TY - JOUR
T1 - Neural net-based identification of cells expressing the p300 tumor-related antigen using fluorescence image analysis
AU - Hurst, Robert E.
AU - Bonner, Rebecca Bass
AU - Ashenayi, Kaveh
AU - Veltri, Robert W.
AU - Hemstreet, George P.
N1 - Copyright:
Copyright 2007 Elsevier B.V., All rights reserved.
PY - 1997/1/1
Y1 - 1997/1/1
N2 - We report on preliminary investigations of the use of an image analysis system to perform preliminary algorithmic classification of images of fluorochrome-labeled cells followed by capture of gray-level images of potentially abnormal cells for analysis by a neural network. Cells were labeled with an antibody against a bladder cancer tumor-associated antigen, and the neural net was used to distinguish true-positive cells from negative cells, false-positive cells (autofluorescent or nonspecific labeling), and cell-sized artifacts. Gray-level cell images were digitized and processed for analysis by a feed-forward neural network using back-propagation. The network was trained and tested with two independent image sets. Various network configurations and activation functions were investigated, including a sinusoidal activation function. At high power, the network agreed completely with the human observer's classification. At low power, a strong clustering of cells classified by the network with expert classification was seen, while the neural network showed roughly 75% concordance with the human observer. In addition, a set of four features extracted from raw cell images were investigated. The features were: shape factor, texture, area, and average pixel intensity. A network trained with these features performed better than one operating with gray-level images. We conclude that using neural networks to recognize and classify images captured by an image analysis microscope is feasible.
AB - We report on preliminary investigations of the use of an image analysis system to perform preliminary algorithmic classification of images of fluorochrome-labeled cells followed by capture of gray-level images of potentially abnormal cells for analysis by a neural network. Cells were labeled with an antibody against a bladder cancer tumor-associated antigen, and the neural net was used to distinguish true-positive cells from negative cells, false-positive cells (autofluorescent or nonspecific labeling), and cell-sized artifacts. Gray-level cell images were digitized and processed for analysis by a feed-forward neural network using back-propagation. The network was trained and tested with two independent image sets. Various network configurations and activation functions were investigated, including a sinusoidal activation function. At high power, the network agreed completely with the human observer's classification. At low power, a strong clustering of cells classified by the network with expert classification was seen, while the neural network showed roughly 75% concordance with the human observer. In addition, a set of four features extracted from raw cell images were investigated. The features were: shape factor, texture, area, and average pixel intensity. A network trained with these features performed better than one operating with gray-level images. We conclude that using neural networks to recognize and classify images captured by an image analysis microscope is feasible.
KW - M344
KW - bladder cancer
KW - fluorescence
KW - image analysis
KW - neural network
KW - quantitative cytology
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U2 - 10.1002/(SICI)1097-0320(19970101)27:1<36::AID-CYTO5>3.0.CO;2-J
DO - 10.1002/(SICI)1097-0320(19970101)27:1<36::AID-CYTO5>3.0.CO;2-J
M3 - Article
C2 - 9000583
AN - SCOPUS:0031036985
VL - 27
SP - 36
EP - 42
JO - Cytometry
JF - Cytometry
SN - 0196-4763
IS - 1
ER -