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Accuracy of nuclear classification in cervical smear images. Quantitative impact of computational deconvolution and
R W Mackin1, L M Newton, J N Turner
1Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, New York, USA.
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.
Study Design:
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.