A new lung nodule simulation model was designed to create and insert synthetic solid lung nodules, with shapes
and density similar to real nodules, into normal MDCT chest exams. The nodule simulation model was validated
both subjectively by human experts and quantitatively by comparing density attenuation profiles of simulated
nodules with real nodules. These validation studies demonstrated a high level of similarity between the synthetic
nodules and real nodules. This nodule simulation model was used to create objective test databases for use in
evaluating lung nodule growth measurement of a CAD system. The performance evaluation studies demonstrated
a high level of accuracy for the automatic growth measurement tool, while the error margin for the growth
measurement increased with nodule size decreasing. The experiments also showed the volume/growth estimation
errors for low dose scans were comparable to the ones for the normal dose scans, thus demonstrated a robust
performance across different dosages.
A new lung nodule simulation model was designed to create and insert synthetic solid lung nodules, with shapes
and density similar to real nodules, into normal MDCT chest exams. Nodule shapes were modeled using linearly
deformed superquadrics with added randomly generated high dimensional deformations. Nodule density statistics
and attenuation profiles were extracted from a group of real nodule samples, by dissecting each real nodule
digitally layer by layer from the border to the core. A nodule created with modeled shape and density was
inserted into real CT images by creating volume average layers using weighted averaging between nodule density
and background density for each voxel. The nodule simulation model was validated both subjectively by human
experts and quantitatively by comparing density attenuation profiles of simulated nodules with real nodules.
These validation studies demonstrated a high level of similarity between the synthetic nodules and real nodules.
This nodule simulation model was used to create objective test databases for use in evaluating a CAD system. The
evaluation study showed that the CAD system was accurate in detection and volume measurement for isolated
nodules, and also performed relatively well for juxta-vascular nodules. The CAD system also demonstrated
stable performances for different dosages.
the problem of quanitizing sub-images of a multiresolution image decomposition while preserving edges is considered. For this purpose, we propose a coding algorithm which exploits both spatial and frequency location of wavelet coefficients within and across scales. This algorithm is dedicated to low bit rate image coding. In this paper, we develop a new constrained quantizer based on a lagrangian formulation called edge adaptive quantizer. Given a significance map, this algorithm preserves significant coefficients while smoothing elsewhere. This is done by introducing a spatial adaptation term based on Markov random field. A new criterion based on spatial models and entropy constraint is then derived. With this new formation, a practical solution to the multiresolution optimization problem is presented in the form of a bit allocation procedure. An optimal quantizer is constructed minimizing this new criterion for a target bit rate. Experiments using constraint quantization demonstrate PSNR gains over standard uniform scalar quantization and appreciable visual improvements. A simple extension of the algorithm allows for the use of other scalar quantizers.
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