Gerd Haeusler

Optical coherence tomography by spectral radar: dynamic range estimation and in-vivo measurements of skin

Spectral radar is an optical sensor for tomography, working in the Fourier domain, rather than in the time domain. The scattering amplitude a (z) along one vertical axis from the surface into the bulk can be measured within one exposure. No reference arm scanning is necessary. One important property of optical coherence tomography (OCT) sensors is the dynamic range. We will compare the dynamic range of spectral radar with standard OCT. The influence of the Fourier transformation on the dynamic range of spectral radar will be discussed. The clinical relevance of the in vivo measurements will be demonstrated.

Determination of facial symmetry in unilateral cleft lip and palate patients from three-dimensional data: technical report and assessment of measurement errors.

Objective To assess measurement errors of a novel technique for the three-dimensional determination of the degree of facial symmetry in patients suffering from unilateral cleft lip and palate malformations. Design Technical report, reliability study. Setting Cleft Lip and Palate Center of the University of Erlangen-Nuremberg, Erlangen, Germany. Patients The three-dimensional facial surface data of five 10-year-old unilateral cleft lip and palate patients were subjected to the analysis. Distances, angles, surface areas, and volumes were assessed twice. Main Outcome Measures Calculations were made for method error, intraclass correlation coefficient, and repeatability of the measurements of distances, angles, surface areas, and volumes. Results The method errors were less than 1 mm for distances and less than 1.5° for angles. The intraclass correlation coefficients showed values greater than .90 for all parameters. The repeatability values were comparable for cleft and noncleft sides. Conclusion The small method errors, high intraclass correlation coefficients, and comparable repeatability values for cleft and noncleft sides reveal that the new technique is appropriate for clinical use.

Physical limits of 3D sensing

A fundamental limit for the distance uncertainty of coherent 3D-sensors is presented. The minimum distance uncertainty is given by (delta) z equals 1/2(pi) (DOT) (lambda) /sin2u, with the aperture of observation sinu and wavelength (lambda) . This distance uncertainty can be derived via speckle statistics for different sensing principles, and surprisingly the same result can be obtained directly from Heisenbergs uncertainty, principle for a single photon. Because speckles are the main reason for distance uncertainty, possibilities to overcome the speckle problem are discussed. This leads to an uncertainty principle between lateral resolution and longitudinal distance uncertainty. A way to improve the distance uncertainty without sacrificing lateral resolution is the use of temporally incoherent light.

Modifications of the coherence radar for in-vivo profilometry in dermatology

Important aims in dermatology are the measurement of pathological alterations of human skin and on the other hand the quantification of the influences caused by pharmaceutic and cosmetic products. We present modifications of the well- established coherence radar that allow in vivo measurement of human skin, in spite of involuntary body movements and bloodflow. The measuring field can be varied from 100 X 100 micrometers 2 to 5 X 5 mm2. The measuring time is 5 to 15 s and the longitudinal measuring uncertainty is about 2 micrometers . A fiber optical implementation allows the separation of the sensor head from the mechanical scan. The mobile and compact sensor head can now be freely positioned and adjusted to each part of the patients skin. Disturbances caused by unavoidable movement of the patient can be compensated by modified setups of the coherence radar. We show measurements of clinical and cosmetic relevance.

SIM and Deflectometry: New Tools to Acquire Beautiful, SEM-like 3D Images

Structured-illumination microscopy and microdeflectometry acquire the shape of microscopic objects with a noise level down to 1 nanometer, a depth of field 100 times larger than the Rayleigh depth, and slope angles up to 80°.

Neural nets and Hough strategies: competitors in image processing

Object segmentation, recognition and localization are challenging because of the large amount of input data and because of the invariances required. We discuss strategies to overcome these problems, considering sensors, algorithms and architectures. Specifically, we address neural nets and Hough strategies. The ability of implicit learning makes neural nets interesting for industrial inspection: compared to classical methods they promise robustness against variations of the input data. Furthermore, no expert is necessary for supervision. The inherent parallelity simplifies the design of algorithms. However, the advantages are counterbalanced by a serious drawback: the high computational complexity -- if images are considered. The ability of optics, to help by its inherent parallelity is limited, because neural architectures are usually space variant and cannot simply be implemented optically. We discuss approaching these problems by feature extraction, by sparse algorithms and by space invariant architectures. A competitive strategy for object recognition and localization is based on probability tables, such as the Hough transform uses: a couple of weak but independent hypotheses can give a safe decision about the kind and the locus of an object. This method requires a learning phase prior to the working phase, as the neural strategy does. In that sense it is similar, however, the computational complexity can be much smaller. This makes it possible to segment, localize and recognize objects invariant against shift, rotation and scale.