TY - GEN

T1 - Adaptive hotelling discriminant functions

AU - Brème, Arthur

AU - Kupinski, Matthew A.

AU - Clarkson, Eric

AU - Barrett, Harrison H.

PY - 2007/10/15

Y1 - 2007/10/15

N2 - Any observer performing a detection task on an image produces a single number that represents the observer's confidence that a signal (e.g., a tumor) is present. A linear observer produces this test statistic using a linear template or a linear discriminant. The optimal linear discriminant is well-known to be the Hotelling observer and uses both first- and second-order statistics of the image data. There are many situations where it is advantageous to consider discriminant functions that adapt themselves to some characteristics of the data. In these situations, the linear template is itself a function of the data and, thus, the observer is nonlinear. In this paper, we present an example adaptive Hotelling discriminant and compare the performance of this observer to that of the Hotelling observer and the Bayesian ideal observer. The task is to detect a signal that is imbedded in one of a finite number of possible random backgrounds. Each random background is Gaussian but has different covariance properties. The observer uses the image data to determine which background type is present and then uses the template appropriate for that background. We show that the performance of this particular observer falls between that of Hotelling and ideal observers.

AB - Any observer performing a detection task on an image produces a single number that represents the observer's confidence that a signal (e.g., a tumor) is present. A linear observer produces this test statistic using a linear template or a linear discriminant. The optimal linear discriminant is well-known to be the Hotelling observer and uses both first- and second-order statistics of the image data. There are many situations where it is advantageous to consider discriminant functions that adapt themselves to some characteristics of the data. In these situations, the linear template is itself a function of the data and, thus, the observer is nonlinear. In this paper, we present an example adaptive Hotelling discriminant and compare the performance of this observer to that of the Hotelling observer and the Bayesian ideal observer. The task is to detect a signal that is imbedded in one of a finite number of possible random backgrounds. Each random background is Gaussian but has different covariance properties. The observer uses the image data to determine which background type is present and then uses the template appropriate for that background. We show that the performance of this particular observer falls between that of Hotelling and ideal observers.

UR - http://www.scopus.com/inward/record.url?scp=35148874236&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35148874236&partnerID=8YFLogxK

U2 - 10.1117/12.707804

DO - 10.1117/12.707804

M3 - Conference contribution

AN - SCOPUS:35148874236

SN - 0819466336

SN - 9780819466334

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2007

T2 - Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment

Y2 - 21 February 2007 through 22 February 2007

ER -