Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method.

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Mapping global urban boundaries from the global artificial impervious area (GAIA) data

The ability of dual-energy computed-tomographic (CT) systems to determine the concentration of constituent materials in a mixture, known as material decomposition, is the basis for many of dual-energy CT's clinical applications. However, the complex composition of tissues and organs in the human body poses a challenge for many material decomposition methods, which assume the presence of only two, or at most three, materials in the mixture. We developed a flexible, model-based method that extends dual-energy CT's core material decomposition capability to handle more complex situations, in which it is necessary to disambiguate among and quantify the concentration of a larger number of materials. The proposed method, named multi-material decomposition (MMD), was used to develop two image analysis algorithms. The first was virtual unenhancement (VUE), which digitally removes the effect of contrast agents from contrast-enhanced dual-energy CT exams. VUE has the ability to reduce patient dose and improve clinical workflow, and can be used in a number of clinical applications such as CT urography and CT angiography. The second algorithm developed was liver-fat quantification (LFQ), which accurately quantifies the fat concentration in the liver from dual-energy CT exams. LFQ can form the basis of a clinical application targeting the diagnosis and treatment of fatty liver disease. Using image data collected from a cohort consisting of 50 patients and from phantoms, the application of MMD to VUE and LFQ yielded quantitatively accurate results when compared against gold standards. Furthermore, consistent results were obtained across all phases of imaging (contrast-free and contrast-enhanced). This is of particular importance since most clinical protocols for abdominal imaging with CT call for multi-phase imaging. We conclude that MMD can successfully form the basis of a number of dual-energy CT image analysis algorithms, and has the potential to improve the clinical utility of dual-energy CT in disease management.

A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images

The development of reliable, sustainable, and economical sources of alternative fuels to petroleum is required to tackle the global energy crisis. One such alternative is microalgal biofuel, which is expected to play a key role in reducing the detrimental effects of global warming as microalgae absorb atmospheric CO2 via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid amounts and fail to characterize a diverse population of microalgal cells with single-cell resolution in a non-invasive and interference-free manner. Here high-throughput label-free single-cell screening of lipid-producing microalgal cells with optofluidic time-stretch quantitative phase microscopy was demonstrated. In particular, Euglena gracilis, an attractive microalgal species that produces wax esters (suitable for biodiesel and aviation fuel after refinement), within lipid droplets was investigated. The optofluidic time-stretch quantitative phase microscope is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch quantitative phase microscope, and a digital image processor equipped with machine learning. As a result, it provides both the opacity and phase maps of every single cell at a high throughput of 10,000 cells/s, enabling accurate cell classification without the need for fluorescent staining. Specifically, the dataset was used to characterize heterogeneous populations of E. gracilis cells under two different culture conditions (nitrogen-sufficient and nitrogen-deficient) and achieve the cell classification with an error rate of only 2.15%. The method holds promise as an effective analytical tool for microalgae-based biofuel production. © 2017 International Society for Advancement of Cytometry.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cyto.a.23084

High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy.

For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced.

Image correction using a deep generative machine-learning model

Numerous algorithms have been proposed in the literature to speed up dilation/erosion operations. The motivation has been to reduce computational complexity by exploiting the structuring element and the image object properties. This paper presents a new algorithm for binary morphological dilation and erosion called the Kernel Sub-Division algorithm and discusses its implementation in the two dimensional case. It decomposes the n-dimensional structuring element, into several subsets and operates on the object contours in the image. The image characteristics are exploited by subdividing the object contours into bins while performing contour processing. The elegance of the algorithm lies in its retaining the correspondence to the output of the classical implementation with massive speed gain. The results of the algorithm on a statistically significant test set of images, showed that it performed five times better than the classical implementation for a 3x3 kernel. It also demonstrated a marginal rise in execution time with increasing size of the kernel.

Fast binary dilation/erosion algorithm using kernel subdivision

Methods and systems and computer program products for automatically segmenting the volume of interest from intensity images are provided. The method for segmenting a volume of interest in an intensity image receives the intensity image and the scanner acquisition parameters used to acquire the intensity image. The method then scales the contrast of the intensity image based, at least in part, on the scanner acquisition parameters. The method segments the intensity image based, at least in part, on image data of the intensity image and the scanner acquisition parameters, to obtain the volume of interest.

Method and system for automated volume of interest segmentation

Provided is a tomography apparatus including: a data acquisitor configured to obtain a first image by using tomography data acquired as a first X-ray generator for generating X-rays having a first energy rotates around an object over a first angular range and obtain a second image by using tomography data acquired as a second X-ray generator for generating X-rays having a second energy rotates around the object over a second angular range; a controller configured to acquire motion information representing an amount of motion of the object over time by using the first and second images; and an image reconstructor configured to reconstruct a target image showing the object at a target time point by using the motion information.

Tomography apparatus and method of reconstructing tomography images

The disclosed embodiments relate to determining an amount of a contrast agent in an image. For example, a computer-implemented method of image processing includes generating, from a first polychromatic contrast-enhanced X-ray image obtained at a first energy and a second polychromatic contrast-enhanced X-ray image obtained at a second energy, a simulated first monochromatic contrast-enhanced X-ray image and a simulated second monochromatic contrast-enhanced X-ray image. The simulated first monochromatic contrast-enhanced X-ray image includes first regions of enhanced contrast and the simulated second monochromatic contrast-enhanced includes second regions of enhanced contrast. The method also includes isolating the first and second regions of enhanced contrast from other regions of the image, and determining an amount of the contrast agent within the first and second regions of enhanced contrast based at least on a derived partial signal attributable to the contrast agent.

System and method for contrast agent estimation in x-ray imaging

The disclosed embodiments relate to characterizing or quantifying an element or composition of interest within an imaged volume. In accordance with one embodiment, high and low energy images are acquired of a volume of interest using a polychromatic emission source. The high and low energy images are processed to generate monochromatic images. Based on the observed attenuation within the monochromatic images, one or more elements or compositions of interest are characterized within the imaged volume.

Ajay Narayanan

Papers

Fast binary dilation/erosion algorithm using kernel subdivision

Method and system for automated volume of interest segmentation

Tomography apparatus and method of reconstructing tomography images

System and method for contrast agent estimation in x-ray imaging

System and method for detecting materials or disease states using multi-energy computed tomography