Sentence examples for methods for contrast from inspiring English sources

The machine learning methods for contrast are decision tree, naive Bayesian classifier, K-neighborhood, SVM (polynomial kernel), and SVM (Gaussian kernel).

This review describes the progress that has been made in the development and testing of methods for contrast ultrasound molecular imaging of cardiovascular disease.

Currently available methods for contrast agent-based magnetic resonance imaging (MRI) and computed tomography (CT) of articular cartilage can only detect cartilage degradation after biochemical changes have occurred within the tissue volume.

We have presented and evaluated different <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0007497.e047.PNG" class= inline-graphic"/> -projection methods for contrast enhancement in bright field image stacks, and shown that the projection approach can replace whole cell fluorescent staining for our set of macrophage images.

The hydration of high-risk patients is part of the module supporting the prevention of CIN and is based on national guidelines with preventing methods for contrast medium administration [ 17, 18].

The Global Contrast Enhancement Histogram Modification Algorithm [12] was represented as the effective method for contrast enhancement by adjusting linear operations of the input histogram and utilizing the black and white (BW) stretching to obtain the visually pleasing, artifact-free, and natural looking images.

This paper describes a new method for contrast enhancement in images and image sequences of low-light or unevenly illuminated scenes based on statistical modelling of wavelet coefficients of the image.

Using the previously introduced theoretical notions, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a reaction-diffusion partial differential system [92] [99].

Then, based on these biological considerations about the real vision mechanisms, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a non-isotropic diffusion reaction partial differential system.

Here we introduce a method for contrast-enhanced imaging of unstained transparent objects that is capable of high-throughput imaging.

Satisfactory images were obtained by adjusting the scanning time of three-phase contrast-enhanced CT and optimizing administration methods for the contrast agent.