Abstract

Objects of interest are rendered from spectral images. Seven types of blood and cancer cells are imaged in a microscope with changes in source illumination and sensor gain over one year calibrated. Chromatic distortion is measured and corrections analyzed. Background is discriminated with binary decisions generated from a training sample pair. A filter is derived from two sample-dependent binary decision parameters: a linear discriminant and a minimum error bias. Excluded middle decisions eliminate order-dependent errors. A global bias maximizes the number and size of spectral objects. Sample size and dimensional limits on accuracy are described using a covariance stability relation.

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