginal Smears