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Biomedical subjects

M A Schulze

Publications and source records attributed to M A Schulze.

2 recordsLinked to original sources

Classification accuracy in multiple color fluorescence imaging microscopy.

BACKGROUND: The discriminatory power and imaging efficiency of different multicolor FISH (M-FISH) analysis systems are key factors in obtaining accurate and reproducible classification results. In a recent paper, Garini et al. put forth an analytical technique to quantify the discriminatory power ("S/N ratio") and imaging efficiency ('excitation efficiency') of multicolor fluorescent karyotyping systems. METHODS: A parametric model of multicolor fluorescence microscopy, based on the Beer-Lambert law, is analyzed and reduced to a simple expression for S/N ratio. Parameters for individual system configurations are then plugged into the model for comparison purposes. RESULTS: We found that several invalid assumptions, which are used to reduce the complex mathematics of the Beer-Lambert law to a simple S/N ratio, result in some completely misleading conclusions about classification accuracy. The authors omit the most significant noise source, and consider only one highly abstract and unrepresentative situation. Unwisely chosen parameters used in the examples lead to predictions that are not consistent with actual results. CONCLUSIONS: The earlier paper presents an inaccurate view of the M-FISH situation. In this short communication, we point out several inaccurate assumptions in the mathematical development of Garini et al. and the poor choices of parameters in their examples. We show results obtained with different imaging systems that indicate that reliable and comparable results are obtained if the metaphase samples are well-hybridized. We also conclude that so-called biochemical noise, not photon noise, is the primary factor that limits pixel classification accuracy, given reasonable exposure times.

Chromosomes↗

Morphological image processing techniques in thermographic imaging.

Mathematical morphology is a set algebra that defines some important new techniques in image processing. Morphological filters are closely related to order statistic and other nonlinear filters, but they are uniquely sensitive to shape. A morphological filter will preserve shapes similar to its structuring element shape while modifying dissimilar shapes. Most morphological filters are effective at removing both linear and nonlinear noise processes. However, the standard morphological operators introduce a statistical and deterministic bias to images. Fortunately, these operators exist in complementary pairs that are equally and oppositely biased. One way to alleviate the bias is to average the two complementary operators. The filters formed by such averages are the midrange filter (basic operators), the pseudomedian filter (singly compound operators) and the LOCO filter (doubly compound operators). In thermographic imaging, one often wishes to find exact temperatures or accurate isothermal contours. Therefore, techniques used to remove sensor noise and scanning artifact should not introduce bias. The LOCO filter that we have devised provides the shape control and noise suppression of morphological techniques without biasing the image. We will demonstrate the effects of different structuring element shapes on thermographic images of tissue heated by laser irradiation and electrosurgery.

Animals↗