White-light interferometry is an absolute 3D-measurement technique, used for the inspection of structured silicon and
other materials with high quality surfaces. In this technique, each pixel of the camera detects a separate interference
signal, which correlates with the height of the corresponding object point. Different signal processing algorithms are
used, which extract the height from the interference signal by using the coherence or the phase information of the signal.
However, measurement errors can occur if there are chromatic aberrations in the interferometer system. Then the phase
information correlates with the height information in an unexpected manner and there are often disturbing 2&pgr; phase
jumps in the numerical evaluation process, although the topography of the object is continuous and a light source with a
short coherence length is used. We examined a Mirau type white-light interferometer with chromatic aberrations and
explain how mirrorlike, tilted objects cause a correlation of the phase and the height information in each interference
signal. We also show that this measurement error depends on both the slope of the object point and its field position. A
comparison of measurements and a simulation, which shows the described correlation effect, is given.
In earlier publications, it was shown that scanning of surfaces by dark beams can be exploited for sub-wavelength feature analysis. In this work, we present vector simulations based in Rigorous Coupled-Wave Analysis with the purpose to estimate the expected resolution of the method, both lateral (feature size) and axial (height). The dark beam used in this study has a line singularity generated by a π-phase step positioned in a Gaussian beam. Various combinations of the illumination and detection nuFmerical apertures (from NA=0.2 to NA=0.8) and different surface features were studied. Polarization effects which become significant at high numerical apetures, were considered as an additional source of information for the analysis. In the case of a sub-wavelength feature on an ideal surface, the resolution of the method is limited only by the electronics noise. In particular, under a reasonable assumption of a 105 signal to noise ratio, it is possible to detect a 0.2 nm step.
In ultrasonic tissue characterization the small reflections originating from the scattering structures inside the tissue are analyzed. To obtain diagnostic performance for tissue characterization by means of analysis of echocardiographic images we use methods of mathematical texture analysis. We investigate whether myocardial changes effect the texture of ultrasonic images and if this could be described using quantitative texture analysis. The texture analysis was computed in a single window of an ultrasound image/sequence covering the inner myocardial septum. Parameters from gray level histogram, co-occurrence matrices, run length statistics and run difference, from power spectrum and fractal dimensions were investigated to provide satisfying and generalizable results for classification of the myocardium. A set of parameters that could discriminate between normal and pathological myocardium were extracted. The results of 142 biopsies were compared with those of texture analysis in echocardiograms of 106 patients suspected having myocarditis. Using the reduced set of parameters the best sensitivity was 89.0% and the specificity was 83.6%. Myocarditis is associated with echocardiographic texture alteration. Texture analysis with methods of digital image processing can reliably identify myocarditis. A suitable solution for a computer-assisted non- invasive support for the diagnosis and detection of myocarditis was found.
Intravascular images from patients undergoing coronary angioplasty were obtained by a 20 MHz catheter probe. Texture analysis was performed computing features of different regions of interest, representing soft and calcified plaque and thrombus. For each class about 100 feature sets were disposed, computed in regions selected in 30 images. Texture features were classified using Bayesian classifier and a neural back propagation network. The statistical classifier led to a good discrimination between soft and calcified plaque whereas half of the thrombus feature sets were recognized as soft plaque. The accuracy of the classification result when using the neural network classifier was 87% for calcified plaque, 88% for soft plaque, and 76% for thrombus. The neural classification process was implemented as a visualization routine for PC supported classification. For this purpose the 51 texture parameters were calculated and sent to the recall routine which delivered the neural network classification result. The classification result were color encoded with red, blue and green labels for calcified plaque, soft plaque and thrombus.
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