Thomas Blaffert

Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform

Contrast enhancement of radiographies based on a multiscale decomposition of the images recently has proven to be a far more versatile and efficient method than regular unsharp-masking techniques, while containing these as a subset. In this paper, we compare the performance of two multiscale-methods, namely the Laplacian Pyramid and the fast wavelet transform (FWT). We find that enhancement based on the FWT suffers from one serious drawback-the introduction of visible artifacts when large structures are enhanced strongly. By contrast, the Laplacian Pyramid allows a smooth enhancement of large structures, such that visible artifacts can be avoided. Only for the enhancement of very small details, for denoising applications or compression of images, the FWT may have some advantages over the Laplacian Pyramid.

Computer-assisted multicomponent spectral analysis with fuzzy data sets

Abstract A new approach to the interpretation of spectra with “fuzzy sets” is described. A computer program CIF (Compound Identification with Fuzzy sets) is applied. This program is capable of finding components in a mixture by comparing the sample spectrum with reference spectra in a library. The applications discussed involve the interpretation of infrared spectra. The problems of spectral library search are discussed, an elementary introduction to fuzzy set theory is given, and applications to spectral library search are demonstrated.

Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions.

A pulmonary ventilation imaging technique based on four-dimensional (4D) computed tomography (CT) has advantages over existing techniques. However, physiologically accurate 4D-CT ventilation imaging has not been achieved in patients. The purpose of this study was to evaluate 4D-CT ventilation imaging by correlating ventilation with emphysema. Emphysematous lung regions are less ventilated and can be used as surrogates for low ventilation. We tested the hypothesis: 4D-CT ventilation in emphysematous lung regions is significantly lower than in non-emphysematous regions. Four-dimensional CT ventilation images were created for 12 patients with emphysematous lung regions as observed on CT, using a total of four combinations of two deformable image registration (DIR) algorithms: surface-based (DIR(sur)) and volumetric (DIR(vol)), and two metrics: Hounsfield unit (HU) change (V(HU)) and Jacobian determinant of deformation (V(Jac)), yielding four ventilation image sets per patient. Emphysematous lung regions were detected by density masking. We tested our hypothesis using the one-tailed t-test. Visually, different DIR algorithms and metrics yielded spatially variant 4D-CT ventilation images. The mean ventilation values in emphysematous lung regions were consistently lower than in non-emphysematous regions for all the combinations of DIR algorithms and metrics. V(HU) resulted in statistically significant differences for both DIR(sur) (0.14 ± 0.14 versus 0.29 ± 0.16, p = 0.01) and DIR(vol) (0.13 ± 0.13 versus 0.27 ± 0.15, p < 0.01). However, V(Jac) resulted in non-significant differences for both DIR(sur) (0.15 ± 0.07 versus 0.17 ± 0.08, p = 0.20) and DIR(vol) (0.17 ± 0.08 versus 0.19 ± 0.09, p = 0.30). This study demonstrated the strong correlation between the HU-based 4D-CT ventilation and emphysema, which indicates the potential for HU-based 4D-CT ventilation imaging to achieve high physiologic accuracy. A further study is needed to confirm these results.

Gray-Value Based Registration of CT and MR Images by Maximization of Local Correlation

For gray-value based multi-modality registration the similarity measure is essential. Excellent results have been obtained with mutual information for various modality combinations. In this contribution we consider local correlation as similarity measure for multi-modality registration. Using a software phantom it is analyzed why local correlation is suitable for this registration task whereas direct gray-value correlation itself is usually not. It is shown that registration with local correlation can be done using only a fraction of the image volume offering an opportunity to accelerate the algorithm. Within validation, registration of the phantom images, two simultaneously acquired dual contrast MR images, and a clinical CT-MR data set has been studied. For comparison, the data sets have also been registered with mutual information. The results show that not only mutual information, but also local correlation is suitable for gray-value based multi-modality registration.

