Automatic signal classification in fluorescence in situ hybridization images /
Lerner, Boaz.
University of Cambridge.Computer Laboratory.
Cambridge : University of Cambridge, Computer Laboratory,
[1999]
Abstract: "Previous systems for dot counting in fluorescence in-situ hybridization (FISH) images have relied on an automatic focusing method for obtaining a clearly defined image.  Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals can be left unfocused or unseen.  This leads to dot counting errors, which increase with the number of probes.  The approach described here dispenses with automatic focusing, and instead relies on a larger statistical sample of the specimen at a fixed focal plane.  Images across the specimen can be obtained in significantly less time if a fixed focal plane is used.  A trainable classifier based on a neural network (NN) is used to discriminate between valid and artifact signals represented by a set of features.  This improves upon previous classification schemes that are based on non-adaptable decision boundaries and are trained using only examples of valid signals.  Trained by examples of valid and artifact signals, three NN classifiers, two of them hierarchical, each achieve between 83% and 87% classification accuracy on unseen data.  When data is pre-discriminated in this way, errors in dot counting can be significantly reduced."
"May 1999."
Abstract: "Previous systems for dot counting in fluorescence in-situ hybridization (FISH) images have relied on an automatic focusing method for obtaining a clearly defined image.  Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals can be left unfocused or unseen.  This leads to dot counting errors, which increase with the number of probes.  The approach described here dispenses with automatic focusing, and instead relies on a larger statistical sample of the specimen at a fixed focal plane.  Images across the specimen can be obtained in significantly less time if a fixed focal plane is used.  A trainable classifier based on a neural network (NN) is used to discriminate between valid and artifact signals represented by a set of features.  This improves upon previous classification schemes that are based on non-adaptable decision boundaries and are trained using only examples of valid signals.  Trained by examples of valid and artifact signals, three NN classifiers, two of them hierarchical, each achieve between 83% and 87% classification accuracy on unseen data.  When data is pre-discriminated in this way, errors in dot counting can be significantly reduced."
Includes bibliographical references.
Supported in part by the EPSRC.
Fluorescence in situ hybridization.
Neural networks (Computer science)
Signal processing