Topic
Parametric Image
About: Parametric Image is a research topic. Over the lifetime, 311 publications have been published within this topic receiving 6095 citations.
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01 Jan 2004
TL;DR: In this paper, the kernel maximum likelihood estimator (KML) is proposed for image segmentation, and a provably convergent, iterative algorithm for the resultant optimization problem is presented.
Abstract: Models of spatial variation in images are central to a large number of low-level computer vision problems including segmentation, registration, and 3D structure detection. Often, images are represented using parametric models to characterize (noise-free) image variation, and, additive noise. However, the noise model may be unknown and parametric models may only be valid on individual segments of the image. Consequently, we model noise using a nonparametric kernel density estimation framework and use a locally or globally linear parametric model to represent the noise-free image pattern. This results in a novel, robust, redescending, M- parameter estimator for the above image model which we call the Kernel Maximum Likelihood estimator (KML). We also provide a provably convergent, iterative algorithm for the resultant optimization problem. The estimation framework is empirically validated on synthetic data and applied to the task of range image segmentation.
18 citations
TL;DR: This work studies a direct parametric maximum likelihood expectation maximization algorithm applied to [(18)F]DOPA data using reference-tissue input function and shows quantitative robustness and clinical reproducibility within six human acquisitions in the region of clinical interest.
18 citations
TL;DR: The findings indicate that the texture-feature parametric imaging method can be not only useful for determining the location of the lesion boundary but also as a tool to improve the accuracy of breast tumor classifications.
18 citations
TL;DR: The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric image with very similar noise properties to those of MC images, in a fraction of the time.
Abstract: Purpose:
To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo
(MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images.
Methods:
The tool was developed in matlab using both new and previously reported modules of petstep
(PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS).
Results:
dpetstep was 8000 times faster than MC. Dynamic images from dpetstep had a root mean square error that was within 4% on average of that of MC
images, whereas the GAUSS images were within 11%. The average bias in dpetstep and MC
images was the same, while GAUSS differed by 3% points. Noise profiles in dpetstep
images conformed well to MC
images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dpetstep
images and noise properties agreed better with MC.
Conclusions:
The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC
images, in a fraction of the time. They believe dpetstep to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dpetstep can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.
17 citations
TL;DR: This work introduces and explores the concept of octrees for registration which drastically reduces the amount of processed data and thus the computational costs and presents a suitable optimization technique.
Abstract: Even for reasonably sized three-dimensional (3D) images, image registration becomes a computationally intensive task. Here, we introduce and explore the concept of octrees for registration which drastically reduces the amount of processed data and thus the computational costs. We show how to map the registration problem onto an octree and present a suitable optimization technique. Furthermore, we demonstrate the performance of the new approach by academic (two-dimensional) as well as real life (3D) examples. These examples indicate that the computational time can be reduced by a factor of 3-4 compared with standard approaches.
17 citations