Effect of model inaccuracy on selectivity optimization procedures in reversed-phase liquid chromatography

Abstract Interpretive methods for the optimization of the selectivity in chromatography require the description of the retention surfaces of all individual solutes in a mixture by some kind of model. The requirements and availability of such models arediscussed for the particular case of optimizing ternary and quaternary mobile phase compositions in reversed-phase liquid chromatography. It is concluded that in order to allow an adequate prediction of the optimal conditions, a model equation should describe the experimental data to within 1% or less (in terms of the capacity factor k). Several suggested models from the literature were tried, but it appears that none of the currently available models provides a description of the data with the required accuracy. It is demonstrated that this situation does not improve when more experimental data become available. The alternative of using piecewise (linear) interpolation is discussed, and it is demonstrated that this approach may provide a sufficiently accurate description of the data on the basis of a limited number of carefully selected experiments.

Expertise — an expert system for infrared spectra evaluation

Abstract A system for the automated elucidation of chemical structures by the interpretation of infrared spectra is described. “Rules” for the finding of substructure features in an unknown spectrum are obtained by a pattern recognition procedure. A structure generation algorithm links particular substructures, called “superatoms”, together. Fuzzy set operators are used to consider inaccuracies in peak positions and intensities.

Surface based cardiac and respiratory motion extraction for pulmonary structures from multi-phase CT

During medical imaging and therapeutic interventions, pulmonary structures are in general subject to cardiac and respiratory motion. This motion leads potentially to artefacts and blurring in the resulting image material and to uncertainties during interventions. This paper presents a new automatic approach for surface based motion tracking of pulmonary structures and reports on the results for cardiac and respiratory induced motion. The method applies an active shape approach to ad-hoc generated surface representations of the pulmonary structures for phase to phase surface tracking. Input of the method are multi-phase CT data, either cardiac or respiratory gated. The iso-surface representing the transition between air or lung parenchyma to soft tissue, is triangulated for a selected phase p0. An active shape procedure is initialised in the image of phase p1 using the generated surface in p0. The used internal energy term penalizes shape deformation as compared to p0. The process is iterated for all phases pi to pi+1 of the complete cycle. Since the mesh topology is the same for all phases, the vertices of the triangular mesh can be treated as pseudo-landmarks defining tissue trajectories. A dense motion field is interpolated. The motion field was especially designed to estimate the error margins for radiotherapy. In the case of respiratory motion extraction, a validation on ten biphasic thorax CT images (2.5mm slice distance) was performed with expert landmarks placed at vessel bifurcations. The mean error on landmark position was below 2.6mm. We further applied the method to ECG gated images and estimated the influence of the heart beat on lung tissue displacement.

Comparison of different follow-up lung registration methods with and without segmentation

In modern multi slice CT scanners the increasing amount of data also increases the demand on image processing methods that assist the diagnosis. For the detection and classification of lung nodules in a follow up study it is very helpful to have the slices of a previous scan aligned with the slices of the current scan. This is a typical problem of image registration, for which different types of solutions exist. We investigated the accuracy and computation times of a rigid body, an affine, and a spline based elastic registration approach on the complete data set, and compared the results to a method where the registration was preceded by a segmentation of the lung volume. The registration quality was determined on a ground truth of previously determined lung nodule locations by measuring the average distance of corresponding nodules. It was found that an affine registration is slightly better than a rigid body registration, and that both are much faster than the elastic registration, which in turn showed the best registration quality. A good compromise was the affine registration on a previously segmented lung volume, which in total is not much slower than the registration without segmentation, but shows better alignment and higher robustness.

Lung lobe modeling and segmentation with individualized surface meshes

An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

Computer-aided segmentation of pulmonary nodules: automated vasculature cutoff in thin- and thick-slice CT

Abstract Consistent volume measurement of pulmonary nodules is essential for follow-up monitoring for diagnosis and therapy purposes. Automated segmentation algorithms have to cope in a robust manner with nodules which are attached to surrounding vessels, the lung walls, or the diaphragm. In this paper we introduce such a nodule segmentation algorithm, and study the effect of slice spacing (thickness) and Hounsfield threshold on the estimated volume. With thin-slice data from multi-array CT scanners, the delicate vasculature structure surrounding most pulmonary nodules becomes visible. Even though the connectivity of the nodules to the surrounding vasculature varies with slice thickness and Hounsfield threshold, we find that the vasculature cutoff decisions of the proposed segmentation algorithm yield consistent measurement characteristics which are robust enough to compare nodule volumes between follow-up CT studies even of different slice thickness.