Showing papers on "Parametric Image published in 2015"

Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images.

Abstract: Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.

Comparative assessment of parametric neuroreceptor mapping approaches based on the simplified reference tissue model using [¹¹C]ABP688 PET.

Abstract: In recent years, several linearized model approaches for fast and reliable parametric neuroreceptor mapping based on dynamic nuclear imaging have been developed from the simplified reference tissue model (SRTM) equation. All the methods share the basic SRTM assumptions, but use different schemes to alleviate the effect of noise in dynamic-image voxels. Thus, this study aimed to compare those approaches in terms of their performance in parametric image generation. We used the basis function method and MRTM2 (multilinear reference tissue model with two parameters), which require a division process to obtain the distribution volume ratio (DVR). In addition, a linear model with the DVR as a model parameter (multilinear SRTM) was used in two forms: one based on linear least squares and the other based on extension of total least squares (TLS). Assessment using simulated and actual dynamic [(11)C]ABP688 positron emission tomography data revealed their equivalence with the SRTM, except for different noise susceptibilities. In the DVR image production, the two multilinear SRTM approaches achieved better image quality and regional compatibility with the SRTM than the others, with slightly better performance in the TLS-based method.

Parametric Image Segmentation of Humans with Structural Shape Priors

Abstract: The figure-ground segmentation of humans in images captured in natural environments is an outstanding open problem due to the presence of complex backgrounds, articulation, varying body proportions, partial views and viewpoint changes. In this work we propose class-specific segmentation models that leverage parametric max-flow image segmentation and a large dataset of human shapes. Our contributions are as follows: (1) formulation of a sub-modular energy model that combines class-specific structural constraints and data-driven shape priors, within a parametric max-flow optimization methodology that systematically computes all breakpoints of the model in polynomial time; (2) design of a data-driven class-specific fusion methodology, based on matching against a large training set of exemplar human shapes (100,000 in our experiments), that allows the shape prior to be constructed on-the-fly, for arbitrary viewpoints and partial views. (3) demonstration of state of the art results, in two challenging datasets, H3D and MPII (where figure-ground segmentation annotations have been added by us), where we substantially improve on the first ranked hypothesis estimates of mid-level segmentation methods, by 20%, with hypothesis set sizes that are up to one order of magnitude smaller.

Quality estimation method of parametric image based on nonlinear structural similarity deviation

Abstract: The present invention discloses a quality estimation method of a parametric image based on nonlinear structural similarity deviation. Firstly the RGB color image spaces of a reference image and a degraded image are converted into a Gauss image space and a grayscale image space, then a local edge intensity spectrum and a local gradient spectrum are generated and are subjected to nonlinear normalization, the corresponding local edge similarity map and the corresponding local gradient similarity map are calculated, and finally through analyzing the structural characteristic of the local similarity map, a value with a small similarity deviation is adaptively selected to be the quality estimation value of the degraded image. According to the method, the quality estimation effect of different fuzzy, JPEG, noise and other natural images is good, the calculation is convenient and efficient, and the realizability is good.

Fixed-Point Methods in Parametric Model Checking

Abstract: We present a general framework for the synthesis of the constraints under which the selected properties hold in a class of models with discrete transitions, together with Boolean encoding - based method of implementing the theory. We introduce notions of parametric image and preimage, and show how to use them to build fixed-point algorithms for parametric model checking of reachability and deadlock freedom. An outline of how the ideas shown in this paper were specialized for an extension of Computation Tree Logic is given together with some experimental results.

Direct Image Alignment for Active Near Infrared Image Differencing

Abstract: One of the difficult challenges in face recognition is dealing with the illumination variations that occur in varying environments. A practical and efficient way to address harsh illumination variations is to use active image differencing in near-infrared frequency range. In this method, two types of image frames are taken: an illuminated frame is taken with near infrared illumination, and an ambient frame is taken without the illumination. The difference between face regions of these frames reveals the face image illuminated only by the illumination. Therefore the image is not affected by the ambient illumination and illumination robust face recognition can be achieved. But the method assumes that there is no motion between two frames. Faces in different locations on the two frames introduces a motion artifact. To compensate for motion between two frames, a motion interpolation method has been proposed; but it has limitations, including an assumption that the face motion is linear. In this paper, we propose a new image alignment method that directly aligns the actively illuminated and ambient frames. The method is based on Lucas-Kanade parametric image alignment method and involves a new definition of errors based on the properties of the two types of frames. Experimental results show that the direct method outperforms the motion interpolation method in terms of face recognition rates.

Anatomy-assisted direct parametric PET imaging for myocardial blood flow abnormality detection

Abstract: Dynamic myocardial perfusion (MP) PET imaging followed by tracer kinetic modeling provides quantitative measurement of myocardial blood flow (MBF). The purpose of this study is to incorporate anatomical information in the 4D direct parametric image reconstruction and to evaluate the performance in detecting regional MBF abnormality. The one-tissue compartment model was formulated in the maximum likelihood (ML) problem to relate the dynamic projection datasets directly to the kinetic parameters. A maximum a posteriori (MAP) algorithm that incorporates the joint entropy (JE) between the anatomic and parametric images in the reconstruction was developed. The preconditioned steepest ascent (PSA) algorithm was used to solve the ML and the JE-MAP estimation problems. Using the XCAT phantom and the patient-based organ time activity curves, we simulated two sets of dynamic MP Rb-82 PET data, one carrying normal MBF and the other with reduced MBF on a region of interest, each with 20 noise realizations. Corresponding MR images were simulated with the 3D T1-weighted sequence as specified in a clinical PET/MRI protocol. The reconstructed parametric images from the ML and the JE-MAP algorithms were compared using the tradeoff between noise and bias and the signal to noise ratio (SNR), which reflects the separability between the normal and abnormal K1 parameters. The proposed JE-MAP algorithm resulted in improved noise versus bias tradeoff compared to the ML algorithm and also demonstrated better performance in the regional abnormal MBF detection task.

Theoretical analysis of lesion detectability in penalized maximum-likelihood patlak parametric image reconstruction using dynamic PET

Abstract: Detecting cancerous lesion is a major clinical application in emission tomography. In previous work, we have studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by reconstructing a sequence of dynamic PET images first and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in the Patlak slope image. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize the lesion detectability. The proposed method is validated using computer-based Monte Carlo simulation. Good agreements between theoretical predictions and Monte Carlo results are observed. The theoretical formula also shows the benefit of the direct method in dynamic PET reconstruction for lesion detection.

Dynamic Parameter Visualization and Noise Suppression Techniques for Contrast-Enhanced Ultrasonography

Abstract: This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.